MKTG90011
Jun 1, 2026
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我拿到了一份我在上的课的考前宝典！你帮我拆解一下这节课，告诉我复习重点，我快final了
越详细越好，公式，定义，核心考点我都需要，救救孩子
- 当然可以。我会只基于你这两份材料（bible + cheatsheet）把 MKTG90011 的 Final 复习重点拆成一张“可直接照做的复习地图”：考什么 → 怎么拿分 → 必背定义/规则 → 必会题型模板 → 高频陷阱清单 → 冲刺安排。
- （小提醒）你这门课的“胜负手”非常明确：90% 分数都围绕同一个技能——“哪个问题用哪个检验（which test for which question）”。把这棵决策树练到反射，你基本就稳了。[1]Source: asksia-bible-mktg90011-bilingual.pdf! Read this first: the assessment shape, and what you may bring in 先读这一段:评估的形态,以及你可以带进考场的东西 MKTG90011 is assessed by three pieces: a 50% final exam, a 40% group project (the real-client "Loneliness Economy" research), and 10% quizzes (5 x 2%). The exam is an on-campus digital LMS quiz under a lockdown browser - 2 hours + 15 min reading, MCQ and long-answer, answer all. A Week 9 slide indicates a restricted set of permitted materials - the prescribed text, paper-based notes, and a non-programmable calculator - so it is worth building bring-in notes from this book. This is not an open-book exam: confirm the exact permitted materials on your own exam cover sheet before you rely on any of it. MKTG90011 由三部分评估:一场占 50% 的期末考、一个占40% 的小组项目(真实客户的“孤独经济”研究),以及占 10% 的测验(5×2%)。考试是一场在校园内、用防作弊锁定浏览器进行的数字化 LMS 测验 -- 2 小时 +15 分钟阅 读,含选择题与简答题,全部作答。第9周的一张幻灯片表明允许携带的材料是受限的 -- 规定教材、纸质笔记,以及 一台非可编程计算器 -- 所以值得用本书做一份带入笔记。这不是开卷考:在依赖以上任何说法之前,请在你自己的考 试封面页上确认确切的允许材料。 i How this book was built 这本书是怎么搭建出来的 Method and statistics canon here is grounded in this subject's own materials - the Canvas pages, the W1-W11 seminar slides and SPSS exercises, the project rubric, and Sarstedt & Mooi, A Concise Guide to Market Research (3rd ed. ). Software is IBM SPSS Statistics 29 + Qualtrics. Standard methods and tests are stated plainly; the Loneliness-Economy brief and any proprietary datasets are paraphrased and all worked numbers are AskSia- invented. Verify weights, dates and permitted materials on your LMS - details shift between cohorts. 这里的方法与统计经典内容植根于本课自己的材料 -- Canvas 页面、第 1-11周研讨课幻灯片与 SPSS 练习、项目评分 细则,以及 Sarstedt & Mooi,《A Concise Guide to Market Research》(第3版)。软件为 IBM SPSS Statistics 29+ Qualtrics。标准方法与检验如实直述;孤独经济简报及任何专有数据集均经转述,所有演示数字皆由 AskSia 虚 构。请在你的LMS 上核实分值、日期与允许材料 -- 各届之间细节会有变动。 AskSia Library · MKTG90011 · 双语 Bilingual THE BLUEPRINT - THE EXAM BLUEPRINT 50% EXAM . 40% PROJECT . 10% QUIZ Where every mark lives 每一分都在这里 One 50% exam, one 40% project, ten percent of quizzes - all driven by the same skill 一场占50%的考试、一个占40%的项目、占10%的若干测验 -- 全部由同一项技能驱动 TL;DR. Half your mark is a single final exam whose make-or-break skill is "which test for which question"; the project mandates the very same tests across H1-H6. Master one decision tree and you hold the keys to 90% of the unit. - 一句话总结。你一半的分数来自一场期末考试,而它成败攸关的技能就是“哪个问题用哪个检验”;项目作业则强制在 H1-H6 上用到同样这批检验。掌握好这一棵决策树,你就握住了全课 90%的钥匙。 - 50% FINAL EXAM 期末考 40% GROUP PROJECT 小组项目 10% QUIZZES (5×2) 小测验 (5×2) TESTS TO KNOW COLD 必须烂熟于心的检验 The three assessment pieces 三项评估构成 Component Weight When Format[3]Source: asksia-bible-mktg90011-bilingual.pdf✓ The strategy this dictates 由此推导出的策略 One skill dominates: which test for which question. Drill the tree opposite until it is reflex - categorical × categorical - chi-square; one metric outcome over 2 groups - t-test (independent if separate people, paired if same/before-after); 3+ groups - one-way ANOVA; two metric variables - correlation (Pearson if linear&normal, Spearman if ordinal/non-normal); predict a metric outcome - regression; reduce many items - PCA. The project mandates this same map - H1 chi-square . H2 & H3 two different t-tests . H4 one-way ANOVA . H5 multiple regression . H6 any - so the work you do for one piece directly serves the other. 有一项技能压倒一切:哪个问题用哪个检验。把对页 那棵树练到成为本能 -- 类别×类别→ 卡方检验; 一个度量型结果跨 2组→t检验(不同的人则独立, 同一批/前后则配对);3+组→单因素方差分析;两 个度量型变量 →相关(线性且正态用皮尔逊,定序/ 非正态用斯皮尔曼);预测一个度量型结果 →回归; 缩减众多测量项→ PCA。项目强制同一张地图 -- H1 卡方 · H2 与 H3 两种不同的 t检验 · H4 单因素方差 分析 · H5 多元回归 · H6 任意 -- 所以你为其中一部 分所做的功直接服务于另一部分。 ★ The single highest-value hour 单位时间价值最高的那一小时 If you study one thing, study the which-test tree and the output-reading rule for each test (the statistic, the Sig. /p, the decision, the one-line APA sentence). Then build your permitted bring-in notes around it - after confirming the exact materials allowed on your exam cover sheet. 若你只学一样东西,就学选检验决策树和每个检验的 读输出规则(统计量、Sig. /p、决策、一句 APA 句)。然后围绕它搭建你的允许带入笔记 -- 在确认了 你的考试封面页上准许的确切材料之后。 AskSia Library · MKTG90011 · 双语 Bilingual CONTENTS CONTENTS The whole subject, in one ordered book 整门课,浓缩为一本有序之书 Twelve seminar weeks - one process-and-which-test map 十二周研讨课 →一张流程兼“选检验”地图 TL;DR. The book follows the unit's arc - build the method foundations (W1-6), then the statistical toolkit organised around test selection (W7-11), then turn it into marks with the project playbook, glossary and practice bank. 一句话总结。本书顺着本课的脉络展开 -- 先打牢方法基础(第1-6周),再围绕检验选择组织起统计工具箱(第 7-11 周),最后通过项目实战手册、术语表与练习题库把它转化为分数。 Ch Topic Core idea Methods foundations . Weeks 1-6 1 The research process 7 steps · symptom - problem . MDP vs MRQ → 2 Secondary data & hypotheses primary vs secondary . DV/IV . conceptual model → 3 Qualitative research interviews . focus groups . thematic analysis → 4 Measurement & scaling the 4 scale types . reliability & validity . a → 5 Research designs exploratory · descriptive . causal / experiments →
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0）先把考试形态钉死（避免复习跑偏）
- 评分构成（你复习优先级要围着它转）
- Final exam：50%
- Group project：40%（Loneliness Economy 真客户研究）
- Quizzes：10%（5×2%） [1]Source: asksia-bible-mktg90011-bilingual.pdf! Read this first: the assessment shape, and what you may bring in 先读这一段:评估的形态,以及你可以带进考场的东西 MKTG90011 is assessed by three pieces: a 50% final exam, a 40% group project (the real-client "Loneliness Economy" research), and 10% quizzes (5 x 2%). The exam is an on-campus digital LMS quiz under a lockdown browser - 2 hours + 15 min reading, MCQ and long-answer, answer all. A Week 9 slide indicates a restricted set of permitted materials - the prescribed text, paper-based notes, and a non-programmable calculator - so it is worth building bring-in notes from this book. This is not an open-book exam: confirm the exact permitted materials on your own exam cover sheet before you rely on any of it. MKTG90011 由三部分评估:一场占 50% 的期末考、一个占40% 的小组项目(真实客户的“孤独经济”研究),以及占 10% 的测验(5×2%)。考试是一场在校园内、用防作弊锁定浏览器进行的数字化 LMS 测验 -- 2 小时 +15 分钟阅 读,含选择题与简答题,全部作答。第9周的一张幻灯片表明允许携带的材料是受限的 -- 规定教材、纸质笔记,以及 一台非可编程计算器 -- 所以值得用本书做一份带入笔记。这不是开卷考:在依赖以上任何说法之前,请在你自己的考 试封面页上确认确切的允许材料。 i How this book was built 这本书是怎么搭建出来的 Method and statistics canon here is grounded in this subject's own materials - the Canvas pages, the W1-W11 seminar slides and SPSS exercises, the project rubric, and Sarstedt & Mooi, A Concise Guide to Market Research (3rd ed. ). Software is IBM SPSS Statistics 29 + Qualtrics. Standard methods and tests are stated plainly; the Loneliness-Economy brief and any proprietary datasets are paraphrased and all worked numbers are AskSia- invented. Verify weights, dates and permitted materials on your LMS - details shift between cohorts. 这里的方法与统计经典内容植根于本课自己的材料 -- Canvas 页面、第 1-11周研讨课幻灯片与 SPSS 练习、项目评分 细则,以及 Sarstedt & Mooi,《A Concise Guide to Market Research》(第3版)。软件为 IBM SPSS Statistics 29+ Qualtrics。标准方法与检验如实直述;孤独经济简报及任何专有数据集均经转述,所有演示数字皆由 AskSia 虚 构。请在你的LMS 上核实分值、日期与允许材料 -- 各届之间细节会有变动。 AskSia Library · MKTG90011 · 双语 Bilingual THE BLUEPRINT - THE EXAM BLUEPRINT 50% EXAM . 40% PROJECT . 10% QUIZ Where every mark lives 每一分都在这里 One 50% exam, one 40% project, ten percent of quizzes - all driven by the same skill 一场占50%的考试、一个占40%的项目、占10%的若干测验 -- 全部由同一项技能驱动 TL;DR. Half your mark is a single final exam whose make-or-break skill is "which test for which question"; the project mandates the very same tests across H1-H6. Master one decision tree and you hold the keys to 90% of the unit. - 一句话总结。你一半的分数来自一场期末考试,而它成败攸关的技能就是“哪个问题用哪个检验”;项目作业则强制在 H1-H6 上用到同样这批检验。掌握好这一棵决策树,你就握住了全课 90%的钥匙。 - 50% FINAL EXAM 期末考 40% GROUP PROJECT 小组项目 10% QUIZZES (5×2) 小测验 (5×2) TESTS TO KNOW COLD 必须烂熟于心的检验 The three assessment pieces 三项评估构成 Component Weight When Format[2]Source: asksia-bible-mktg90011-bilingual.pdfFinal exam 50% Exam period On-campus digital LMS quiz, lockdown; 2h + 15m; MCQ + long-answer Group project 40% W12 - Loneliness Economy Team of 4-5; report + 6-page deck; W6 questionnaire 1% Quizzes - 10% Across W2- W10 MCQ / true-false / short-answer Examinable scope (per the subject guide): both the seminar notes and exercises and the textbook chapters in the outline - Ch 1-7 and Ch 10. Nothing outside that. The approved non- programmable calculator is the tell that some items ask you to read SPSS output and reach a numeric decision, not only recall a definition. 可考范围(依课程指南):研讨课笔记与练习以及大纲中列 出的教材章节––第1-7 章和第10章。范围之外的内容一 概不考。获准使用的非可编程计算器正是一个信号:部分题 目要你读懂 SPSS 输出并得出一个数值结论,而不仅是回忆 某个定义。 FIG 0. 1 What is your research goal? COMPARE groups (metric DV) ASSOCIATE two variables PREDICT a metrie DV REDUCE many items How many groups? paired or Independent? Variable type? categorical vs metric How many predictors (Ms) 1 va bertchmark two categorical Chi-square (independence) contingency rabie one metric IV one-sample t-test Simple (bivariate) regression summarise items Principal Component Analysis (PCA)[18]Source: asksia-cheatsheet-mktg90011.pdfMKTG90011 Marketing Research . The Which-Test Map UNIV. OF MELBOURNE . FBE . GRADUATE CHEAT SHEET Sem 1 2026 . SIDE 1 OF 2 Exam 50% · Project 40% · Quiz 10% SIDE 1/2 EXAM BLUEPRINT & THE WHICH-TEST MAP . Research process . Problem (MDP/RQ/H) . * Which-test table . Compare/Associate/Predict/Reduce . 4 scale types . Reliability & validity . Sampling EXAM 50% . WHICH-TEST = THE SKILL Compiled by AskSia . mapped to the MKTG90011 syllabus . asksia. ai/cheatsheet/unimelb-mktg90011 0 . How to Use This READ FIRST * Grading: Final exam 50% . Group project 40% . Quizzes 10% (5×2%). The exam is on-campus, individual, digital (LMS quiz under a lockdown browser), ~2h + 15min reading, MCQ + long-answer (answer all). Examinable = seminar notes + exercises + textbook Ch 1-7, 10 (Sarstedt & Mooi, 3rd ed. ). Software: IBM SPSS 29 + Qualtrics. Permitted materials: a W9 slide lists prescribed text + paper notes + non-programmable calculator. Build concise bring-in notes & confirm the permitted- materials line on your own exam cover sheet - don't assume open-book. -- SIA > The make-or-break skill is "which test for which question". Master Side 1's selection table; Side 2 drills each test's SPSS path, output & APA sentence. 1 . The Research Process W1 . 7 STEPS The spine of the whole unit. The first step is the most important. 1. Identify & formulate the problem 2. Determine the research design (explor. /descr. /causal) 3. Design the sample & data-collection method 4. Collect the data 5. Analyse the data (< the which-test skill lives here) 6. Interpret, discuss & present findings 7. Follow-up Marketing research = the function linking consumer to marketer through information - to identify/define problems & opportunities, evaluate actions, monitor performance. Symptom > problem (iceberg): the visible symptom (falling sales) sits above the real decision problem. The first step - define the problem right - drives everything after it.
- 期末考试形式（你要按这个练）
- 校园内 digital LMS quiz、lockdown browser
- 2 小时 + 15 分钟 reading
- MCQ + long-answer（全部作答） [1]Source: asksia-bible-mktg90011-bilingual.pdf! Read this first: the assessment shape, and what you may bring in 先读这一段:评估的形态,以及你可以带进考场的东西 MKTG90011 is assessed by three pieces: a 50% final exam, a 40% group project (the real-client "Loneliness Economy" research), and 10% quizzes (5 x 2%). The exam is an on-campus digital LMS quiz under a lockdown browser - 2 hours + 15 min reading, MCQ and long-answer, answer all. A Week 9 slide indicates a restricted set of permitted materials - the prescribed text, paper-based notes, and a non-programmable calculator - so it is worth building bring-in notes from this book. This is not an open-book exam: confirm the exact permitted materials on your own exam cover sheet before you rely on any of it. MKTG90011 由三部分评估:一场占 50% 的期末考、一个占40% 的小组项目(真实客户的“孤独经济”研究),以及占 10% 的测验(5×2%)。考试是一场在校园内、用防作弊锁定浏览器进行的数字化 LMS 测验 -- 2 小时 +15 分钟阅 读,含选择题与简答题,全部作答。第9周的一张幻灯片表明允许携带的材料是受限的 -- 规定教材、纸质笔记,以及 一台非可编程计算器 -- 所以值得用本书做一份带入笔记。这不是开卷考:在依赖以上任何说法之前,请在你自己的考 试封面页上确认确切的允许材料。 i How this book was built 这本书是怎么搭建出来的 Method and statistics canon here is grounded in this subject's own materials - the Canvas pages, the W1-W11 seminar slides and SPSS exercises, the project rubric, and Sarstedt & Mooi, A Concise Guide to Market Research (3rd ed. ). Software is IBM SPSS Statistics 29 + Qualtrics. Standard methods and tests are stated plainly; the Loneliness-Economy brief and any proprietary datasets are paraphrased and all worked numbers are AskSia- invented. Verify weights, dates and permitted materials on your LMS - details shift between cohorts. 这里的方法与统计经典内容植根于本课自己的材料 -- Canvas 页面、第 1-11周研讨课幻灯片与 SPSS 练习、项目评分 细则,以及 Sarstedt & Mooi,《A Concise Guide to Market Research》(第3版)。软件为 IBM SPSS Statistics 29+ Qualtrics。标准方法与检验如实直述;孤独经济简报及任何专有数据集均经转述,所有演示数字皆由 AskSia 虚 构。请在你的LMS 上核实分值、日期与允许材料 -- 各届之间细节会有变动。 AskSia Library · MKTG90011 · 双语 Bilingual THE BLUEPRINT - THE EXAM BLUEPRINT 50% EXAM . 40% PROJECT . 10% QUIZ Where every mark lives 每一分都在这里 One 50% exam, one 40% project, ten percent of quizzes - all driven by the same skill 一场占50%的考试、一个占40%的项目、占10%的若干测验 -- 全部由同一项技能驱动 TL;DR. Half your mark is a single final exam whose make-or-break skill is "which test for which question"; the project mandates the very same tests across H1-H6. Master one decision tree and you hold the keys to 90% of the unit. - 一句话总结。你一半的分数来自一场期末考试,而它成败攸关的技能就是“哪个问题用哪个检验”;项目作业则强制在 H1-H6 上用到同样这批检验。掌握好这一棵决策树,你就握住了全课 90%的钥匙。 - 50% FINAL EXAM 期末考 40% GROUP PROJECT 小组项目 10% QUIZZES (5×2) 小测验 (5×2) TESTS TO KNOW COLD 必须烂熟于心的检验 The three assessment pieces 三项评估构成 Component Weight When Format[2]Source: asksia-bible-mktg90011-bilingual.pdfFinal exam 50% Exam period On-campus digital LMS quiz, lockdown; 2h + 15m; MCQ + long-answer Group project 40% W12 - Loneliness Economy Team of 4-5; report + 6-page deck; W6 questionnaire 1% Quizzes - 10% Across W2- W10 MCQ / true-false / short-answer Examinable scope (per the subject guide): both the seminar notes and exercises and the textbook chapters in the outline - Ch 1-7 and Ch 10. Nothing outside that. The approved non- programmable calculator is the tell that some items ask you to read SPSS output and reach a numeric decision, not only recall a definition. 可考范围(依课程指南):研讨课笔记与练习以及大纲中列 出的教材章节––第1-7 章和第10章。范围之外的内容一 概不考。获准使用的非可编程计算器正是一个信号:部分题 目要你读懂 SPSS 输出并得出一个数值结论,而不仅是回忆 某个定义。 FIG 0. 1 What is your research goal? COMPARE groups (metric DV) ASSOCIATE two variables PREDICT a metrie DV REDUCE many items How many groups? paired or Independent? Variable type? categorical vs metric How many predictors (Ms) 1 va bertchmark two categorical Chi-square (independence) contingency rabie one metric IV one-sample t-test Simple (bivariate) regression summarise items Principal Component Analysis (PCA)[5]Source: asksia-bible-mktg90011-bilingual.pdf从一份干净的数据集到正确的检验 -- 考试的核心技能 TL;DR. Before any test you must (1) clean the data, (2) describe it with the right statistic for the scale type, then (3) pick the test by DV/IV scale x number of groups x paired-or-independent. That last step - which test for which question - is the make-or-break skill on the 50% final and the spine of the H1-H6 project. 一句话总结。任何检验之前你都必须(1)清洗数据,(2)用对应量表类型的正确统计量来描述它,再(3)按 DV/IV 量表 × 组数 × 配对还是独立 挑选检验。最后那一步 -- 哪个问题用哪个检验 -- 正是占50% 的期末考成败攸关的技能,也是 H1- H6 项目的脊梁。 ★ What the exam asks here 考试在这里问什么 The final is an on-campus digital LMS quiz (2h + 15min reading, lockdown browser), MCQ + long-answer, answer all. The single most repeated skill is Type C - choose the right test: you are handed a questionnaire + a hypothesis and must name the test, justify it by scale & group structure, then read the SPSS output and state the decision. Examinable = seminar notes + exercises + textbook Ch 1-7, 10. Build bring-in notes; confirm the exact permitted materials on your exam cover sheet. 期末考是一场校园内的数字化 LMS 测验(2小时+15分钟阅读,锁定浏览器),选择题+简答题,全部作答。被反复 考查最多的一项技能是 C类 -- 选对检验:你拿到一份问卷+一个假设,须命名检验、用量表与分组结构为其辩护,再 读 SPSS 输出并陈述决策。可考范围=研讨课笔记+练习+教材第1-7章、第10章。做一份带入笔记;在你的考试 封面页上确认确切的允许材料。 7. 1 Data preparation - clean before you compute 7. 1 数据准备 -- 先清洗,再计算 Analysis cannot rescue bad data. Preparation has two goals: data must be correct (no errors, no inconsistencies, missing values handled) and ready (coded into numeric form per question type). The standard pipeline: 分析无法挽救糟糕的数据。数据准备有两个目标:数据须正确(无错误、无矛盾、缺失值已处理)且就绪(按题型编码为数值 形式)。标准流程如下: 1 Editing - scan for impossible / inconsistent answers (an "age = 220", a "male" who answered the pregnancy filter). 编辑(Editing) - 扫查不可能/自相矛盾的回答(“年龄 =220”、一个回答了怀孕筛选题的“男性”)。 2 Coding - assign numbers to responses (e. g. Female=O, Male=1; Likert "Strongly agree"=5) so SPSS can read them. 编码(Coding) -- 给回答赋数字(例如 女=0、男=1;李克特“非常同意”=5),好让 SPSS 能读取。 AskSia Library · MKTG90011 · 双语 Bilingual 3 Cleaning & outliers - flag extreme values with z-scores: [z] > 3 is a likely outlier. Investigate before deleting; if it is a data-entry error, delete it but keep a backup and record the fix in the SPSS Syntax. 清洗与离群值 -- 用 z 分数标记极端值:|z|>3 是可能的离群值。删除前先调查;若确属录入错误,就删除它,但要保留备 份并在 SPSS Syntax 中记录这次修正。 4 Missing data - decide a treatment (exclude case-wise / pair-wise, or impute). Missing data is a non- sampling error. 缺失数据 -- 决定一种处理方式(逐案/逐对剔除,或插补)。缺失数据是一种非抽样误差。 OUTLIER RULE standardised value (z-score) : z = ( x - x ) / s > flag |z| > 3 as a candidate outlier SPSS · Analyze - Descriptive Statistics - Descriptives - tick "Save standardized values as variables" ! Delete an outlier without a reason 无正当理由就删除一个离群值 A value being large is not a reason to drop it - a genuine big spender is real data. Only delete when it is an error (impossible value, data-entry slip). Always keep the original file and log the edit in Syntax so the change is reproducible. 一个值大并不构成删除它的理由 -- 一个真实的大额消费者就是真实数据。只有当它是错误(不可能的值、录入失误) 时才删除。永远保留原始文件,并把改动记录在 Syntax 中,以使更改可复现。 AskSia Library . MKTG90011 . XXia Bilingual WEEK 7 . ANALYSIS & WHICH -TEST WEEK 7 . ANALYSIS & WHICH - TEST[18]Source: asksia-cheatsheet-mktg90011.pdfMKTG90011 Marketing Research . The Which-Test Map UNIV. OF MELBOURNE . FBE . GRADUATE CHEAT SHEET Sem 1 2026 . SIDE 1 OF 2 Exam 50% · Project 40% · Quiz 10% SIDE 1/2 EXAM BLUEPRINT & THE WHICH-TEST MAP . Research process . Problem (MDP/RQ/H) . * Which-test table . Compare/Associate/Predict/Reduce . 4 scale types . Reliability & validity . Sampling EXAM 50% . WHICH-TEST = THE SKILL Compiled by AskSia . mapped to the MKTG90011 syllabus . asksia. ai/cheatsheet/unimelb-mktg90011 0 . How to Use This READ FIRST * Grading: Final exam 50% . Group project 40% . Quizzes 10% (5×2%). The exam is on-campus, individual, digital (LMS quiz under a lockdown browser), ~2h + 15min reading, MCQ + long-answer (answer all). Examinable = seminar notes + exercises + textbook Ch 1-7, 10 (Sarstedt & Mooi, 3rd ed. ). Software: IBM SPSS 29 + Qualtrics. Permitted materials: a W9 slide lists prescribed text + paper notes + non-programmable calculator. Build concise bring-in notes & confirm the permitted- materials line on your own exam cover sheet - don't assume open-book. -- SIA > The make-or-break skill is "which test for which question". Master Side 1's selection table; Side 2 drills each test's SPSS path, output & APA sentence. 1 . The Research Process W1 . 7 STEPS The spine of the whole unit. The first step is the most important. 1. Identify & formulate the problem 2. Determine the research design (explor. /descr. /causal) 3. Design the sample & data-collection method 4. Collect the data 5. Analyse the data (< the which-test skill lives here) 6. Interpret, discuss & present findings 7. Follow-up Marketing research = the function linking consumer to marketer through information - to identify/define problems & opportunities, evaluate actions, monitor performance. Symptom > problem (iceberg): the visible symptom (falling sales) sits above the real decision problem. The first step - define the problem right - drives everything after it.
- 可考范围（别学超纲）
- seminar notes + exercises + 教材章节 Ch 1–7 和 Ch 10，范围外不考 [2]Source: asksia-bible-mktg90011-bilingual.pdfFinal exam 50% Exam period On-campus digital LMS quiz, lockdown; 2h + 15m; MCQ + long-answer Group project 40% W12 - Loneliness Economy Team of 4-5; report + 6-page deck; W6 questionnaire 1% Quizzes - 10% Across W2- W10 MCQ / true-false / short-answer Examinable scope (per the subject guide): both the seminar notes and exercises and the textbook chapters in the outline - Ch 1-7 and Ch 10. Nothing outside that. The approved non- programmable calculator is the tell that some items ask you to read SPSS output and reach a numeric decision, not only recall a definition. 可考范围(依课程指南):研讨课笔记与练习以及大纲中列 出的教材章节––第1-7 章和第10章。范围之外的内容一 概不考。获准使用的非可编程计算器正是一个信号:部分题 目要你读懂 SPSS 输出并得出一个数值结论,而不仅是回忆 某个定义。 FIG 0. 1 What is your research goal? COMPARE groups (metric DV) ASSOCIATE two variables PREDICT a metrie DV REDUCE many items How many groups? paired or Independent? Variable type? categorical vs metric How many predictors (Ms) 1 va bertchmark two categorical Chi-square (independence) contingency rabie one metric IV one-sample t-test Simple (bivariate) regression summarise items Principal Component Analysis (PCA)[5]Source: asksia-bible-mktg90011-bilingual.pdf从一份干净的数据集到正确的检验 -- 考试的核心技能 TL;DR. Before any test you must (1) clean the data, (2) describe it with the right statistic for the scale type, then (3) pick the test by DV/IV scale x number of groups x paired-or-independent. That last step - which test for which question - is the make-or-break skill on the 50% final and the spine of the H1-H6 project. 一句话总结。任何检验之前你都必须(1)清洗数据,(2)用对应量表类型的正确统计量来描述它,再(3)按 DV/IV 量表 × 组数 × 配对还是独立 挑选检验。最后那一步 -- 哪个问题用哪个检验 -- 正是占50% 的期末考成败攸关的技能,也是 H1- H6 项目的脊梁。 ★ What the exam asks here 考试在这里问什么 The final is an on-campus digital LMS quiz (2h + 15min reading, lockdown browser), MCQ + long-answer, answer all. The single most repeated skill is Type C - choose the right test: you are handed a questionnaire + a hypothesis and must name the test, justify it by scale & group structure, then read the SPSS output and state the decision. Examinable = seminar notes + exercises + textbook Ch 1-7, 10. Build bring-in notes; confirm the exact permitted materials on your exam cover sheet. 期末考是一场校园内的数字化 LMS 测验(2小时+15分钟阅读,锁定浏览器),选择题+简答题,全部作答。被反复 考查最多的一项技能是 C类 -- 选对检验:你拿到一份问卷+一个假设,须命名检验、用量表与分组结构为其辩护,再 读 SPSS 输出并陈述决策。可考范围=研讨课笔记+练习+教材第1-7章、第10章。做一份带入笔记;在你的考试 封面页上确认确切的允许材料。 7. 1 Data preparation - clean before you compute 7. 1 数据准备 -- 先清洗,再计算 Analysis cannot rescue bad data. Preparation has two goals: data must be correct (no errors, no inconsistencies, missing values handled) and ready (coded into numeric form per question type). The standard pipeline: 分析无法挽救糟糕的数据。数据准备有两个目标:数据须正确(无错误、无矛盾、缺失值已处理)且就绪(按题型编码为数值 形式)。标准流程如下: 1 Editing - scan for impossible / inconsistent answers (an "age = 220", a "male" who answered the pregnancy filter). 编辑(Editing) - 扫查不可能/自相矛盾的回答(“年龄 =220”、一个回答了怀孕筛选题的“男性”)。 2 Coding - assign numbers to responses (e. g. Female=O, Male=1; Likert "Strongly agree"=5) so SPSS can read them. 编码(Coding) -- 给回答赋数字(例如 女=0、男=1;李克特“非常同意”=5),好让 SPSS 能读取。 AskSia Library · MKTG90011 · 双语 Bilingual 3 Cleaning & outliers - flag extreme values with z-scores: [z] > 3 is a likely outlier. Investigate before deleting; if it is a data-entry error, delete it but keep a backup and record the fix in the SPSS Syntax. 清洗与离群值 -- 用 z 分数标记极端值:|z|>3 是可能的离群值。删除前先调查;若确属录入错误,就删除它,但要保留备 份并在 SPSS Syntax 中记录这次修正。 4 Missing data - decide a treatment (exclude case-wise / pair-wise, or impute). Missing data is a non- sampling error. 缺失数据 -- 决定一种处理方式(逐案/逐对剔除,或插补)。缺失数据是一种非抽样误差。 OUTLIER RULE standardised value (z-score) : z = ( x - x ) / s > flag |z| > 3 as a candidate outlier SPSS · Analyze - Descriptive Statistics - Descriptives - tick "Save standardized values as variables" ! Delete an outlier without a reason 无正当理由就删除一个离群值 A value being large is not a reason to drop it - a genuine big spender is real data. Only delete when it is an error (impossible value, data-entry slip). Always keep the original file and log the edit in Syntax so the change is reproducible. 一个值大并不构成删除它的理由 -- 一个真实的大额消费者就是真实数据。只有当它是错误(不可能的值、录入失误) 时才删除。永远保留原始文件,并把改动记录在 Syntax 中,以使更改可复现。 AskSia Library . MKTG90011 . XXia Bilingual WEEK 7 . ANALYSIS & WHICH -TEST WEEK 7 . ANALYSIS & WHICH - TEST[18]Source: asksia-cheatsheet-mktg90011.pdfMKTG90011 Marketing Research . The Which-Test Map UNIV. OF MELBOURNE . FBE . GRADUATE CHEAT SHEET Sem 1 2026 . SIDE 1 OF 2 Exam 50% · Project 40% · Quiz 10% SIDE 1/2 EXAM BLUEPRINT & THE WHICH-TEST MAP . Research process . Problem (MDP/RQ/H) . * Which-test table . Compare/Associate/Predict/Reduce . 4 scale types . Reliability & validity . Sampling EXAM 50% . WHICH-TEST = THE SKILL Compiled by AskSia . mapped to the MKTG90011 syllabus . asksia. ai/cheatsheet/unimelb-mktg90011 0 . How to Use This READ FIRST * Grading: Final exam 50% . Group project 40% . Quizzes 10% (5×2%). The exam is on-campus, individual, digital (LMS quiz under a lockdown browser), ~2h + 15min reading, MCQ + long-answer (answer all). Examinable = seminar notes + exercises + textbook Ch 1-7, 10 (Sarstedt & Mooi, 3rd ed. ). Software: IBM SPSS 29 + Qualtrics. Permitted materials: a W9 slide lists prescribed text + paper notes + non-programmable calculator. Build concise bring-in notes & confirm the permitted- materials line on your own exam cover sheet - don't assume open-book. -- SIA > The make-or-break skill is "which test for which question". Master Side 1's selection table; Side 2 drills each test's SPSS path, output & APA sentence. 1 . The Research Process W1 . 7 STEPS The spine of the whole unit. The first step is the most important. 1. Identify & formulate the problem 2. Determine the research design (explor. /descr. /causal) 3. Design the sample & data-collection method 4. Collect the data 5. Analyse the data (< the which-test skill lives here) 6. Interpret, discuss & present findings 7. Follow-up Marketing research = the function linking consumer to marketer through information - to identify/define problems & opportunities, evaluate actions, monitor performance. Symptom > problem (iceberg): the visible symptom (falling sales) sits above the real decision problem. The first step - define the problem right - drives everything after it.
- 允许携带材料：不是完全开卷
- 有提示“教材 + 纸质笔记 + 不可编程计算器”可能允许，但你必须以自己考试封面页为准，不要赌。[1]Source: asksia-bible-mktg90011-bilingual.pdf! Read this first: the assessment shape, and what you may bring in 先读这一段:评估的形态,以及你可以带进考场的东西 MKTG90011 is assessed by three pieces: a 50% final exam, a 40% group project (the real-client "Loneliness Economy" research), and 10% quizzes (5 x 2%). The exam is an on-campus digital LMS quiz under a lockdown browser - 2 hours + 15 min reading, MCQ and long-answer, answer all. A Week 9 slide indicates a restricted set of permitted materials - the prescribed text, paper-based notes, and a non-programmable calculator - so it is worth building bring-in notes from this book. This is not an open-book exam: confirm the exact permitted materials on your own exam cover sheet before you rely on any of it. MKTG90011 由三部分评估:一场占 50% 的期末考、一个占40% 的小组项目(真实客户的“孤独经济”研究),以及占 10% 的测验(5×2%)。考试是一场在校园内、用防作弊锁定浏览器进行的数字化 LMS 测验 -- 2 小时 +15 分钟阅 读,含选择题与简答题,全部作答。第9周的一张幻灯片表明允许携带的材料是受限的 -- 规定教材、纸质笔记,以及 一台非可编程计算器 -- 所以值得用本书做一份带入笔记。这不是开卷考:在依赖以上任何说法之前,请在你自己的考 试封面页上确认确切的允许材料。 i How this book was built 这本书是怎么搭建出来的 Method and statistics canon here is grounded in this subject's own materials - the Canvas pages, the W1-W11 seminar slides and SPSS exercises, the project rubric, and Sarstedt & Mooi, A Concise Guide to Market Research (3rd ed. ). Software is IBM SPSS Statistics 29 + Qualtrics. Standard methods and tests are stated plainly; the Loneliness-Economy brief and any proprietary datasets are paraphrased and all worked numbers are AskSia- invented. Verify weights, dates and permitted materials on your LMS - details shift between cohorts. 这里的方法与统计经典内容植根于本课自己的材料 -- Canvas 页面、第 1-11周研讨课幻灯片与 SPSS 练习、项目评分 细则,以及 Sarstedt & Mooi,《A Concise Guide to Market Research》(第3版)。软件为 IBM SPSS Statistics 29+ Qualtrics。标准方法与检验如实直述;孤独经济简报及任何专有数据集均经转述,所有演示数字皆由 AskSia 虚 构。请在你的LMS 上核实分值、日期与允许材料 -- 各届之间细节会有变动。 AskSia Library · MKTG90011 · 双语 Bilingual THE BLUEPRINT - THE EXAM BLUEPRINT 50% EXAM . 40% PROJECT . 10% QUIZ Where every mark lives 每一分都在这里 One 50% exam, one 40% project, ten percent of quizzes - all driven by the same skill 一场占50%的考试、一个占40%的项目、占10%的若干测验 -- 全部由同一项技能驱动 TL;DR. Half your mark is a single final exam whose make-or-break skill is "which test for which question"; the project mandates the very same tests across H1-H6. Master one decision tree and you hold the keys to 90% of the unit. - 一句话总结。你一半的分数来自一场期末考试,而它成败攸关的技能就是“哪个问题用哪个检验”;项目作业则强制在 H1-H6 上用到同样这批检验。掌握好这一棵决策树,你就握住了全课 90%的钥匙。 - 50% FINAL EXAM 期末考 40% GROUP PROJECT 小组项目 10% QUIZZES (5×2) 小测验 (5×2) TESTS TO KNOW COLD 必须烂熟于心的检验 The three assessment pieces 三项评估构成 Component Weight When Format[18]Source: asksia-cheatsheet-mktg90011.pdfMKTG90011 Marketing Research . The Which-Test Map UNIV. OF MELBOURNE . FBE . GRADUATE CHEAT SHEET Sem 1 2026 . SIDE 1 OF 2 Exam 50% · Project 40% · Quiz 10% SIDE 1/2 EXAM BLUEPRINT & THE WHICH-TEST MAP . Research process . Problem (MDP/RQ/H) . * Which-test table . Compare/Associate/Predict/Reduce . 4 scale types . Reliability & validity . Sampling EXAM 50% . WHICH-TEST = THE SKILL Compiled by AskSia . mapped to the MKTG90011 syllabus . asksia. ai/cheatsheet/unimelb-mktg90011 0 . How to Use This READ FIRST * Grading: Final exam 50% . Group project 40% . Quizzes 10% (5×2%). The exam is on-campus, individual, digital (LMS quiz under a lockdown browser), ~2h + 15min reading, MCQ + long-answer (answer all). Examinable = seminar notes + exercises + textbook Ch 1-7, 10 (Sarstedt & Mooi, 3rd ed. ). Software: IBM SPSS 29 + Qualtrics. Permitted materials: a W9 slide lists prescribed text + paper notes + non-programmable calculator. Build concise bring-in notes & confirm the permitted- materials line on your own exam cover sheet - don't assume open-book. -- SIA > The make-or-break skill is "which test for which question". Master Side 1's selection table; Side 2 drills each test's SPSS path, output & APA sentence. 1 . The Research Process W1 . 7 STEPS The spine of the whole unit. The first step is the most important. 1. Identify & formulate the problem 2. Determine the research design (explor. /descr. /causal) 3. Design the sample & data-collection method 4. Collect the data 5. Analyse the data (< the which-test skill lives here) 6. Interpret, discuss & present findings 7. Follow-up Marketing research = the function linking consumer to marketer through information - to identify/define problems & opportunities, evaluate actions, monitor performance. Symptom > problem (iceberg): the visible symptom (falling sales) sits above the real decision problem. The first step - define the problem right - drives everything after it.
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1）全课“唯一主线”：三步路径（考试最爱考、项目也强制用）
- 你做任何题都要先写这条（写出来就先拿一部分分）：
- Step 1：目的（Goal）：你要做的是 比较 / 关联 / 预测 / 降维 哪一种？[2]Source: asksia-bible-mktg90011-bilingual.pdfFinal exam 50% Exam period On-campus digital LMS quiz, lockdown; 2h + 15m; MCQ + long-answer Group project 40% W12 - Loneliness Economy Team of 4-5; report + 6-page deck; W6 questionnaire 1% Quizzes - 10% Across W2- W10 MCQ / true-false / short-answer Examinable scope (per the subject guide): both the seminar notes and exercises and the textbook chapters in the outline - Ch 1-7 and Ch 10. Nothing outside that. The approved non- programmable calculator is the tell that some items ask you to read SPSS output and reach a numeric decision, not only recall a definition. 可考范围(依课程指南):研讨课笔记与练习以及大纲中列 出的教材章节––第1-7 章和第10章。范围之外的内容一 概不考。获准使用的非可编程计算器正是一个信号:部分题 目要你读懂 SPSS 输出并得出一个数值结论,而不仅是回忆 某个定义。 FIG 0. 1 What is your research goal? COMPARE groups (metric DV) ASSOCIATE two variables PREDICT a metrie DV REDUCE many items How many groups? paired or Independent? Variable type? categorical vs metric How many predictors (Ms) 1 va bertchmark two categorical Chi-square (independence) contingency rabie one metric IV one-sample t-test Simple (bivariate) regression summarise items Principal Component Analysis (PCA)[8]Source: asksia-bible-mktg90011-bilingual.pdf对一道“选哪个检验”的简答题,写出三步路径(目的→量表→结构),说出检验名称,陈述需要检查的假设(Levene / 线性/ 期望频数),并给出APA 报告句式模板。仅说出检验名称只能拿到一小部分分数。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . WHICH-TEST RECAP PRACTICE BANK (CONT. ) Which-test recap - the one-screen table 选检验回顾 --- 屏一览表 Walk in with this memorised: goal - scale - structure - test 把这一条背进考场:目标→量表→ 结构→ 检验 TL;DR. If you internalise one table for the exam, make it this one. Match the research goal + the variable scales + the group/structure to the test, its key SPSS output, and the APA format. 一句话总结。若你为考试只内化一张表,就内化这一张。把研究目标+变量量表+组/结构 匹配到检验、它的关键 SPSS 输 出,以及APA 格式。 Test When (DV / IV scale . #groups . paired?) Key output to read Report format COMPARE GROUP MEANS (metric DV) One-sample t 1 metric variable vs a constant; 1 group t, df, Two-Sided p; Mean vs test value t(df) = _ , p = _ Independent t 1 metric DV · 2 independent groups Levene - row; then t, df, p; group means t(df) = _ , p = _ Paired t 2 metric vars . same subjects / pre-post Paired Differences t, df, p; both means t(df) = _ , p =_ One-way ANOVA 1 metric DV . 3+ groups . 1 factor Levene - F, df1, df2, Sig . ; post-hoc pairs F(df1, df2) = _ , p =
- Step 2：量表（Scale）：每个变量标清 N/O/I/R（定类 Nominal / 定序 Ordinal / 定距 Interval / 定比 Ratio）。[11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重
- Step 3：结构（Structure）：几组？配对还是独立？（same people twice = paired；不同人 = independent）[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading[22]Source: asksia-cheatsheet-mktg90011.pdfW4 Predict a metric DV from 1 / 2+ IVs Bivariate / multiple regression R2/Adj R2; ANOVA Sig . ; B, Beta, Sig. Reduce many correlated items PCA (factor analysis) KMO, Bartlett; eigenvalue>1; loadings Profile 1 variable ("which is most . . . ", "how much spread") Descriptive (not a test) Mode / Median / Mean + SD by scale Scope: only these are taught. NOT examined - cluster, conjoint, MDS/perceptual maps, discriminant. 3b . The 4-Verb Guide DECODE THE VERB MATCH THE QUESTION'S VERB COMPARE means - t-test (2) / ANOVA (3+) ASSOCIATE + x2 (cat) / correlation (metric) PREDICT a DV - regression REDUCE items + PCA If the question says "differ / higher / vs" you are comparing means -> count the groups. "Related / linked / depends on" > associate. "Explain / predict / drives" -> regression. "most frequent / which is the . . . " (1 var) -> descriptive (mode), not a test. Paired vs independent hinges on dependence of observations : same people rated twice (pre/post, two Qs) = paired; two separate groups = independent. Identify this before you name the t-test type. COMPARE needs a metric DV + a grouping variable (count its groups: 2 -> t, 3+ -> ANOVA). ASSOCIATE needs two variables & no DV/IV split. PREDICT names a DV explained by IVs. REDUCE has many items, no DV at all. -- SIA > Decode the verb first, then count groups & check scale. The grader rewards the scale - #groups - paired? - test path written out, not a guessed test name. 4 . The 4 Scale Types W4 . DRIVES TEST CHOICE Scale type sets which stat & test are allowed identify it first, every time.
- 这条三步路径 = 你 Final 50% 的“命门技能”，也是项目 H1–H6 同一套逻辑。[3]Source: asksia-bible-mktg90011-bilingual.pdf✓ The strategy this dictates 由此推导出的策略 One skill dominates: which test for which question. Drill the tree opposite until it is reflex - categorical × categorical - chi-square; one metric outcome over 2 groups - t-test (independent if separate people, paired if same/before-after); 3+ groups - one-way ANOVA; two metric variables - correlation (Pearson if linear&normal, Spearman if ordinal/non-normal); predict a metric outcome - regression; reduce many items - PCA. The project mandates this same map - H1 chi-square . H2 & H3 two different t-tests . H4 one-way ANOVA . H5 multiple regression . H6 any - so the work you do for one piece directly serves the other. 有一项技能压倒一切:哪个问题用哪个检验。把对页 那棵树练到成为本能 -- 类别×类别→ 卡方检验; 一个度量型结果跨 2组→t检验(不同的人则独立, 同一批/前后则配对);3+组→单因素方差分析;两 个度量型变量 →相关(线性且正态用皮尔逊,定序/ 非正态用斯皮尔曼);预测一个度量型结果 →回归; 缩减众多测量项→ PCA。项目强制同一张地图 -- H1 卡方 · H2 与 H3 两种不同的 t检验 · H4 单因素方差 分析 · H5 多元回归 · H6 任意 -- 所以你为其中一部 分所做的功直接服务于另一部分。 ★ The single highest-value hour 单位时间价值最高的那一小时 If you study one thing, study the which-test tree and the output-reading rule for each test (the statistic, the Sig. /p, the decision, the one-line APA sentence). Then build your permitted bring-in notes around it - after confirming the exact materials allowed on your exam cover sheet. 若你只学一样东西,就学选检验决策树和每个检验的 读输出规则(统计量、Sig. /p、决策、一句 APA 句)。然后围绕它搭建你的允许带入笔记 -- 在确认了 你的考试封面页上准许的确切材料之后。 AskSia Library · MKTG90011 · 双语 Bilingual CONTENTS CONTENTS The whole subject, in one ordered book 整门课,浓缩为一本有序之书 Twelve seminar weeks - one process-and-which-test map 十二周研讨课 →一张流程兼“选检验”地图 TL;DR. The book follows the unit's arc - build the method foundations (W1-6), then the statistical toolkit organised around test selection (W7-11), then turn it into marks with the project playbook, glossary and practice bank. 一句话总结。本书顺着本课的脉络展开 -- 先打牢方法基础(第1-6周),再围绕检验选择组织起统计工具箱(第 7-11 周),最后通过项目实战手册、术语表与练习题库把它转化为分数。 Ch Topic Core idea Methods foundations . Weeks 1-6 1 The research process 7 steps · symptom - problem . MDP vs MRQ → 2 Secondary data & hypotheses primary vs secondary . DV/IV . conceptual model → 3 Qualitative research interviews . focus groups . thematic analysis → 4 Measurement & scaling the 4 scale types . reliability & validity . a → 5 Research designs exploratory · descriptive . causal / experiments →[5]Source: asksia-bible-mktg90011-bilingual.pdf从一份干净的数据集到正确的检验 -- 考试的核心技能 TL;DR. Before any test you must (1) clean the data, (2) describe it with the right statistic for the scale type, then (3) pick the test by DV/IV scale x number of groups x paired-or-independent. That last step - which test for which question - is the make-or-break skill on the 50% final and the spine of the H1-H6 project. 一句话总结。任何检验之前你都必须(1)清洗数据,(2)用对应量表类型的正确统计量来描述它,再(3)按 DV/IV 量表 × 组数 × 配对还是独立 挑选检验。最后那一步 -- 哪个问题用哪个检验 -- 正是占50% 的期末考成败攸关的技能,也是 H1- H6 项目的脊梁。 ★ What the exam asks here 考试在这里问什么 The final is an on-campus digital LMS quiz (2h + 15min reading, lockdown browser), MCQ + long-answer, answer all. The single most repeated skill is Type C - choose the right test: you are handed a questionnaire + a hypothesis and must name the test, justify it by scale & group structure, then read the SPSS output and state the decision. Examinable = seminar notes + exercises + textbook Ch 1-7, 10. Build bring-in notes; confirm the exact permitted materials on your exam cover sheet. 期末考是一场校园内的数字化 LMS 测验(2小时+15分钟阅读,锁定浏览器),选择题+简答题,全部作答。被反复 考查最多的一项技能是 C类 -- 选对检验:你拿到一份问卷+一个假设,须命名检验、用量表与分组结构为其辩护,再 读 SPSS 输出并陈述决策。可考范围=研讨课笔记+练习+教材第1-7章、第10章。做一份带入笔记;在你的考试 封面页上确认确切的允许材料。 7. 1 Data preparation - clean before you compute 7. 1 数据准备 -- 先清洗,再计算 Analysis cannot rescue bad data. Preparation has two goals: data must be correct (no errors, no inconsistencies, missing values handled) and ready (coded into numeric form per question type). The standard pipeline: 分析无法挽救糟糕的数据。数据准备有两个目标:数据须正确(无错误、无矛盾、缺失值已处理)且就绪(按题型编码为数值 形式)。标准流程如下: 1 Editing - scan for impossible / inconsistent answers (an "age = 220", a "male" who answered the pregnancy filter). 编辑(Editing) - 扫查不可能/自相矛盾的回答(“年龄 =220”、一个回答了怀孕筛选题的“男性”)。 2 Coding - assign numbers to responses (e. g. Female=O, Male=1; Likert "Strongly agree"=5) so SPSS can read them. 编码(Coding) -- 给回答赋数字(例如 女=0、男=1;李克特“非常同意”=5),好让 SPSS 能读取。 AskSia Library · MKTG90011 · 双语 Bilingual 3 Cleaning & outliers - flag extreme values with z-scores: [z] > 3 is a likely outlier. Investigate before deleting; if it is a data-entry error, delete it but keep a backup and record the fix in the SPSS Syntax. 清洗与离群值 -- 用 z 分数标记极端值:|z|>3 是可能的离群值。删除前先调查;若确属录入错误,就删除它,但要保留备 份并在 SPSS Syntax 中记录这次修正。 4 Missing data - decide a treatment (exclude case-wise / pair-wise, or impute). Missing data is a non- sampling error. 缺失数据 -- 决定一种处理方式(逐案/逐对剔除,或插补)。缺失数据是一种非抽样误差。 OUTLIER RULE standardised value (z-score) : z = ( x - x ) / s > flag |z| > 3 as a candidate outlier SPSS · Analyze - Descriptive Statistics - Descriptives - tick "Save standardized values as variables" ! Delete an outlier without a reason 无正当理由就删除一个离群值 A value being large is not a reason to drop it - a genuine big spender is real data. Only delete when it is an error (impossible value, data-entry slip). Always keep the original file and log the edit in Syntax so the change is reproducible. 一个值大并不构成删除它的理由 -- 一个真实的大额消费者就是真实数据。只有当它是错误(不可能的值、录入失误) 时才删除。永远保留原始文件,并把改动记录在 Syntax 中,以使更改可复现。 AskSia Library . MKTG90011 . XXia Bilingual WEEK 7 . ANALYSIS & WHICH -TEST WEEK 7 . ANALYSIS & WHICH - TEST[11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重
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2）“选检验”一页总表（你要背进考场的版本）
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A. 比较均值（COMPARE group means：metric DV）
- One-sample t-test
- Independent-samples t-test
- 触发：1 个 metric DV + 2 个独立组（两组是不同人）
- 关键先看：Levene（选对行）
- 再读：$t$、df、$p$、两组均值方向
- 报告句：$t(df)=_,\ p=_$ [4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[8]Source: asksia-bible-mktg90011-bilingual.pdf对一道“选哪个检验”的简答题,写出三步路径(目的→量表→结构),说出检验名称,陈述需要检查的假设(Levene / 线性/ 期望频数),并给出APA 报告句式模板。仅说出检验名称只能拿到一小部分分数。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . WHICH-TEST RECAP PRACTICE BANK (CONT. ) Which-test recap - the one-screen table 选检验回顾 --- 屏一览表 Walk in with this memorised: goal - scale - structure - test 把这一条背进考场:目标→量表→ 结构→ 检验 TL;DR. If you internalise one table for the exam, make it this one. Match the research goal + the variable scales + the group/structure to the test, its key SPSS output, and the APA format. 一句话总结。若你为考试只内化一张表,就内化这一张。把研究目标+变量量表+组/结构 匹配到检验、它的关键 SPSS 输 出,以及APA 格式。 Test When (DV / IV scale . #groups . paired?) Key output to read Report format COMPARE GROUP MEANS (metric DV) One-sample t 1 metric variable vs a constant; 1 group t, df, Two-Sided p; Mean vs test value t(df) = _ , p = _ Independent t 1 metric DV · 2 independent groups Levene - row; then t, df, p; group means t(df) = _ , p = _ Paired t 2 metric vars . same subjects / pre-post Paired Differences t, df, p; both means t(df) = _ , p =_ One-way ANOVA 1 metric DV . 3+ groups . 1 factor Levene - F, df1, df2, Sig . ; post-hoc pairs F(df1, df2) = _ , p =
- Paired-samples t-test
- 触发：同一批人两次测量/前后对比（before vs after / same people）
- 输出读：Paired Differences 的 $t$、df、$p$，两次均值
- 报告句：$t(df)=_,\ p=_$ [8]Source: asksia-bible-mktg90011-bilingual.pdf对一道“选哪个检验”的简答题,写出三步路径(目的→量表→结构),说出检验名称,陈述需要检查的假设(Levene / 线性/ 期望频数),并给出APA 报告句式模板。仅说出检验名称只能拿到一小部分分数。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . WHICH-TEST RECAP PRACTICE BANK (CONT. ) Which-test recap - the one-screen table 选检验回顾 --- 屏一览表 Walk in with this memorised: goal - scale - structure - test 把这一条背进考场:目标→量表→ 结构→ 检验 TL;DR. If you internalise one table for the exam, make it this one. Match the research goal + the variable scales + the group/structure to the test, its key SPSS output, and the APA format. 一句话总结。若你为考试只内化一张表,就内化这一张。把研究目标+变量量表+组/结构 匹配到检验、它的关键 SPSS 输 出,以及APA 格式。 Test When (DV / IV scale . #groups . paired?) Key output to read Report format COMPARE GROUP MEANS (metric DV) One-sample t 1 metric variable vs a constant; 1 group t, df, Two-Sided p; Mean vs test value t(df) = _ , p = _ Independent t 1 metric DV · 2 independent groups Levene - row; then t, df, p; group means t(df) = _ , p = _ Paired t 2 metric vars . same subjects / pre-post Paired Differences t, df, p; both means t(df) = _ , p =_ One-way ANOVA 1 metric DV . 3+ groups . 1 factor Levene - F, df1, df2, Sig . ; post-hoc pairs F(df1, df2) = _ , p =[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
- One-way ANOVA（单因素方差分析）
- 触发：1 个 metric DV 跨 3 组及以上（1 个分组因子）
- 关键先看：Levene（同样要选对行）
- 再读：$F(df1,df2)$、$p$；若显著要 post-hoc 才能说“哪几组不同”
- 报告句：$F(df1,df2)=_,\ p=_$ [8]Source: asksia-bible-mktg90011-bilingual.pdf对一道“选哪个检验”的简答题,写出三步路径(目的→量表→结构),说出检验名称,陈述需要检查的假设(Levene / 线性/ 期望频数),并给出APA 报告句式模板。仅说出检验名称只能拿到一小部分分数。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . WHICH-TEST RECAP PRACTICE BANK (CONT. ) Which-test recap - the one-screen table 选检验回顾 --- 屏一览表 Walk in with this memorised: goal - scale - structure - test 把这一条背进考场:目标→量表→ 结构→ 检验 TL;DR. If you internalise one table for the exam, make it this one. Match the research goal + the variable scales + the group/structure to the test, its key SPSS output, and the APA format. 一句话总结。若你为考试只内化一张表,就内化这一张。把研究目标+变量量表+组/结构 匹配到检验、它的关键 SPSS 输 出,以及APA 格式。 Test When (DV / IV scale . #groups . paired?) Key output to read Report format COMPARE GROUP MEANS (metric DV) One-sample t 1 metric variable vs a constant; 1 group t, df, Two-Sided p; Mean vs test value t(df) = _ , p = _ Independent t 1 metric DV · 2 independent groups Levene - row; then t, df, p; group means t(df) = _ , p = _ Paired t 2 metric vars . same subjects / pre-post Paired Differences t, df, p; both means t(df) = _ , p =_ One-way ANOVA 1 metric DV . 3+ groups . 1 factor Levene - F, df1, df2, Sig . ; post-hoc pairs F(df1, df2) = _ , p =[11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
-
B. 两变量关联（ASSOCIATE）
- Chi-square（卡方独立性检验）
- Correlation（相关：Pearson / Spearman）
- Pearson：两个“干净”的度量变量、且关系线性
- Spearman：只要含定序（或非正态/秩次）就用它
- 关键提醒：相关 ≠ 因果；因果需要实验等条件。[3]Source: asksia-bible-mktg90011-bilingual.pdf✓ The strategy this dictates 由此推导出的策略 One skill dominates: which test for which question. Drill the tree opposite until it is reflex - categorical × categorical - chi-square; one metric outcome over 2 groups - t-test (independent if separate people, paired if same/before-after); 3+ groups - one-way ANOVA; two metric variables - correlation (Pearson if linear&normal, Spearman if ordinal/non-normal); predict a metric outcome - regression; reduce many items - PCA. The project mandates this same map - H1 chi-square . H2 & H3 two different t-tests . H4 one-way ANOVA . H5 multiple regression . H6 any - so the work you do for one piece directly serves the other. 有一项技能压倒一切:哪个问题用哪个检验。把对页 那棵树练到成为本能 -- 类别×类别→ 卡方检验; 一个度量型结果跨 2组→t检验(不同的人则独立, 同一批/前后则配对);3+组→单因素方差分析;两 个度量型变量 →相关(线性且正态用皮尔逊,定序/ 非正态用斯皮尔曼);预测一个度量型结果 →回归; 缩减众多测量项→ PCA。项目强制同一张地图 -- H1 卡方 · H2 与 H3 两种不同的 t检验 · H4 单因素方差 分析 · H5 多元回归 · H6 任意 -- 所以你为其中一部 分所做的功直接服务于另一部分。 ★ The single highest-value hour 单位时间价值最高的那一小时 If you study one thing, study the which-test tree and the output-reading rule for each test (the statistic, the Sig. /p, the decision, the one-line APA sentence). Then build your permitted bring-in notes around it - after confirming the exact materials allowed on your exam cover sheet. 若你只学一样东西,就学选检验决策树和每个检验的 读输出规则(统计量、Sig. /p、决策、一句 APA 句)。然后围绕它搭建你的允许带入笔记 -- 在确认了 你的考试封面页上准许的确切材料之后。 AskSia Library · MKTG90011 · 双语 Bilingual CONTENTS CONTENTS The whole subject, in one ordered book 整门课,浓缩为一本有序之书 Twelve seminar weeks - one process-and-which-test map 十二周研讨课 →一张流程兼“选检验”地图 TL;DR. The book follows the unit's arc - build the method foundations (W1-6), then the statistical toolkit organised around test selection (W7-11), then turn it into marks with the project playbook, glossary and practice bank. 一句话总结。本书顺着本课的脉络展开 -- 先打牢方法基础(第1-6周),再围绕检验选择组织起统计工具箱(第 7-11 周),最后通过项目实战手册、术语表与练习题库把它转化为分数。 Ch Topic Core idea Methods foundations . Weeks 1-6 1 The research process 7 steps · symptom - problem . MDP vs MRQ → 2 Secondary data & hypotheses primary vs secondary . DV/IV . conceptual model → 3 Qualitative research interviews . focus groups . thematic analysis → 4 Measurement & scaling the 4 scale types . reliability & validity . a → 5 Research designs exploratory · descriptive . causal / experiments →[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading[22]Source: asksia-cheatsheet-mktg90011.pdfW4 Predict a metric DV from 1 / 2+ IVs Bivariate / multiple regression R2/Adj R2; ANOVA Sig . ; B, Beta, Sig. Reduce many correlated items PCA (factor analysis) KMO, Bartlett; eigenvalue>1; loadings Profile 1 variable ("which is most . . . ", "how much spread") Descriptive (not a test) Mode / Median / Mean + SD by scale Scope: only these are taught. NOT examined - cluster, conjoint, MDS/perceptual maps, discriminant. 3b . The 4-Verb Guide DECODE THE VERB MATCH THE QUESTION'S VERB COMPARE means - t-test (2) / ANOVA (3+) ASSOCIATE + x2 (cat) / correlation (metric) PREDICT a DV - regression REDUCE items + PCA If the question says "differ / higher / vs" you are comparing means -> count the groups. "Related / linked / depends on" > associate. "Explain / predict / drives" -> regression. "most frequent / which is the . . . " (1 var) -> descriptive (mode), not a test. Paired vs independent hinges on dependence of observations : same people rated twice (pre/post, two Qs) = paired; two separate groups = independent. Identify this before you name the t-test type. COMPARE needs a metric DV + a grouping variable (count its groups: 2 -> t, 3+ -> ANOVA). ASSOCIATE needs two variables & no DV/IV split. PREDICT names a DV explained by IVs. REDUCE has many items, no DV at all. -- SIA > Decode the verb first, then count groups & check scale. The grader rewards the scale - #groups - paired? - test path written out, not a guessed test name. 4 . The 4 Scale Types W4 . DRIVES TEST CHOICE Scale type sets which stat & test are allowed identify it first, every time.[25]Source: asksia-cheatsheet-mktg90011.pdfχ² Spear. Spear. I/R x2 Spear. Pearson x2 = ≥1 nominal · Spearman = ≥1 ordinal (rank, non- normal) · Pearson = both interval/ratio & linear. Describe any association by presence · direction . strength . x2 gives presence + strength (C) but no direction; correlation gives all three. Association # causation - need an experiment for that. The diagonal is the easy case (same scale on both axes); off-diagonals fall to the weaker scale - any nominal forces x2; any ordinal (no nominal) forces Spearman. Pearson is reserved for two clean metric vars. 12 . Side-1 Recap CARRY TO SIDE 2 You now have the selection logic : verb > scale -> #groups -> test. Side 2 turns each test into a do-this block: WHEN . SPSS path . KEY OUTPUT . APA sentence, then the qualitative methods, research designs, and the project's H1-H6 test map. One-line scope check: if a question tempts you toward cluster, conjoint, MDS or discriminant - stop. They are not taught; the answer is always inside the eight-test toolkit above (+ descriptive). Same logic, two payoffs: the exam (50%) tests it as MCQ + long-answer, and the project (40%) hard-codes it as the H1-H6 map. Get the selection reflex automatic and you've covered 90% of the grade. SIA > In a "choose the test" item, write your reasoning: "region has 4 categories & satisfaction is interval ++ 3+ group means on a metric DV - one-way ANOVA; significant F - run Tukey for which pairs. " The lecturer rewards the path, not a guessed name. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 1/2 AskSia CHEAT SHEET SERIES 9 · Sampling Population (all of interest) -> sample (subset examined). Parameter describes the population (u); statistic describes the sample. Sampling frame = list of every element; frame # population is a non- sampling error. PROBABILITY NON-PROBABILITY
-
C. 预测（PREDICT metric DV）
- Regression（回归）
- 一元 vs 多元：metric DV 由 1 个 IV → bivariate；由 2+ IV → multiple [15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
- 读表顺序（强烈建议背下来）：
- 关键解释：
- $B$（非标准化）：$X$ 增 1 单位，$Y$ 变 $B$ 单位（其他变量不变）[6]Source: asksia-bible-mktg90011-bilingual.pdf调整后决定系数 R2 penalised for extra IVs; use it to compare multiple-predictor models. B (unstandardised) 非标准化系数 The equation coefficient: a one-unit change in X - B-unit change in Y. Beta (standardised) 标准化系数 Relative importance of each predictor (don't compare for dummies). Omnibus F-test 总体 F 检验 Regression/ANOVA validity: Sig. ≤ . 05 - the model has predictive power. APA reporting APA 报告格式 t(df) =. . , p <. . . / F(df1,df2) =. . . / x2(df,N) =. . . - the sentence the unit wants. Syntax 语法记录 SPSS command log; record cleaning steps so the analysis is reproducible. ✓ How to drill the glossary for the exam 如何为考试操练术语表 Two passes. First EN-x: read the term, recall the gloss - this builds bilingual recall. Then EX->term: cover the left two columns, read the one-line meaning, and name the term - this is the exact shape of a Type-A conceptual MCQ. For the test rows, add a third pass: say the variable structure that forces each test (scale x #groups x paired?) - that is the Type-C "which test" skill in vocabulary form. 两遍过。先 EN→中文:读术语,回忆释义 -- 这练就双语回忆。再 定义→术语:盖住左侧两列,读那句一行释义,说 出术语 -- 这正是 A类概念选择题的题型。对检验那几行,加第三遍:说出迫使每个检验成立的变量结构(量表 ×组 数 ×是否配对?) -- 那就是C类“选哪个检验”技能的词汇版。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . CHOOSE THE TEST 1-3 - PRACTICE BANK FOR THE 50% FINAL Drill the exam's one skill: which test? 操练考试的核心技能:用哪种检验? AskSia-authored items in the exam's own style - every one worked AskSia自编、采用考试风格的题目 -- 每题皆有解答 TL;DR. The final exam is 50% of MKTG90011 and its make-or-break move is "which test for which question. " Read the research goal, label each variable's scale (nominal / ordinal / interval / ratio), count the groups, ask paired or independent - then the test falls out. Every item below is AskSia-invented neutral data; the statistics are the unit's real canon.[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
- Beta（标准化）：相对重要性（但不要拿 dummy 的 Beta 去比）[6]Source: asksia-bible-mktg90011-bilingual.pdf调整后决定系数 R2 penalised for extra IVs; use it to compare multiple-predictor models. B (unstandardised) 非标准化系数 The equation coefficient: a one-unit change in X - B-unit change in Y. Beta (standardised) 标准化系数 Relative importance of each predictor (don't compare for dummies). Omnibus F-test 总体 F 检验 Regression/ANOVA validity: Sig. ≤ . 05 - the model has predictive power. APA reporting APA 报告格式 t(df) =. . , p <. . . / F(df1,df2) =. . . / x2(df,N) =. . . - the sentence the unit wants. Syntax 语法记录 SPSS command log; record cleaning steps so the analysis is reproducible. ✓ How to drill the glossary for the exam 如何为考试操练术语表 Two passes. First EN-x: read the term, recall the gloss - this builds bilingual recall. Then EX->term: cover the left two columns, read the one-line meaning, and name the term - this is the exact shape of a Type-A conceptual MCQ. For the test rows, add a third pass: say the variable structure that forces each test (scale x #groups x paired?) - that is the Type-C "which test" skill in vocabulary form. 两遍过。先 EN→中文:读术语,回忆释义 -- 这练就双语回忆。再 定义→术语:盖住左侧两列,读那句一行释义,说 出术语 -- 这正是 A类概念选择题的题型。对检验那几行,加第三遍:说出迫使每个检验成立的变量结构(量表 ×组 数 ×是否配对?) -- 那就是C类“选哪个检验”技能的词汇版。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . CHOOSE THE TEST 1-3 - PRACTICE BANK FOR THE 50% FINAL Drill the exam's one skill: which test? 操练考试的核心技能:用哪种检验? AskSia-authored items in the exam's own style - every one worked AskSia自编、采用考试风格的题目 -- 每题皆有解答 TL;DR. The final exam is 50% of MKTG90011 and its make-or-break move is "which test for which question. " Read the research goal, label each variable's scale (nominal / ordinal / interval / ratio), count the groups, ask paired or independent - then the test falls out. Every item below is AskSia-invented neutral data; the statistics are the unit's real canon.[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
- $R^2$：解释的方差比例；比较多预测变量模型用 Adjusted $R^2$ [6]Source: asksia-bible-mktg90011-bilingual.pdf调整后决定系数 R2 penalised for extra IVs; use it to compare multiple-predictor models. B (unstandardised) 非标准化系数 The equation coefficient: a one-unit change in X - B-unit change in Y. Beta (standardised) 标准化系数 Relative importance of each predictor (don't compare for dummies). Omnibus F-test 总体 F 检验 Regression/ANOVA validity: Sig. ≤ . 05 - the model has predictive power. APA reporting APA 报告格式 t(df) =. . , p <. . . / F(df1,df2) =. . . / x2(df,N) =. . . - the sentence the unit wants. Syntax 语法记录 SPSS command log; record cleaning steps so the analysis is reproducible. ✓ How to drill the glossary for the exam 如何为考试操练术语表 Two passes. First EN-x: read the term, recall the gloss - this builds bilingual recall. Then EX->term: cover the left two columns, read the one-line meaning, and name the term - this is the exact shape of a Type-A conceptual MCQ. For the test rows, add a third pass: say the variable structure that forces each test (scale x #groups x paired?) - that is the Type-C "which test" skill in vocabulary form. 两遍过。先 EN→中文:读术语,回忆释义 -- 这练就双语回忆。再 定义→术语:盖住左侧两列,读那句一行释义,说 出术语 -- 这正是 A类概念选择题的题型。对检验那几行,加第三遍:说出迫使每个检验成立的变量结构(量表 ×组 数 ×是否配对?) -- 那就是C类“选哪个检验”技能的词汇版。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . CHOOSE THE TEST 1-3 - PRACTICE BANK FOR THE 50% FINAL Drill the exam's one skill: which test? 操练考试的核心技能:用哪种检验? AskSia-authored items in the exam's own style - every one worked AskSia自编、采用考试风格的题目 -- 每题皆有解答 TL;DR. The final exam is 50% of MKTG90011 and its make-or-break move is "which test for which question. " Read the research goal, label each variable's scale (nominal / ordinal / interval / ratio), count the groups, ask paired or independent - then the test falls out. Every item below is AskSia-invented neutral data; the statistics are the unit's real canon.[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
- VIF < 10（多重共线性检查）[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
-
D. 降维（REDUCE many items）
- PCA（主成分分析）：把许多相关的度量型题项压成少数互不相关的成分；它不是假设检验。[3]Source: asksia-bible-mktg90011-bilingual.pdf✓ The strategy this dictates 由此推导出的策略 One skill dominates: which test for which question. Drill the tree opposite until it is reflex - categorical × categorical - chi-square; one metric outcome over 2 groups - t-test (independent if separate people, paired if same/before-after); 3+ groups - one-way ANOVA; two metric variables - correlation (Pearson if linear&normal, Spearman if ordinal/non-normal); predict a metric outcome - regression; reduce many items - PCA. The project mandates this same map - H1 chi-square . H2 & H3 two different t-tests . H4 one-way ANOVA . H5 multiple regression . H6 any - so the work you do for one piece directly serves the other. 有一项技能压倒一切:哪个问题用哪个检验。把对页 那棵树练到成为本能 -- 类别×类别→ 卡方检验; 一个度量型结果跨 2组→t检验(不同的人则独立, 同一批/前后则配对);3+组→单因素方差分析;两 个度量型变量 →相关(线性且正态用皮尔逊,定序/ 非正态用斯皮尔曼);预测一个度量型结果 →回归; 缩减众多测量项→ PCA。项目强制同一张地图 -- H1 卡方 · H2 与 H3 两种不同的 t检验 · H4 单因素方差 分析 · H5 多元回归 · H6 任意 -- 所以你为其中一部 分所做的功直接服务于另一部分。 ★ The single highest-value hour 单位时间价值最高的那一小时 If you study one thing, study the which-test tree and the output-reading rule for each test (the statistic, the Sig. /p, the decision, the one-line APA sentence). Then build your permitted bring-in notes around it - after confirming the exact materials allowed on your exam cover sheet. 若你只学一样东西,就学选检验决策树和每个检验的 读输出规则(统计量、Sig. /p、决策、一句 APA 句)。然后围绕它搭建你的允许带入笔记 -- 在确认了 你的考试封面页上准许的确切材料之后。 AskSia Library · MKTG90011 · 双语 Bilingual CONTENTS CONTENTS The whole subject, in one ordered book 整门课,浓缩为一本有序之书 Twelve seminar weeks - one process-and-which-test map 十二周研讨课 →一张流程兼“选检验”地图 TL;DR. The book follows the unit's arc - build the method foundations (W1-6), then the statistical toolkit organised around test selection (W7-11), then turn it into marks with the project playbook, glossary and practice bank. 一句话总结。本书顺着本课的脉络展开 -- 先打牢方法基础(第1-6周),再围绕检验选择组织起统计工具箱(第 7-11 周),最后通过项目实战手册、术语表与练习题库把它转化为分数。 Ch Topic Core idea Methods foundations . Weeks 1-6 1 The research process 7 steps · symptom - problem . MDP vs MRQ → 2 Secondary data & hypotheses primary vs secondary . DV/IV . conceptual model → 3 Qualitative research interviews . focus groups . thematic analysis → 4 Measurement & scaling the 4 scale types . reliability & validity . a → 5 Research designs exploratory · descriptive . causal / experiments →[9]Source: asksia-bible-mktg90011-bilingual.pdf→ PCA trigger: reduce many correlated metric items to a few uncorrelated components - not a hypothesis test. PCA 触发信号:把许多相关的度量型项目压缩成少数几个互不相关的成分 - 不是假设检验。 → Suitability: KMO > . 50; Bartlett Sig. < . 05 (reject Ho of no correlation). I 适合性:KMO>. 50; Bartlett Sig . <. 05(拒绝“无相关”的 Ho)。 → How many: eigenvalue > 1 (Kaiser) + scree elbow (keep one fewer than the elbow); report cumulative % variance. 保留几个:特征值>1(凯撒准则)+碎石图拐点(保留比拐点少一个);报告累计方差百分比。 - Interpret: Varimax -> Rotated Component Matrix; highest loading assigns the item; name each component; communality ≥ . 3. 解释:Varimax → 旋转后成分矩阵;最高载荷决定项目归属;为每个成分命名;共同度≥. 3。 → Visualisation: match chart to scale (bar/pie categorical, line metric, scatter two-metric, histogram/boxplot distribution); avoid 3-D pies & truncated axes. 可视化:图表匹配量表(条形/饼图用于分类、折线用于度量、散点用于双度量、直方/箱线用于分布);避免 3D 饼图和 截断坐标轴。 → PCA 触发信号:把许多相关的度量型项目压缩成少数几个互不相关的成分 -- 不是假设检验。 → 适合性:KMO>. 50; Bartlett Sig. <. 05 (拒绝“变量间无相关”的Ho)。 → 保留几个:特征值>1(凯撒准则)+碎石图拐点(保留比拐点少一个);报告累计方差百分比。 → 解释:Varimax→ 旋转后成分矩阵;最高载荷决定项目归属;为每个成分命名;共同度≥. 3。 → 可视化:图表匹配量表(条形/饼图用于分类、折线用于度量、散点用于双度量、直方/箱线用于分布);避免 3D 饼 图和截断坐标轴。 AskSia Library · MKTG90011 · 双语 Bilingual PROJECT . 40% PROJECT . 40% GROUP OF 4-5 . ~ 6,000 WORDS The research project playbook 研究项目实战手册 One real client, one team, eight deliverables - and a fixed test map that mirrors the exam 一个真实客户、一个团队、八项交付物 -- 以及一张与考试相互映照的固定检验地图 TL;DR. The project re-runs the marketing-research process end-to-end on a live client problem - the "loneliness economy" theme - and it forces a fixed sequence of six hypothesis tests (H1 chi-square - H6 free choice). Learn that map here and you have also learnt the exam's "which test" skill. 一句话总结。项目把市场营销研究流程从头到尾在一个真实客户问题上重跑一遍 -- “孤独经济”主题 -- 并强制一组固定顺 序的六项假设检验(H1 卡方→H6 自由选择)。在这里学会这张地图,你也就学会了考试的“选哪个检验”技能。 ★ What the exam asks here 考试在这里问什么 The project is examinable indirectly: the same H1-H6 test mapping is the model for the exam's signature "choose the right test" item (worth a large slice of the 50% final). If you can justify why H4 is a one-way ANOVA and not a t- test, you can answer the exam version of the same question. Memorise the map, then practise reading the variable structure that forces each test. 项目以间接方式被考查:同一套 H1-H6 检验映射正是考试招牌题“选对检验”的范本(在 50% 期末考中占很大比重)。 如果你能论证为什么 H4 是单因素 ANOVA 而非 t检验,你就能回答同一问题的考试版本。先记住这张映射表,再练习读 出迫使每个检验成立的变量结构。 1 The pipeline - one client problem, end to end 1流水线 -- 一个客户问题,从头到尾 PROJ 1 define the problem first; everything downstream serves the decision Problem definition[14]Source: asksia-bible-mktg90011-bilingual.pdfOn the 50% final, PCA shows up as read-the-output items: is the data suitable (KMO > . 50, Bartlett Sig. < . 05)? How many components (eigenvalue > 1 / the scree elbow)? Which items load where (Rotated Component Matrix)? And the "which technique" trigger: reduce many correlated items to underlying dimensions - PCA. It is a valid choice for project H6 and a fix for multicollinearity in H5. Out of scope: oblique/promax rotation, principal-axis factoring and confirmatory factor analysis are flagged "not in the exam. " 在 50% 期末考里,PCA 主成分分析以读输出的题型出现:数据是否适合(KMO >. 50,Bartlett Sig. <. 05)?保留 几个成分(特征值>1/碎石图的拐点(elbow))?哪些项目载荷在哪里(旋转后成分矩阵)?以及“选哪种技术”的触 发信号:把许多相关的项目压缩成潜在维度→PCA。它是项目 H6 的合法选项,也是 H5 中处理多重共线性的修正手 段。超出范围:斜交/promax旋转、主轴因子法、验证性因子分析都被标注为“不会出现在考试中”。 Step What you do Decision rule 1. Requirements Check sample size + correlations + suitability n > 10xk items; most r ≥. 30; KMO > . 50; Bartlett Sig. < . 05 2. Extract Project onto new orthogonal axes ordered by variance PC1 = most variance, PC2 next . . . (Principal components) 3. How many factors Keep the components worth keeping Kaiser: eigenvalue > 1; Scree: the elbow; cumulative % variance 4. Interpret Rotate & name each component Varimax rotation; assign each item to its highest loading; label it 5. Goodness-of-fit Do the factors reproduce the correlations? Communalities ≥. 3; few residual correlations > |. 05| AskSia Library · MKTG90011 · 双语 Bilingual KMO . BARTLETT . KEY QUANTITIES Suitability gate. KMO (Kaiser-Meyer-01kin) sampling adequacy - >. 90 marvellous . . 80 -. 90 meritorious . . 70 -. 80 middling . . 60 -. 70 mediocre . >. 50 minimum. Bartlett's Test of Sphericity - Ho: variables mutually uncorrelated; reject Ho (Sig. < . 05) - enough correlation to justify PCA. Eigenvalue = variance a component captures ; communality = variance of one item captured by the retained components; loading = correlation between an item and a component (-1 . . . +1) . AskSia Library . MKTG90011 . XXia Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Eigenvalues, the scree plot & loadings 特征值、碎石图与载荷 how many components, and what to call them 保留多少个成分,以及怎么给它们命名 TL;DR. Keep components with eigenvalue > 1 (Kaiser) and confirm with the scree elbow. Then read the Rotated Component Matrix (Varimax) - assign each item to the component on which it loads highest, and give that cluster an umbrella name.
- 考试最常问的是“读输出”：[14]Source: asksia-bible-mktg90011-bilingual.pdfOn the 50% final, PCA shows up as read-the-output items: is the data suitable (KMO > . 50, Bartlett Sig. < . 05)? How many components (eigenvalue > 1 / the scree elbow)? Which items load where (Rotated Component Matrix)? And the "which technique" trigger: reduce many correlated items to underlying dimensions - PCA. It is a valid choice for project H6 and a fix for multicollinearity in H5. Out of scope: oblique/promax rotation, principal-axis factoring and confirmatory factor analysis are flagged "not in the exam. "
在 50% 期末考里,PCA 主成分分析以读输出的题型出现:数据是否适合(KMO >. 50,Bartlett Sig. <. 05)?保留 几个成分(特征值>1/碎石图的拐点(elbow))?哪些项目载荷在哪里(旋转后成分矩阵)?以及“选哪种技术”的触 发信号:把许多相关的项目压缩成潜在维度→PCA。它是项目 H6 的合法选项,也是 H5 中处理多重共线性的修正手 段。超出范围:斜交/promax旋转、主轴因子法、验证性因子分析都被标注为“不会出现在考试中”。
Step
What you do
Decision rule
1. Requirements
Check sample size + correlations + suitability
n > 10xk items; most r ≥. 30; KMO > . 50; Bartlett Sig. < . 05
2. Extract
Project onto new orthogonal axes ordered by variance
PC1 = most variance, PC2 next . . . (Principal components)
3. How many factors
Keep the components worth keeping
Kaiser: eigenvalue > 1; Scree: the elbow; cumulative % variance
4. Interpret
Rotate & name each component
Varimax rotation; assign each item to its highest loading; label it
5. Goodness-of-fit
Do the factors reproduce the correlations?
Communalities ≥. 3; few residual correlations > |. 05|
AskSia Library · MKTG90011 · 双语 Bilingual
KMO . BARTLETT . KEY QUANTITIES
Suitability gate. KMO (Kaiser-Meyer-01kin) sampling adequacy - >. 90 marvellous . . 80 -. 90 meritorious . . 70 -. 80 middling . . 60 -. 70 mediocre . >. 50 minimum. Bartlett's Test of Sphericity - Ho: variables mutually uncorrelated; reject Ho (Sig. < . 05) - enough correlation to justify PCA. Eigenvalue = variance a component captures ; communality = variance of one item captured by the retained components; loading = correlation between an item and a component (-1 . . . +1) .
AskSia Library . MKTG90011 . XXia Bilingual
WEEK 11 . PCA & VISUALISATION
- WEEK 11 . PCA & VISUALISATION
Eigenvalues, the scree plot & loadings
特征值、碎石图与载荷
how many components, and what to call them 保留多少个成分,以及怎么给它们命名
TL;DR. Keep components with eigenvalue > 1 (Kaiser) and confirm with the scree elbow. Then read the Rotated Component Matrix (Varimax) - assign each item to the component on which it loads highest, and give that cluster an umbrella name.
- 适合性门槛：KMO > .50；Bartlett Sig. < .05
- 保留几个：特征值 eigenvalue > 1（Kaiser）+ 碎石图拐点（scree elbow）
- 怎么解释：看 Rotated Component Matrix（Varimax），每题项归到最高载荷的成分并命名；communality 通常看是否 ≥ .3 [9]Source: asksia-bible-mktg90011-bilingual.pdf→ PCA trigger: reduce many correlated metric items to a few uncorrelated components - not a hypothesis test. PCA 触发信号:把许多相关的度量型项目压缩成少数几个互不相关的成分 - 不是假设检验。 → Suitability: KMO > . 50; Bartlett Sig. < . 05 (reject Ho of no correlation). I 适合性:KMO>. 50; Bartlett Sig . <. 05(拒绝“无相关”的 Ho)。 → How many: eigenvalue > 1 (Kaiser) + scree elbow (keep one fewer than the elbow); report cumulative % variance. 保留几个:特征值>1(凯撒准则)+碎石图拐点(保留比拐点少一个);报告累计方差百分比。 - Interpret: Varimax -> Rotated Component Matrix; highest loading assigns the item; name each component; communality ≥ . 3. 解释:Varimax → 旋转后成分矩阵;最高载荷决定项目归属;为每个成分命名;共同度≥. 3。 → Visualisation: match chart to scale (bar/pie categorical, line metric, scatter two-metric, histogram/boxplot distribution); avoid 3-D pies & truncated axes. 可视化:图表匹配量表(条形/饼图用于分类、折线用于度量、散点用于双度量、直方/箱线用于分布);避免 3D 饼图和 截断坐标轴。 → PCA 触发信号:把许多相关的度量型项目压缩成少数几个互不相关的成分 -- 不是假设检验。 → 适合性:KMO>. 50; Bartlett Sig. <. 05 (拒绝“变量间无相关”的Ho)。 → 保留几个:特征值>1(凯撒准则)+碎石图拐点(保留比拐点少一个);报告累计方差百分比。 → 解释:Varimax→ 旋转后成分矩阵;最高载荷决定项目归属;为每个成分命名;共同度≥. 3。 → 可视化:图表匹配量表(条形/饼图用于分类、折线用于度量、散点用于双度量、直方/箱线用于分布);避免 3D 饼 图和截断坐标轴。 AskSia Library · MKTG90011 · 双语 Bilingual PROJECT . 40% PROJECT . 40% GROUP OF 4-5 . ~ 6,000 WORDS The research project playbook 研究项目实战手册 One real client, one team, eight deliverables - and a fixed test map that mirrors the exam 一个真实客户、一个团队、八项交付物 -- 以及一张与考试相互映照的固定检验地图 TL;DR. The project re-runs the marketing-research process end-to-end on a live client problem - the "loneliness economy" theme - and it forces a fixed sequence of six hypothesis tests (H1 chi-square - H6 free choice). Learn that map here and you have also learnt the exam's "which test" skill. 一句话总结。项目把市场营销研究流程从头到尾在一个真实客户问题上重跑一遍 -- “孤独经济”主题 -- 并强制一组固定顺 序的六项假设检验(H1 卡方→H6 自由选择)。在这里学会这张地图,你也就学会了考试的“选哪个检验”技能。 ★ What the exam asks here 考试在这里问什么 The project is examinable indirectly: the same H1-H6 test mapping is the model for the exam's signature "choose the right test" item (worth a large slice of the 50% final). If you can justify why H4 is a one-way ANOVA and not a t- test, you can answer the exam version of the same question. Memorise the map, then practise reading the variable structure that forces each test. 项目以间接方式被考查:同一套 H1-H6 检验映射正是考试招牌题“选对检验”的范本(在 50% 期末考中占很大比重)。 如果你能论证为什么 H4 是单因素 ANOVA 而非 t检验,你就能回答同一问题的考试版本。先记住这张映射表,再练习读 出迫使每个检验成立的变量结构。 1 The pipeline - one client problem, end to end 1流水线 -- 一个客户问题,从头到尾 PROJ 1 define the problem first; everything downstream serves the decision Problem definition[14]Source: asksia-bible-mktg90011-bilingual.pdfOn the 50% final, PCA shows up as read-the-output items: is the data suitable (KMO > . 50, Bartlett Sig. < . 05)? How many components (eigenvalue > 1 / the scree elbow)? Which items load where (Rotated Component Matrix)? And the "which technique" trigger: reduce many correlated items to underlying dimensions - PCA. It is a valid choice for project H6 and a fix for multicollinearity in H5. Out of scope: oblique/promax rotation, principal-axis factoring and confirmatory factor analysis are flagged "not in the exam. " 在 50% 期末考里,PCA 主成分分析以读输出的题型出现:数据是否适合(KMO >. 50,Bartlett Sig. <. 05)?保留 几个成分(特征值>1/碎石图的拐点(elbow))?哪些项目载荷在哪里(旋转后成分矩阵)?以及“选哪种技术”的触 发信号:把许多相关的项目压缩成潜在维度→PCA。它是项目 H6 的合法选项,也是 H5 中处理多重共线性的修正手 段。超出范围:斜交/promax旋转、主轴因子法、验证性因子分析都被标注为“不会出现在考试中”。 Step What you do Decision rule 1. Requirements Check sample size + correlations + suitability n > 10xk items; most r ≥. 30; KMO > . 50; Bartlett Sig. < . 05 2. Extract Project onto new orthogonal axes ordered by variance PC1 = most variance, PC2 next . . . (Principal components) 3. How many factors Keep the components worth keeping Kaiser: eigenvalue > 1; Scree: the elbow; cumulative % variance 4. Interpret Rotate & name each component Varimax rotation; assign each item to its highest loading; label it 5. Goodness-of-fit Do the factors reproduce the correlations? Communalities ≥. 3; few residual correlations > |. 05| AskSia Library · MKTG90011 · 双语 Bilingual KMO . BARTLETT . KEY QUANTITIES Suitability gate. KMO (Kaiser-Meyer-01kin) sampling adequacy - >. 90 marvellous . . 80 -. 90 meritorious . . 70 -. 80 middling . . 60 -. 70 mediocre . >. 50 minimum. Bartlett's Test of Sphericity - Ho: variables mutually uncorrelated; reject Ho (Sig. < . 05) - enough correlation to justify PCA. Eigenvalue = variance a component captures ; communality = variance of one item captured by the retained components; loading = correlation between an item and a component (-1 . . . +1) . AskSia Library . MKTG90011 . XXia Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Eigenvalues, the scree plot & loadings 特征值、碎石图与载荷 how many components, and what to call them 保留多少个成分,以及怎么给它们命名 TL;DR. Keep components with eigenvalue > 1 (Kaiser) and confirm with the scree elbow. Then read the Rotated Component Matrix (Varimax) - assign each item to the component on which it loads highest, and give that cluster an umbrella name.
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3）必背“定义/一句话”清单（MCQ 和简答都靠它）
- Marketing research：通过信息把消费者与营销者连接起来，用于定义/设计/收集/分析/报告（单位要求精确术语）。[18]Source: asksia-cheatsheet-mktg90011.pdfMKTG90011 Marketing Research . The Which-Test Map UNIV. OF MELBOURNE . FBE . GRADUATE CHEAT SHEET Sem 1 2026 . SIDE 1 OF 2 Exam 50% · Project 40% · Quiz 10% SIDE 1/2 EXAM BLUEPRINT & THE WHICH-TEST MAP . Research process . Problem (MDP/RQ/H) . * Which-test table . Compare/Associate/Predict/Reduce . 4 scale types . Reliability & validity . Sampling EXAM 50% . WHICH-TEST = THE SKILL Compiled by AskSia . mapped to the MKTG90011 syllabus . asksia. ai/cheatsheet/unimelb-mktg90011 0 . How to Use This READ FIRST * Grading: Final exam 50% . Group project 40% . Quizzes 10% (5×2%). The exam is on-campus, individual, digital (LMS quiz under a lockdown browser), ~2h + 15min reading, MCQ + long-answer (answer all). Examinable = seminar notes + exercises + textbook Ch 1-7, 10 (Sarstedt & Mooi, 3rd ed. ). Software: IBM SPSS 29 + Qualtrics. Permitted materials: a W9 slide lists prescribed text + paper notes + non-programmable calculator. Build concise bring-in notes & confirm the permitted- materials line on your own exam cover sheet - don't assume open-book. -- SIA > The make-or-break skill is "which test for which question". Master Side 1's selection table; Side 2 drills each test's SPSS path, output & APA sentence. 1 . The Research Process W1 . 7 STEPS The spine of the whole unit. The first step is the most important. 1. Identify & formulate the problem 2. Determine the research design (explor. /descr. /causal) 3. Design the sample & data-collection method 4. Collect the data 5. Analyse the data (< the which-test skill lives here) 6. Interpret, discuss & present findings 7. Follow-up Marketing research = the function linking consumer to marketer through information - to identify/define problems & opportunities, evaluate actions, monitor performance. Symptom > problem (iceberg): the visible symptom (falling sales) sits above the real decision problem. The first step - define the problem right - drives everything after it.[10]Source: asksia-bible-mktg90011-bilingual.pdf→ Every test: state the assumptions checked, the statistic, the p-value, the decision, a visual, and a plain-English line. 每个检验:说明检查过的假设、统计量、p值、决策、一张可视化图、以及一句大白话结论。 → Marks live in Quant (9) and Qualitative (7); mind the word caps; let qualitative themes generate the survey constructs. 分数所在是定量(9分)和定性(7分);注意字数上限;让定性主题去生成问卷的构念。 → 流水线:MDP→≤3个研究问题(+4P 子问题)→文献/二手数据→定性研究(≥1种方法,达到最低样本)→ 25 题 Qualtrics 问卷(n≥ 100)→ SPSS→报告+6 页幻灯片。 → 检验映射:H1 卡方 · H2 一个 t检验 · H3 一个不同的 t检验 · H4 单因素 ANOVA · H5 多元回归 · H6 任选 描述统计 +≥1个相关分析。 → 每个检验:说明检查过的假设、统计量、p值、决策、一张可视化图、以及一句大白话结论。 → 分数所在是定量(9分)和定性(7分);注意字数上限;让定性主题去生成问卷的构念。 AskSia Library · MKTG90011 · 双语 Bilingual GLOSSARY . 1/3 - GLOSSARY BILINGUAL · 双语词汇表 MKTG90011 in two languages MKTG90011,双语对照 Every core term, its X, and a one-line meaning - grouped by theme 每个核心术语、其中文,以及一行解释 -- 按主题归类 TL;DR. The exam rewards precise vocabulary - a wrong word ("accept HO", "mean of an ordinal scale") loses MCQ marks. This is the recall layer: ~ 95 terms across the unit, each with an accurate Chinese gloss. Cover the right two columns and define each term cold - recognition is not recall. 一句话总结。考试奖励精确的术语 -- 一个用错的词(“接受 HO”、“定序量表的均值”)会丢掉选择题分数。这是回忆层:全 课约 95个术语,每个配一个准确的中文释义。遮住右边两列,对每个术语冷启动地作答 -- 认得出不等于回忆得起。 ★ What the exam asks here 考试在这里问什么 Conceptual recall items (Type A) test exactly these definitions - what is a p-value? which descriptive fits an ordinal variable? what does Levene's test check? The test-selection items (Type C) depend on the scaling and test vocabulary below. If your bring-in notes are permitted, this table is the page to copy - confirm the exact permitted materials on your exam cover sheet first. 概念回忆题(A类)正是考查这些定义 -- 什么是p值?哪种描述统计适合定序变量?Levene 检验检查什么?选检验 题(C类)则依赖下面的量表与检验术语。如果允许携带带入小抄,这张表就是要抄的那一页 -- 请先在考试封面页上 确认确切允许携带的材料。 Term (EN) 中文 One-line meaning Process & research design · 流程与设计 Marketing research 市场调研 The information function linking consumer to marketer; defines, designs, collects, analyses, reports. Research process (7 steps) 调研流程七 步 Define problem - design - sample/collect - collect - analyse - report - follow- up. Marketing symptom
- Research process（7 steps）：从定义问题到设计、抽样/收集、分析、呈现与 follow-up；其中“分析”阶段就是 which-test 技能所在。[18]Source: asksia-cheatsheet-mktg90011.pdfMKTG90011 Marketing Research . The Which-Test Map UNIV. OF MELBOURNE . FBE . GRADUATE CHEAT SHEET Sem 1 2026 . SIDE 1 OF 2 Exam 50% · Project 40% · Quiz 10% SIDE 1/2 EXAM BLUEPRINT & THE WHICH-TEST MAP . Research process . Problem (MDP/RQ/H) . * Which-test table . Compare/Associate/Predict/Reduce . 4 scale types . Reliability & validity . Sampling EXAM 50% . WHICH-TEST = THE SKILL Compiled by AskSia . mapped to the MKTG90011 syllabus . asksia. ai/cheatsheet/unimelb-mktg90011 0 . How to Use This READ FIRST * Grading: Final exam 50% . Group project 40% . Quizzes 10% (5×2%). The exam is on-campus, individual, digital (LMS quiz under a lockdown browser), ~2h + 15min reading, MCQ + long-answer (answer all). Examinable = seminar notes + exercises + textbook Ch 1-7, 10 (Sarstedt & Mooi, 3rd ed. ). Software: IBM SPSS 29 + Qualtrics. Permitted materials: a W9 slide lists prescribed text + paper notes + non-programmable calculator. Build concise bring-in notes & confirm the permitted- materials line on your own exam cover sheet - don't assume open-book. -- SIA > The make-or-break skill is "which test for which question". Master Side 1's selection table; Side 2 drills each test's SPSS path, output & APA sentence. 1 . The Research Process W1 . 7 STEPS The spine of the whole unit. The first step is the most important. 1. Identify & formulate the problem 2. Determine the research design (explor. /descr. /causal) 3. Design the sample & data-collection method 4. Collect the data 5. Analyse the data (< the which-test skill lives here) 6. Interpret, discuss & present findings 7. Follow-up Marketing research = the function linking consumer to marketer through information - to identify/define problems & opportunities, evaluate actions, monitor performance. Symptom > problem (iceberg): the visible symptom (falling sales) sits above the real decision problem. The first step - define the problem right - drives everything after it.
- MDP vs MRQ
- p-value vs $\alpha$（6 步假设检验纪律）
- Sampling error vs non-sampling error
- 抽样误差随样本量 $n$ 增大而下降
- 非抽样误差（框误差、无应答、测量误差、录入错误等）不会因为 $n$ 大就自动消失 [12]Source: asksia-bible-mktg90011-bilingual.pdf! Sampling vs non-sampling error 抽样误差 vs 非抽样误差 Sampling error = the gap between a sample estimate and the true value just from studying a subset (sample 70% vs population 73% - 3%). It shrinks as n grows. Non-sampling error = everything else: frame error, nonresponse, measurement error, data-entry slips - and a bigger n does not fix it. 抽样误差 = 仅因研究一个子集而导致的样本估计与真 值之间的差距(样本 70% vs 总体 73%→3%)。它 随 n 增大而缩小。非抽样误差 = 其余一切:框误差、 无应答、测量误差、录入失误 -- 而更大的n 并不能 修正它。 ✓ Population size barely matters 总体规模几乎无关紧要 Because required n is (almost) independent of N, a Qualtrics calculator at 95%/5% returns ~384 whether the population is 10,000 or 1,000,000. Going 40-60 helps far more than 1,000-1,020 (diminishing returns). Watch for bots inflating online panels. 由于所需 n(几乎)与总体大小 N 无关,一个 Qualtrics 计算器在 95%/5% 下返回约 384,无论总 体是 10,000 还是1,000,000。把40→60 比把 1,000→1,020 帮助大得多(回报递减)。当心机器人在 线上样本组中灌水。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist - Week 6 回忆清单 -- 第6周 · Probability (SRS / systematic / stratified / cluster) = generalisable + error computable; non-probability (convenience / judgment / quota / snowball) = not projectable. 概率抽样(简单随机/系统/分层/整群)=可推广+误差可计算;非概率抽样(便利/判断/配额/滚雪球)= 不可外推。 · Stratified: homogeneous-within, sample every stratum, more precise. Cluster: heterogeneous-within, sample whole clusters, cheaper. 1 分层:层内同质,每一层都抽样,更精确。整群:群内异质,整群抽样,更便宜。 · n for a mean = (zo/E)2; n for a proportion = p(1-p)(z/E)2; z: 95%-+1. 96. Round up; per group. I 均值的 n =(zo/E)2;比例的 n= p(1-p)(z/E)2;z:95%→1. 96。向上取整;按每组算。 · Sampling error falls with n; non-sampling error does not - n is (almost) independent of N. 抽样误差随 n 增大而下降;非抽样误差不会 -- n与总体规模N(几乎)无关。 1 项目下限:n ≥100 份可用 -- 实际要多发很多份。 ● 概率(SRS/系统/分层/整群)=可推广 +误差可计算;非概率(便利/判断/配额/滚雪球)=不可外推。 ● 分层:层内同质、抽每一层、更精确。整群:群内异质、抽整个群、更便宜。 ● 均值的 n=(zo/E)2;比例的 n= p(1-p)(z/E)2;z: 95%→1. 96。向上取整;按组。 ● 抽样误差随 n 下降;非抽样误差不会 -- n(几乎)与 N无关。 ● 项目下限:n≥100 可用 -- 实地发放远多于此。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 7 . ANALYSIS & WHICH- TEST WEEK 7 . ANALYSIS & WHICH - TEST THE ANCHOR CHAPTER Data prep, descriptives & the which-test framework 数据准备、描述统计与“选哪种检验”框架 from a clean dataset to the right test - the exam's core skill[28]Source: asksia-cheatsheet-mktg90011.pdfPrimary = collected new for this question (surveys, interviews). Secondary = collected before for another purpose - cheaper/faster/bigger, but may be outdated or not fit. Internal secondary (CRM, sales invoices) vs external (ABS census, IBISWorld, Roy Morgan, Euromonitor, Google Scholar). Trade-off lens: Cost? Speed? Recency? Exclusivity? Secondary = cheaper/faster/bigger but may be outdated, ill-fitting, or carry hidden errors. Quant vs qual data: quantitative = values/codes; qualitative = open text/observation. You can quantify qual data by coding. Quant vs qual research: quantitative = deductive, confirmatory, structured (surveys, experiments) - tests theory; qualitative = inductive, exploratory, unstructured (interviews, observation) - builds theory. 7 . Questionnaire Design W4 Steps: what to ask > how to phrase -> organise > layout > pre-test (never skip) Avoid: leading, loaded, double-barreled ("pay AND conditions"), ambiguous, jargon, hard-to-recall, extreme words (all/always/never), socially-desirable invitations. 9b · Sample Size W6 . FX-82 ITEMS MEAN (INTERVAL/RATIO) n = ( z. o / E )2 PROPORTION (BINARY) n = p(1-p) . ( z / E )2 Critical z (two-tailed): 90% = 1. 65 . 95% = 1. 96 . 99% = 2. 58. 1 n reduces sampling error (diminishing returns); n is almost independent of population size . More data > reliability, not validity. Size recs are per group (2 groups -> double). Inflate for nonresponse. Need o (mean) or p (proportion) + confidence + margin E. At 95%/5% the calculator gives ~384 whether population is 10k or 1M. Need 100 usable at 10% response > approach ~1,000. 9c · Error & Cleaning W6-7 Sampling error (only-a-sample variation) vs non- sampling error (frame, nonresponse, measurement, data-entry). Sampling-frame error = the frame # the target population (phone book # all adults). Data prep: data must be correct + ready (coded numeric). Spot outliers via z-scores (|z| > 3) ; fix data- entry errors, keep a backup, record in Syntax. 1 n cuts sampling error only - non-sampling error (nonresponse, frame, bias) doesn't shrink with n. Worked z-checks: 0=5, E=1, 95% + (1. 96. 5/1)2 = 96 - n=97; p =. 2, E =. 05, 95% - Data prep, 2 goals: data are correct (no errors/missing/inconsistencies) and ready (coded numeric per question type). Analysis cannot fix bad data - clean first, log every fix in SPSS Syntax. 10 · Descriptive Stats W7 . PICK BY SCALE Choose the summary by scale type - a mandated part of the project's demographic profile. Centre + spread always travel together. SCALE CENTRE SPREAD
- Outlier 规则（数据清洗）
- 标准化值 $z=(x-\bar x)/s$，$|z|>3$ 标记为候选离群值；删前要调查，只有确认为录入错误才删，并在 Syntax 里记录可复现。[5]Source: asksia-bible-mktg90011-bilingual.pdf从一份干净的数据集到正确的检验 -- 考试的核心技能 TL;DR. Before any test you must (1) clean the data, (2) describe it with the right statistic for the scale type, then (3) pick the test by DV/IV scale x number of groups x paired-or-independent. That last step - which test for which question - is the make-or-break skill on the 50% final and the spine of the H1-H6 project. 一句话总结。任何检验之前你都必须(1)清洗数据,(2)用对应量表类型的正确统计量来描述它,再(3)按 DV/IV 量表 × 组数 × 配对还是独立 挑选检验。最后那一步 -- 哪个问题用哪个检验 -- 正是占50% 的期末考成败攸关的技能,也是 H1- H6 项目的脊梁。 ★ What the exam asks here 考试在这里问什么 The final is an on-campus digital LMS quiz (2h + 15min reading, lockdown browser), MCQ + long-answer, answer all. The single most repeated skill is Type C - choose the right test: you are handed a questionnaire + a hypothesis and must name the test, justify it by scale & group structure, then read the SPSS output and state the decision. Examinable = seminar notes + exercises + textbook Ch 1-7, 10. Build bring-in notes; confirm the exact permitted materials on your exam cover sheet. 期末考是一场校园内的数字化 LMS 测验(2小时+15分钟阅读,锁定浏览器),选择题+简答题,全部作答。被反复 考查最多的一项技能是 C类 -- 选对检验:你拿到一份问卷+一个假设,须命名检验、用量表与分组结构为其辩护,再 读 SPSS 输出并陈述决策。可考范围=研讨课笔记+练习+教材第1-7章、第10章。做一份带入笔记;在你的考试 封面页上确认确切的允许材料。 7. 1 Data preparation - clean before you compute 7. 1 数据准备 -- 先清洗,再计算 Analysis cannot rescue bad data. Preparation has two goals: data must be correct (no errors, no inconsistencies, missing values handled) and ready (coded into numeric form per question type). The standard pipeline: 分析无法挽救糟糕的数据。数据准备有两个目标:数据须正确(无错误、无矛盾、缺失值已处理)且就绪(按题型编码为数值 形式)。标准流程如下: 1 Editing - scan for impossible / inconsistent answers (an "age = 220", a "male" who answered the pregnancy filter). 编辑(Editing) - 扫查不可能/自相矛盾的回答(“年龄 =220”、一个回答了怀孕筛选题的“男性”)。 2 Coding - assign numbers to responses (e. g. Female=O, Male=1; Likert "Strongly agree"=5) so SPSS can read them. 编码(Coding) -- 给回答赋数字(例如 女=0、男=1;李克特“非常同意”=5),好让 SPSS 能读取。 AskSia Library · MKTG90011 · 双语 Bilingual 3 Cleaning & outliers - flag extreme values with z-scores: [z] > 3 is a likely outlier. Investigate before deleting; if it is a data-entry error, delete it but keep a backup and record the fix in the SPSS Syntax. 清洗与离群值 -- 用 z 分数标记极端值:|z|>3 是可能的离群值。删除前先调查;若确属录入错误,就删除它,但要保留备 份并在 SPSS Syntax 中记录这次修正。 4 Missing data - decide a treatment (exclude case-wise / pair-wise, or impute). Missing data is a non- sampling error. 缺失数据 -- 决定一种处理方式(逐案/逐对剔除,或插补)。缺失数据是一种非抽样误差。 OUTLIER RULE standardised value (z-score) : z = ( x - x ) / s > flag |z| > 3 as a candidate outlier SPSS · Analyze - Descriptive Statistics - Descriptives - tick "Save standardized values as variables" ! Delete an outlier without a reason 无正当理由就删除一个离群值 A value being large is not a reason to drop it - a genuine big spender is real data. Only delete when it is an error (impossible value, data-entry slip). Always keep the original file and log the edit in Syntax so the change is reproducible. 一个值大并不构成删除它的理由 -- 一个真实的大额消费者就是真实数据。只有当它是错误(不可能的值、录入失误) 时才删除。永远保留原始文件,并把改动记录在 Syntax 中,以使更改可复现。 AskSia Library . MKTG90011 . XXia Bilingual WEEK 7 . ANALYSIS & WHICH -TEST WEEK 7 . ANALYSIS & WHICH - TEST[28]Source: asksia-cheatsheet-mktg90011.pdfPrimary = collected new for this question (surveys, interviews). Secondary = collected before for another purpose - cheaper/faster/bigger, but may be outdated or not fit. Internal secondary (CRM, sales invoices) vs external (ABS census, IBISWorld, Roy Morgan, Euromonitor, Google Scholar). Trade-off lens: Cost? Speed? Recency? Exclusivity? Secondary = cheaper/faster/bigger but may be outdated, ill-fitting, or carry hidden errors. Quant vs qual data: quantitative = values/codes; qualitative = open text/observation. You can quantify qual data by coding. Quant vs qual research: quantitative = deductive, confirmatory, structured (surveys, experiments) - tests theory; qualitative = inductive, exploratory, unstructured (interviews, observation) - builds theory. 7 . Questionnaire Design W4 Steps: what to ask > how to phrase -> organise > layout > pre-test (never skip) Avoid: leading, loaded, double-barreled ("pay AND conditions"), ambiguous, jargon, hard-to-recall, extreme words (all/always/never), socially-desirable invitations. 9b · Sample Size W6 . FX-82 ITEMS MEAN (INTERVAL/RATIO) n = ( z. o / E )2 PROPORTION (BINARY) n = p(1-p) . ( z / E )2 Critical z (two-tailed): 90% = 1. 65 . 95% = 1. 96 . 99% = 2. 58. 1 n reduces sampling error (diminishing returns); n is almost independent of population size . More data > reliability, not validity. Size recs are per group (2 groups -> double). Inflate for nonresponse. Need o (mean) or p (proportion) + confidence + margin E. At 95%/5% the calculator gives ~384 whether population is 10k or 1M. Need 100 usable at 10% response > approach ~1,000. 9c · Error & Cleaning W6-7 Sampling error (only-a-sample variation) vs non- sampling error (frame, nonresponse, measurement, data-entry). Sampling-frame error = the frame # the target population (phone book # all adults). Data prep: data must be correct + ready (coded numeric). Spot outliers via z-scores (|z| > 3) ; fix data- entry errors, keep a backup, record in Syntax. 1 n cuts sampling error only - non-sampling error (nonresponse, frame, bias) doesn't shrink with n. Worked z-checks: 0=5, E=1, 95% + (1. 96. 5/1)2 = 96 - n=97; p =. 2, E =. 05, 95% - Data prep, 2 goals: data are correct (no errors/missing/inconsistencies) and ready (coded numeric per question type). Analysis cannot fix bad data - clean first, log every fix in SPSS Syntax. 10 · Descriptive Stats W7 . PICK BY SCALE Choose the summary by scale type - a mandated part of the project's demographic profile. Centre + spread always travel together. SCALE CENTRE SPREAD
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4）简答题（long-answer）“满分写法模板”（按材料的评分口径）
- 你答“选检验题”不要只写检验名字；材料明确说：只写检验名只能拿一小部分分。[8]Source: asksia-bible-mktg90011-bilingual.pdf对一道“选哪个检验”的简答题,写出三步路径(目的→量表→结构),说出检验名称,陈述需要检查的假设(Levene / 线性/ 期望频数),并给出APA 报告句式模板。仅说出检验名称只能拿到一小部分分数。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . WHICH-TEST RECAP PRACTICE BANK (CONT. ) Which-test recap - the one-screen table 选检验回顾 --- 屏一览表 Walk in with this memorised: goal - scale - structure - test 把这一条背进考场:目标→量表→ 结构→ 检验 TL;DR. If you internalise one table for the exam, make it this one. Match the research goal + the variable scales + the group/structure to the test, its key SPSS output, and the APA format. 一句话总结。若你为考试只内化一张表,就内化这一张。把研究目标+变量量表+组/结构 匹配到检验、它的关键 SPSS 输 出,以及APA 格式。 Test When (DV / IV scale . #groups . paired?) Key output to read Report format COMPARE GROUP MEANS (metric DV) One-sample t 1 metric variable vs a constant; 1 group t, df, Two-Sided p; Mean vs test value t(df) = _ , p = _ Independent t 1 metric DV · 2 independent groups Levene - row; then t, df, p; group means t(df) = _ , p = _ Paired t 2 metric vars . same subjects / pre-post Paired Differences t, df, p; both means t(df) = _ , p =_ One-way ANOVA 1 metric DV . 3+ groups . 1 factor Levene - F, df1, df2, Sig . ; post-hoc pairs F(df1, df2) = _ , p =
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你应该写成固定 6 格（每题照抄结构）
- (1) 目的：比较/关联/预测/降维（先 decode verb）[22]Source: asksia-cheatsheet-mktg90011.pdfW4 Predict a metric DV from 1 / 2+ IVs Bivariate / multiple regression R2/Adj R2; ANOVA Sig . ; B, Beta, Sig. Reduce many correlated items PCA (factor analysis) KMO, Bartlett; eigenvalue>1; loadings Profile 1 variable ("which is most . . . ", "how much spread") Descriptive (not a test) Mode / Median / Mean + SD by scale Scope: only these are taught. NOT examined - cluster, conjoint, MDS/perceptual maps, discriminant. 3b . The 4-Verb Guide DECODE THE VERB MATCH THE QUESTION'S VERB COMPARE means - t-test (2) / ANOVA (3+) ASSOCIATE + x2 (cat) / correlation (metric) PREDICT a DV - regression REDUCE items + PCA If the question says "differ / higher / vs" you are comparing means -> count the groups. "Related / linked / depends on" > associate. "Explain / predict / drives" -> regression. "most frequent / which is the . . . " (1 var) -> descriptive (mode), not a test. Paired vs independent hinges on dependence of observations : same people rated twice (pre/post, two Qs) = paired; two separate groups = independent. Identify this before you name the t-test type. COMPARE needs a metric DV + a grouping variable (count its groups: 2 -> t, 3+ -> ANOVA). ASSOCIATE needs two variables & no DV/IV split. PREDICT names a DV explained by IVs. REDUCE has many items, no DV at all. -- SIA > Decode the verb first, then count groups & check scale. The grader rewards the scale - #groups - paired? - test path written out, not a guessed test name. 4 . The 4 Scale Types W4 . DRIVES TEST CHOICE Scale type sets which stat & test are allowed identify it first, every time.
- (2) 变量 + 量表：逐个写清 N/O/I/R [11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重
- (3) 结构：几组？paired 还是 independent？[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
- (4) 检验名 + 为什么是它：把 “scale + #groups + paired?” 写出来（讲师奖励这个推理过程）[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
- (5) 假设检查（assumptions）：
- t/ANOVA：先看 Levene（选对行）[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual
- 卡方：期望频数稀疏（>20% cell expected <5 则先合并类别）[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
- 回归：线性、VIF<10 等（材料点名 VIF<10）[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
- (6) 读输出 → 结论：先 $p$ vs .05 决策，再说方向（均值谁更高 / $B$ 正负）。最后给一行 APA 句式。[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
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5）高频“送命陷阱”清单（考前必须背）
- 3+ 组别硬做 t-test：错；应该 ANOVA（否则第一类错误膨胀）。[11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
- 把 H2 和 H3 做成同一种 t-test：项目/评分要求两种不同类型（独立 + 配对各一）。[11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重[24]Source: asksia-cheatsheet-mktg90011.pdfH6 any technique free (corr / PCA / . . . ) Plus: descriptive demographic profile (means/medians/modes), ≥1 correlation, all assumptions checked, results with p-values + visuals. Built from a Qualtrics survey (n ≥ 100), analysed in SPSS. 24b . Project Pipeline MDP + DECK MDP -> RQs (+4P sub-Qs) -> lit review + secondary data -> qualitative (≥1 method) > thematic analysis > 25-Q Qualtrics survey > SPSS test of ≥6 hypotheses > report + 6-page deck. Quant floor n ≥ 100 before cleaning (≥15 interval/ratio items). Each hypothesis row: RQ + scale + justification linked to the qual findings. Every test's assumptions checked; results reported with p + a visual. H2 & H3 must be two different t-test types (one independent, one paired). H6 is free - typically a correlation or PCA. The grader's required toolkit: x2 . ≥2 t-types . ANOVA . multiple regression · ≥1 correlation. 25 . Which-Test Quick- Index DRILL THESE CUE TEST 2 categorical, "depends on" Chi-square 1 metric vs a number One-sample t metric, 2 different groups Independent t metric, same people ×2 Paired t
- Independent t / ANOVA 忘记先看 Levene，或选错行。[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[8]Source: asksia-bible-mktg90011-bilingual.pdf对一道“选哪个检验”的简答题,写出三步路径(目的→量表→结构),说出检验名称,陈述需要检查的假设(Levene / 线性/ 期望频数),并给出APA 报告句式模板。仅说出检验名称只能拿到一小部分分数。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . WHICH-TEST RECAP PRACTICE BANK (CONT. ) Which-test recap - the one-screen table 选检验回顾 --- 屏一览表 Walk in with this memorised: goal - scale - structure - test 把这一条背进考场:目标→量表→ 结构→ 检验 TL;DR. If you internalise one table for the exam, make it this one. Match the research goal + the variable scales + the group/structure to the test, its key SPSS output, and the APA format. 一句话总结。若你为考试只内化一张表,就内化这一张。把研究目标+变量量表+组/结构 匹配到检验、它的关键 SPSS 输 出,以及APA 格式。 Test When (DV / IV scale . #groups . paired?) Key output to read Report format COMPARE GROUP MEANS (metric DV) One-sample t 1 metric variable vs a constant; 1 group t, df, Two-Sided p; Mean vs test value t(df) = _ , p = _ Independent t 1 metric DV · 2 independent groups Levene - row; then t, df, p; group means t(df) = _ , p = _ Paired t 2 metric vars . same subjects / pre-post Paired Differences t, df, p; both means t(df) = _ , p =_ One-way ANOVA 1 metric DV . 3+ groups . 1 factor Levene - F, df1, df2, Sig . ; post-hoc pairs F(df1, df2) = _ , p =
- 卡方期望频数太小还硬解释：>20% cells expected <5 要先合并/重编码。[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
- Pearson 乱用在定序变量上：含定序 → Spearman；Pearson 只留给“干净 metric + 线性”。[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading[22]Source: asksia-cheatsheet-mktg90011.pdfW4 Predict a metric DV from 1 / 2+ IVs Bivariate / multiple regression R2/Adj R2; ANOVA Sig . ; B, Beta, Sig. Reduce many correlated items PCA (factor analysis) KMO, Bartlett; eigenvalue>1; loadings Profile 1 variable ("which is most . . . ", "how much spread") Descriptive (not a test) Mode / Median / Mean + SD by scale Scope: only these are taught. NOT examined - cluster, conjoint, MDS/perceptual maps, discriminant. 3b . The 4-Verb Guide DECODE THE VERB MATCH THE QUESTION'S VERB COMPARE means - t-test (2) / ANOVA (3+) ASSOCIATE + x2 (cat) / correlation (metric) PREDICT a DV - regression REDUCE items + PCA If the question says "differ / higher / vs" you are comparing means -> count the groups. "Related / linked / depends on" > associate. "Explain / predict / drives" -> regression. "most frequent / which is the . . . " (1 var) -> descriptive (mode), not a test. Paired vs independent hinges on dependence of observations : same people rated twice (pre/post, two Qs) = paired; two separate groups = independent. Identify this before you name the t-test type. COMPARE needs a metric DV + a grouping variable (count its groups: 2 -> t, 3+ -> ANOVA). ASSOCIATE needs two variables & no DV/IV split. PREDICT names a DV explained by IVs. REDUCE has many items, no DV at all. -- SIA > Decode the verb first, then count groups & check scale. The grader rewards the scale - #groups - paired? - test path written out, not a guessed test name. 4 . The 4 Scale Types W4 . DRIVES TEST CHOICE Scale type sets which stat & test are allowed identify it first, every time.[25]Source: asksia-cheatsheet-mktg90011.pdfχ² Spear. Spear. I/R x2 Spear. Pearson x2 = ≥1 nominal · Spearman = ≥1 ordinal (rank, non- normal) · Pearson = both interval/ratio & linear. Describe any association by presence · direction . strength . x2 gives presence + strength (C) but no direction; correlation gives all three. Association # causation - need an experiment for that. The diagonal is the easy case (same scale on both axes); off-diagonals fall to the weaker scale - any nominal forces x2; any ordinal (no nominal) forces Spearman. Pearson is reserved for two clean metric vars. 12 . Side-1 Recap CARRY TO SIDE 2 You now have the selection logic : verb > scale -> #groups -> test. Side 2 turns each test into a do-this block: WHEN . SPSS path . KEY OUTPUT . APA sentence, then the qualitative methods, research designs, and the project's H1-H6 test map. One-line scope check: if a question tempts you toward cluster, conjoint, MDS or discriminant - stop. They are not taught; the answer is always inside the eight-test toolkit above (+ descriptive). Same logic, two payoffs: the exam (50%) tests it as MCQ + long-answer, and the project (40%) hard-codes it as the H1-H6 map. Get the selection reflex automatic and you've covered 90% of the grade. SIA > In a "choose the test" item, write your reasoning: "region has 4 categories & satisfaction is interval ++ 3+ group means on a metric DV - one-way ANOVA; significant F - run Tukey for which pairs. " The lecturer rewards the path, not a guessed name. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 1/2 AskSia CHEAT SHEET SERIES 9 · Sampling Population (all of interest) -> sample (subset examined). Parameter describes the population (u); statistic describes the sample. Sampling frame = list of every element; frame # population is a non- sampling error. PROBABILITY NON-PROBABILITY
- 把相关当因果：材料明确说“never”；因果需要实验等。[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading[25]Source: asksia-cheatsheet-mktg90011.pdfχ² Spear. Spear. I/R x2 Spear. Pearson x2 = ≥1 nominal · Spearman = ≥1 ordinal (rank, non- normal) · Pearson = both interval/ratio & linear. Describe any association by presence · direction . strength . x2 gives presence + strength (C) but no direction; correlation gives all three. Association # causation - need an experiment for that. The diagonal is the easy case (same scale on both axes); off-diagonals fall to the weaker scale - any nominal forces x2; any ordinal (no nominal) forces Spearman. Pearson is reserved for two clean metric vars. 12 . Side-1 Recap CARRY TO SIDE 2 You now have the selection logic : verb > scale -> #groups -> test. Side 2 turns each test into a do-this block: WHEN . SPSS path . KEY OUTPUT . APA sentence, then the qualitative methods, research designs, and the project's H1-H6 test map. One-line scope check: if a question tempts you toward cluster, conjoint, MDS or discriminant - stop. They are not taught; the answer is always inside the eight-test toolkit above (+ descriptive). Same logic, two payoffs: the exam (50%) tests it as MCQ + long-answer, and the project (40%) hard-codes it as the H1-H6 map. Get the selection reflex automatic and you've covered 90% of the grade. SIA > In a "choose the test" item, write your reasoning: "region has 4 categories & satisfaction is interval ++ 3+ group means on a metric DV - one-way ANOVA; significant F - run Tukey for which pairs. " The lecturer rewards the path, not a guessed name. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 1/2 AskSia CHEAT SHEET SERIES 9 · Sampling Population (all of interest) -> sample (subset examined). Parameter describes the population (u); statistic describes the sample. Sampling frame = list of every element; frame # population is a non- sampling error. PROBABILITY NON-PROBABILITY
- 把定类变量当有均值（邮编、品牌这种不能算“平均值”）。[11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重
- 超纲选项当真答案：cluster / conjoint / MDS / discriminant 不教、常作为干扰项；看到就优先排除。[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading[25]Source: asksia-cheatsheet-mktg90011.pdfχ² Spear. Spear. I/R x2 Spear. Pearson x2 = ≥1 nominal · Spearman = ≥1 ordinal (rank, non- normal) · Pearson = both interval/ratio & linear. Describe any association by presence · direction . strength . x2 gives presence + strength (C) but no direction; correlation gives all three. Association # causation - need an experiment for that. The diagonal is the easy case (same scale on both axes); off-diagonals fall to the weaker scale - any nominal forces x2; any ordinal (no nominal) forces Spearman. Pearson is reserved for two clean metric vars. 12 . Side-1 Recap CARRY TO SIDE 2 You now have the selection logic : verb > scale -> #groups -> test. Side 2 turns each test into a do-this block: WHEN . SPSS path . KEY OUTPUT . APA sentence, then the qualitative methods, research designs, and the project's H1-H6 test map. One-line scope check: if a question tempts you toward cluster, conjoint, MDS or discriminant - stop. They are not taught; the answer is always inside the eight-test toolkit above (+ descriptive). Same logic, two payoffs: the exam (50%) tests it as MCQ + long-answer, and the project (40%) hard-codes it as the H1-H6 map. Get the selection reflex automatic and you've covered 90% of the grade. SIA > In a "choose the test" item, write your reasoning: "region has 4 categories & satisfaction is interval ++ 3+ group means on a metric DV - one-way ANOVA; significant F - run Tukey for which pairs. " The lecturer rewards the path, not a guessed name. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 1/2 AskSia CHEAT SHEET SERIES 9 · Sampling Population (all of interest) -> sample (subset examined). Parameter describes the population (u); statistic describes the sample. Sampling frame = list of every element; frame # population is a non- sampling error. PROBABILITY NON-PROBABILITY
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6）项目与期末共同的“固定检验地图”（背下来=两头通吃）
- 你材料给了项目强制映射：
- H1：chi-square
- H2：一个 t-test
- H3：另一个不同类型的 t-test
- H4：one-way ANOVA（metric DV across 3+ groups）
- H5：multiple regression（metric DV from 2+ predictors）
- H6：自由选择（比如相关/第二个回归或 ANOVA / PCA 等，但必须在课程范围内）[3]Source: asksia-bible-mktg90011-bilingual.pdf✓ The strategy this dictates 由此推导出的策略 One skill dominates: which test for which question. Drill the tree opposite until it is reflex - categorical × categorical - chi-square; one metric outcome over 2 groups - t-test (independent if separate people, paired if same/before-after); 3+ groups - one-way ANOVA; two metric variables - correlation (Pearson if linear&normal, Spearman if ordinal/non-normal); predict a metric outcome - regression; reduce many items - PCA. The project mandates this same map - H1 chi-square . H2 & H3 two different t-tests . H4 one-way ANOVA . H5 multiple regression . H6 any - so the work you do for one piece directly serves the other. 有一项技能压倒一切:哪个问题用哪个检验。把对页 那棵树练到成为本能 -- 类别×类别→ 卡方检验; 一个度量型结果跨 2组→t检验(不同的人则独立, 同一批/前后则配对);3+组→单因素方差分析;两 个度量型变量 →相关(线性且正态用皮尔逊,定序/ 非正态用斯皮尔曼);预测一个度量型结果 →回归; 缩减众多测量项→ PCA。项目强制同一张地图 -- H1 卡方 · H2 与 H3 两种不同的 t检验 · H4 单因素方差 分析 · H5 多元回归 · H6 任意 -- 所以你为其中一部 分所做的功直接服务于另一部分。 ★ The single highest-value hour 单位时间价值最高的那一小时 If you study one thing, study the which-test tree and the output-reading rule for each test (the statistic, the Sig. /p, the decision, the one-line APA sentence). Then build your permitted bring-in notes around it - after confirming the exact materials allowed on your exam cover sheet. 若你只学一样东西,就学选检验决策树和每个检验的 读输出规则(统计量、Sig. /p、决策、一句 APA 句)。然后围绕它搭建你的允许带入笔记 -- 在确认了 你的考试封面页上准许的确切材料之后。 AskSia Library · MKTG90011 · 双语 Bilingual CONTENTS CONTENTS The whole subject, in one ordered book 整门课,浓缩为一本有序之书 Twelve seminar weeks - one process-and-which-test map 十二周研讨课 →一张流程兼“选检验”地图 TL;DR. The book follows the unit's arc - build the method foundations (W1-6), then the statistical toolkit organised around test selection (W7-11), then turn it into marks with the project playbook, glossary and practice bank. 一句话总结。本书顺着本课的脉络展开 -- 先打牢方法基础(第1-6周),再围绕检验选择组织起统计工具箱(第 7-11 周),最后通过项目实战手册、术语表与练习题库把它转化为分数。 Ch Topic Core idea Methods foundations . Weeks 1-6 1 The research process 7 steps · symptom - problem . MDP vs MRQ → 2 Secondary data & hypotheses primary vs secondary . DV/IV . conceptual model → 3 Qualitative research interviews . focus groups . thematic analysis → 4 Measurement & scaling the 4 scale types . reliability & validity . a → 5 Research designs exploratory · descriptive . causal / experiments →[10]Source: asksia-bible-mktg90011-bilingual.pdf→ Every test: state the assumptions checked, the statistic, the p-value, the decision, a visual, and a plain-English line. 每个检验:说明检查过的假设、统计量、p值、决策、一张可视化图、以及一句大白话结论。 → Marks live in Quant (9) and Qualitative (7); mind the word caps; let qualitative themes generate the survey constructs. 分数所在是定量(9分)和定性(7分);注意字数上限;让定性主题去生成问卷的构念。 → 流水线:MDP→≤3个研究问题(+4P 子问题)→文献/二手数据→定性研究(≥1种方法,达到最低样本)→ 25 题 Qualtrics 问卷(n≥ 100)→ SPSS→报告+6 页幻灯片。 → 检验映射:H1 卡方 · H2 一个 t检验 · H3 一个不同的 t检验 · H4 单因素 ANOVA · H5 多元回归 · H6 任选 描述统计 +≥1个相关分析。 → 每个检验:说明检查过的假设、统计量、p值、决策、一张可视化图、以及一句大白话结论。 → 分数所在是定量(9分)和定性(7分);注意字数上限;让定性主题去生成问卷的构念。 AskSia Library · MKTG90011 · 双语 Bilingual GLOSSARY . 1/3 - GLOSSARY BILINGUAL · 双语词汇表 MKTG90011 in two languages MKTG90011,双语对照 Every core term, its X, and a one-line meaning - grouped by theme 每个核心术语、其中文,以及一行解释 -- 按主题归类 TL;DR. The exam rewards precise vocabulary - a wrong word ("accept HO", "mean of an ordinal scale") loses MCQ marks. This is the recall layer: ~ 95 terms across the unit, each with an accurate Chinese gloss. Cover the right two columns and define each term cold - recognition is not recall. 一句话总结。考试奖励精确的术语 -- 一个用错的词(“接受 HO”、“定序量表的均值”)会丢掉选择题分数。这是回忆层:全 课约 95个术语,每个配一个准确的中文释义。遮住右边两列,对每个术语冷启动地作答 -- 认得出不等于回忆得起。 ★ What the exam asks here 考试在这里问什么 Conceptual recall items (Type A) test exactly these definitions - what is a p-value? which descriptive fits an ordinal variable? what does Levene's test check? The test-selection items (Type C) depend on the scaling and test vocabulary below. If your bring-in notes are permitted, this table is the page to copy - confirm the exact permitted materials on your exam cover sheet first. 概念回忆题(A类)正是考查这些定义 -- 什么是p值?哪种描述统计适合定序变量?Levene 检验检查什么?选检验 题(C类)则依赖下面的量表与检验术语。如果允许携带带入小抄,这张表就是要抄的那一页 -- 请先在考试封面页上 确认确切允许携带的材料。 Term (EN) 中文 One-line meaning Process & research design · 流程与设计 Marketing research 市场调研 The information function linking consumer to marketer; defines, designs, collects, analyses, reports. Research process (7 steps) 调研流程七 步 Define problem - design - sample/collect - collect - analyse - report - follow- up. Marketing symptom[11]Source: asksia-bible-mktg90011-bilingual.pdfAskSia Library . MKTG90011 . XXi= Bilingual Hyp Mandated test Variable structure that forces it (neutral example) H4 one-way ANOVA metric DV across 3+ groups - mean loyalty by income tier (low/mid/high) H5 multiple regression metric DV from 2+ predictors - retention + loneliness + community use + age H6 any in-scope technique free choice - a correlation, a second regression/ANOVA, or PCA on the attitude battery Plus, regardless of H1-H6 — Descriptive profile demographic profile via means / medians / modes + a chart per variable — ≥1 correlation Pearson (two metric, linear) or Spearman (≥1 ordinal) discussed somewhere ✓ How to lock each hypothesis to its test 如何把每个假设锁定到它对应的检验 Run the 3-step algorithm on every hypothesis before you touch SPSS: (1) name the variables in the question; (2) label each scale N / O / I / R; (3) match purpose x scale x #groups. Write the justification straight into your hypothesis summary table (RQ . measurement scale . chosen test . link to a qualitative theme) - the grader is looking for the reasoning, not just the output. 在动 SPSS 之前,对每一个假设跑这套三步算法:(1)说出问题中的变量;(2)标注各自的量表 定类 N / 定序 O / 定距 | / 定比 R;(3)按 目的 ×量表 × 组数 匹配。把论证理由直接写进你的假设汇总表(研究问题 · 测量量表 · 所选检验 · 与某个定性主题的关联) -- 评分者看的是推理过程,而不只是输出结果。 ! Where projects lose the analysis marks 项目在哪里丢掉了分析部分的分 (1) Making H2 and H3 the same t-test type - the rubric demands two distinct types. (2) Running a t-test across 3+ groups instead of ANOVA (pairwise t-tests inflate the Type-I error). (3) Reporting a significant ANOVA but claiming which groups differ without a post-hoc. (4) Skipping the assumption checks (Levene's, normality, expected-cell counts) - every test needs them stated. (5) Treating a nominal variable (postcode, brand) as if it had a meaningful mean. (1)把 H2 和 H3 做成同一种 t检验 -- 评分标准要求两种不同类型。(2)对 3 组及以上用 t检验而非 ANOVA(两两 t 检验会放大第一类错误)。(3)报告了显著的 ANOVA,却在没有 post-hoc 事后检验的情况下宣称哪些组不同。(4)跳过 假设检查(Levene、正态性、期望单元格频数) -- 每个检验都需要明确陈述这些。(5)把定类变量(邮编、品牌)当 作有有意义均值的变量来处理。 AskSia Library . MKTG90011 . XXia Bilingual PROJECT . 40% PROJECT . 40% The report, the rubric & the craft 报告、评分标准与功夫 Where the marks live - and how to make qual + quant tell one story 分数藏在哪里 -- 以及如何让定性+定量讲出同一个故事 TL;DR. Marks cluster in the Quantitative Analysis (9) and Qualitative (7) sections. Stay under the word caps, report every test in APA-style sentences with a visual, and make the survey constructs come from the qualitative themes - that integration is the difference between a credit and a high distinction. 一句话总结。分数集中在定量分析(9分)与定性(7分)两部分。守住字数上限,每项检验都用 APA 式句子配可视化来报 告,并让问卷构念源自定性主题 -- 这种整合正是良(credit)与高分(high distinction)之间的分水岭。 3 Report structure & rubric weights 3报告结构与评分标准权重[24]Source: asksia-cheatsheet-mktg90011.pdfH6 any technique free (corr / PCA / . . . ) Plus: descriptive demographic profile (means/medians/modes), ≥1 correlation, all assumptions checked, results with p-values + visuals. Built from a Qualtrics survey (n ≥ 100), analysed in SPSS. 24b . Project Pipeline MDP + DECK MDP -> RQs (+4P sub-Qs) -> lit review + secondary data -> qualitative (≥1 method) > thematic analysis > 25-Q Qualtrics survey > SPSS test of ≥6 hypotheses > report + 6-page deck. Quant floor n ≥ 100 before cleaning (≥15 interval/ratio items). Each hypothesis row: RQ + scale + justification linked to the qual findings. Every test's assumptions checked; results reported with p + a visual. H2 & H3 must be two different t-test types (one independent, one paired). H6 is free - typically a correlation or PCA. The grader's required toolkit: x2 . ≥2 t-types . ANOVA . multiple regression · ≥1 correlation. 25 . Which-Test Quick- Index DRILL THESE CUE TEST 2 categorical, "depends on" Chi-square 1 metric vs a number One-sample t metric, 2 different groups Independent t metric, same people ×2 Paired t
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7）最后 3 天冲刺（最省命、最像“考前急救”）
- 第 1 天：只练“选检验 + 写理由”
- 每道题强制写：Goal → Scale → Structure → Test → Assumptions → $p$ 决策 → APA 句。[8]Source: asksia-bible-mktg90011-bilingual.pdf对一道“选哪个检验”的简答题,写出三步路径(目的→量表→结构),说出检验名称,陈述需要检查的假设(Levene / 线性/ 期望频数),并给出APA 报告句式模板。仅说出检验名称只能拿到一小部分分数。 AskSia Library · MKTG90011 · 双语 Bilingual PRACTICE . WHICH-TEST RECAP PRACTICE BANK (CONT. ) Which-test recap - the one-screen table 选检验回顾 --- 屏一览表 Walk in with this memorised: goal - scale - structure - test 把这一条背进考场:目标→量表→ 结构→ 检验 TL;DR. If you internalise one table for the exam, make it this one. Match the research goal + the variable scales + the group/structure to the test, its key SPSS output, and the APA format. 一句话总结。若你为考试只内化一张表,就内化这一张。把研究目标+变量量表+组/结构 匹配到检验、它的关键 SPSS 输 出,以及APA 格式。 Test When (DV / IV scale . #groups . paired?) Key output to read Report format COMPARE GROUP MEANS (metric DV) One-sample t 1 metric variable vs a constant; 1 group t, df, Two-Sided p; Mean vs test value t(df) = _ , p = _ Independent t 1 metric DV · 2 independent groups Levene - row; then t, df, p; group means t(df) = _ , p = _ Paired t 2 metric vars . same subjects / pre-post Paired Differences t, df, p; both means t(df) = _ , p =_ One-way ANOVA 1 metric DV . 3+ groups . 1 factor Levene - F, df1, df2, Sig . ; post-hoc pairs F(df1, df2) = _ , p =[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
- 第 2 天：读输出训练（把“读哪个格子”练成肌肉记忆）
- t/ANOVA：Levene → 正确那行 → t/F 和 Sig. → 均值方向 [4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
- 回归：Model Summary（$R^2$/Adj $R^2$）→ ANOVA（整体 F Sig.）→ Coefficients（$B$/Beta/Sig.）→ VIF [4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[15]Source: asksia-bible-mktg90011-bilingual.pdf✓ The walk-through the lecturer rewards 讲师奖励的那种推演过程 State your reasoning out loud in the long-answer: "Region has 4 categories (nominal) and satisfaction is interval - | am comparing 3+ group means on a metric DV - one-way ANOVA; a significant F means at least one region differs, then I run post-hoc (e. g. Tukey) to see which. " Naming the scale and the group count earns the marks even before you touch SPSS. 在简答题中把你的推理说出来:“区域有4个范畴(定类)而满意度是定距→我在比较一个度量型 DV 上的 3+ 组均值 →单因素方差分析;F 显著意味着至少有一个区域不同,然后我跑事后检验(如 Tukey)看是哪个。”在你还没碰 SPSS 之前,说出量表和组数就已经拿到了分数。 AskSia Library · MKTG90011 · 双语 Bilingual ★ Recall checklist 回忆清单 → Which regression: metric DV from 1 IV - bivariate, 2+ IVs - multiple. 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→多元。 → Read three tables in order: Model Summary (R2 / Adj R2) - ANOVA (omnibus F, Sig. ) - Coefficients (B, Beta, Sig. ). 按顺序读三张表:Model Summary (R2 / 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、Sig. )。 - B = equation slope (holding others constant); Beta = relative importance (largest |Betal = top driver, not for dummies); Sig. = per-IV t-test. B = 方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各自变 量的t检验。 - R2 = % of DV variance explained; use Adjusted R2 to compare models. I R2= 被解释的因变量方差占比;用调整 R2来比较模型。 → Assumptions: linearity, mean error 0, homoscedasticity, independent errors, (normal residuals); VIF < 10. 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → Categorical IV - (n-1) dummies; B = shift vs reference. 分类自变量 →(n-1)个虚拟变量;B=相对参照组的偏移。 → 选哪种回归:度量型因变量由1个自变量预测→一元,2个及以上→ 多元。 → 按顺序读三张表:Model Summary (R2/ 调整 R2)→ ANOVA (整体 F、Sig. )→ Coefficients (B、Beta、 Sig. ) . → B=方程斜率(其他变量不变时);Beta=相对重要性(|Betal 最大=头号驱动因素,虚拟变量不适用);Sig. = 各 自变量的 t检验。 → R2=被解释的因变量方差占比;用调整 R2来比较模型。 → 假设:线性、误差均值为 0、同方差、误差独立、(残差正态);VIF < 10。 → 分类自变量 →(n-1)个虚拟变量;B =相对参照组的偏移。 AskSia Library · MKTG90011 · 双语 Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Principal Component Analysis 主成分分析 (PCA) many correlated items - a few components 许多相关的题项 →少数几个成分 TL;DR. PCA is a data-reduction technique: it turns many correlated metric items into a few uncorrelated components, keeping as much variance as possible. It assumes the correlations are driven by an underlying latent factor. Decide whether the data suit PCA (KMO & Bartlett), how many components to keep (eigenvalue > 1 / scree), then name them from their loadings. 一句话总结。PCA(主成分分析)是一种数据缩减技术:它把许多相关的度量型测量项变成少数几个不相关的成分,并尽量保 留方差。它假定这些相关由一个潜在的潜变量因子驱动。先判断数据是否适合 PCA (KMO 与 Bartlett),再决定保留几个成 分(特征值>1/碎石图),最后依据载荷为它们命名。 ★ What the exam asks here 考试在这里问什么
- PCA：KMO/Bartlett → eigenvalue/scree → rotated loadings → 命名成分 [14]Source: asksia-bible-mktg90011-bilingual.pdfOn the 50% final, PCA shows up as read-the-output items: is the data suitable (KMO > . 50, Bartlett Sig. < . 05)? How many components (eigenvalue > 1 / the scree elbow)? Which items load where (Rotated Component Matrix)? And the "which technique" trigger: reduce many correlated items to underlying dimensions - PCA. It is a valid choice for project H6 and a fix for multicollinearity in H5. Out of scope: oblique/promax rotation, principal-axis factoring and confirmatory factor analysis are flagged "not in the exam. " 在 50% 期末考里,PCA 主成分分析以读输出的题型出现:数据是否适合(KMO >. 50,Bartlett Sig. <. 05)?保留 几个成分(特征值>1/碎石图的拐点(elbow))?哪些项目载荷在哪里(旋转后成分矩阵)?以及“选哪种技术”的触 发信号:把许多相关的项目压缩成潜在维度→PCA。它是项目 H6 的合法选项,也是 H5 中处理多重共线性的修正手 段。超出范围:斜交/promax旋转、主轴因子法、验证性因子分析都被标注为“不会出现在考试中”。 Step What you do Decision rule 1. Requirements Check sample size + correlations + suitability n > 10xk items; most r ≥. 30; KMO > . 50; Bartlett Sig. < . 05 2. Extract Project onto new orthogonal axes ordered by variance PC1 = most variance, PC2 next . . . (Principal components) 3. How many factors Keep the components worth keeping Kaiser: eigenvalue > 1; Scree: the elbow; cumulative % variance 4. Interpret Rotate & name each component Varimax rotation; assign each item to its highest loading; label it 5. Goodness-of-fit Do the factors reproduce the correlations? Communalities ≥. 3; few residual correlations > |. 05| AskSia Library · MKTG90011 · 双语 Bilingual KMO . BARTLETT . KEY QUANTITIES Suitability gate. KMO (Kaiser-Meyer-01kin) sampling adequacy - >. 90 marvellous . . 80 -. 90 meritorious . . 70 -. 80 middling . . 60 -. 70 mediocre . >. 50 minimum. Bartlett's Test of Sphericity - Ho: variables mutually uncorrelated; reject Ho (Sig. < . 05) - enough correlation to justify PCA. Eigenvalue = variance a component captures ; communality = variance of one item captured by the retained components; loading = correlation between an item and a component (-1 . . . +1) . AskSia Library . MKTG90011 . XXia Bilingual WEEK 11 . PCA & VISUALISATION - WEEK 11 . PCA & VISUALISATION Eigenvalues, the scree plot & loadings 特征值、碎石图与载荷 how many components, and what to call them 保留多少个成分,以及怎么给它们命名 TL;DR. Keep components with eigenvalue > 1 (Kaiser) and confirm with the scree elbow. Then read the Rotated Component Matrix (Varimax) - assign each item to the component on which it loads highest, and give that cluster an umbrella name.[9]Source: asksia-bible-mktg90011-bilingual.pdf→ PCA trigger: reduce many correlated metric items to a few uncorrelated components - not a hypothesis test. PCA 触发信号:把许多相关的度量型项目压缩成少数几个互不相关的成分 - 不是假设检验。 → Suitability: KMO > . 50; Bartlett Sig. < . 05 (reject Ho of no correlation). I 适合性:KMO>. 50; Bartlett Sig . <. 05(拒绝“无相关”的 Ho)。 → How many: eigenvalue > 1 (Kaiser) + scree elbow (keep one fewer than the elbow); report cumulative % variance. 保留几个:特征值>1(凯撒准则)+碎石图拐点(保留比拐点少一个);报告累计方差百分比。 - Interpret: Varimax -> Rotated Component Matrix; highest loading assigns the item; name each component; communality ≥ . 3. 解释:Varimax → 旋转后成分矩阵;最高载荷决定项目归属;为每个成分命名;共同度≥. 3。 → Visualisation: match chart to scale (bar/pie categorical, line metric, scatter two-metric, histogram/boxplot distribution); avoid 3-D pies & truncated axes. 可视化:图表匹配量表(条形/饼图用于分类、折线用于度量、散点用于双度量、直方/箱线用于分布);避免 3D 饼图和 截断坐标轴。 → PCA 触发信号:把许多相关的度量型项目压缩成少数几个互不相关的成分 -- 不是假设检验。 → 适合性:KMO>. 50; Bartlett Sig. <. 05 (拒绝“变量间无相关”的Ho)。 → 保留几个:特征值>1(凯撒准则)+碎石图拐点(保留比拐点少一个);报告累计方差百分比。 → 解释:Varimax→ 旋转后成分矩阵;最高载荷决定项目归属;为每个成分命名;共同度≥. 3。 → 可视化:图表匹配量表(条形/饼图用于分类、折线用于度量、散点用于双度量、直方/箱线用于分布);避免 3D 饼 图和截断坐标轴。 AskSia Library · MKTG90011 · 双语 Bilingual PROJECT . 40% PROJECT . 40% GROUP OF 4-5 . ~ 6,000 WORDS The research project playbook 研究项目实战手册 One real client, one team, eight deliverables - and a fixed test map that mirrors the exam 一个真实客户、一个团队、八项交付物 -- 以及一张与考试相互映照的固定检验地图 TL;DR. The project re-runs the marketing-research process end-to-end on a live client problem - the "loneliness economy" theme - and it forces a fixed sequence of six hypothesis tests (H1 chi-square - H6 free choice). Learn that map here and you have also learnt the exam's "which test" skill. 一句话总结。项目把市场营销研究流程从头到尾在一个真实客户问题上重跑一遍 -- “孤独经济”主题 -- 并强制一组固定顺 序的六项假设检验(H1 卡方→H6 自由选择)。在这里学会这张地图,你也就学会了考试的“选哪个检验”技能。 ★ What the exam asks here 考试在这里问什么 The project is examinable indirectly: the same H1-H6 test mapping is the model for the exam's signature "choose the right test" item (worth a large slice of the 50% final). If you can justify why H4 is a one-way ANOVA and not a t- test, you can answer the exam version of the same question. Memorise the map, then practise reading the variable structure that forces each test. 项目以间接方式被考查:同一套 H1-H6 检验映射正是考试招牌题“选对检验”的范本(在 50% 期末考中占很大比重)。 如果你能论证为什么 H4 是单因素 ANOVA 而非 t检验,你就能回答同一问题的考试版本。先记住这张映射表,再练习读 出迫使每个检验成立的变量结构。 1 The pipeline - one client problem, end to end 1流水线 -- 一个客户问题,从头到尾 PROJ 1 define the problem first; everything downstream serves the decision Problem definition
- 第 3 天：背“陷阱清单 + 超纲排除法 + 术语精确用词”
- 重点是不要被干扰项带走（cluster/conjoint/MDS/discriminant）。[4]Source: asksia-bible-mktg90011-bilingual.pdf! Out of scope - never the answer 超纲内容 -- 绝不会是正确答案 The unit does not teach cluster analysis, conjoint, MDS / perceptual maps, or discriminant analysis. If one of those appears as an MCQ option it is a distractor - eliminate it. Effect sizes (Cohen's d, n2) and nonparametric tests (Mann-Whitney etc. ) are SPSS menu items only; the unit stays parametric (Likert-as-interval). 本课程不讲授聚类分析、联合分析、多维标度(MDS)/感知图、判别分析。如果其中之一作为选择题选项出现,它就 是干扰项 -- 排除它。效应量(Cohen's d、n2)和非参数检验(Mann-Whitney 等)只是 SPSS 菜单项;本课程坚 持参数法(李克特按定距处理)。 ★ Recall checklist - tick before the exam 回忆清单 -- 考前逐项打勾 I can run the 3-step path (goal - scale - structure) on any prompt. 我能对任何题目跑三步路径(目的→量表→结构)。 I know paired vs independent hinges on dependence of observations. 我知道配对 vs 独立取决于观测的依赖性。 I - I read Levene first for independent-t and ANOVA, and pick the right row. I 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 - I apply ps . 05 + reject HO and then read means/coefficients for direction. 我应用 p≤. 05→拒绝 HO,然后读均值/系数判断方向。 - I can write the APA sentence for t / F / x2 / r / regression. 我能为 t/F/x2/r/回归写出 APA 报告句。 - I read R2 / Adj R2, omnibus F, B vs Beta, VIF <10 in a regression. I 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 - I check PCA gates: KMO>. 5, Bartlett <. 05, eigenvalue>1. 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 - I treat cluster / conjoint / MDS / discriminant as out-of-scope distractors. 我把聚类/联合/ MDS/判别当作超范围的干扰项。 I have built bring-in notes and will confirm permitted materials on the cover sheet. 我已做好带入小抄,并会在封面页上确认允许携带的材料。 → 我能对任何题目跑三步路径(目的→量表→结构)。 → 我知道配对vs独立取决于观测的依赖性。 → 对独立样本 t 和 ANOVA,我先看 Levene,并选对行。 → 我应用 p ≤. 05→ 拒绝 HO,然后读均值/系数判断方向。 → 我能为 t/F/x2/r/回归写出 APA 报告句。 → 在回归中我会读 R2/调整 R2、整体 F、B vs Beta、VIF<10。 → 我会检查 PCA 的门槛:KMO>. 5、Bartlett <. 05、特征值>1。 → 我把聚类/联合/ MDS/判别当作超范围的干扰项。 → 我已做好带入小抄,并会在封面页上确认允许携带的材料。 AskSia Library · MKTG90011 · 双语 Bilingual[16]Source: asksia-cheatsheet-mktg90011.pdf26 . Common Traps WHICH IS NOT · Ordinal mean - no "mean rank"; use median. · 3+ groups with t-tests - use ANOVA (Type-I inflation). · Pearson on ordinal - use Spearman; Pearson needs two clean metric vars. · Correlation = causation - never; need an experiment (correlation + antecedence + no third factor). · Independent vs paired - different people = independent; same people twice = paired. Don't mix them up. · x2 with sparse cells - don't interpret if > 20% of cells have expected < 5; merge/recode categories first. · Out-of-scope - cluster / conjoint / MDS / discriminant are NOT taught; don't propose them. 27 . Exam Discipline WIN THE MARKS · For every which-test item, write the path: scale > #groups -> paired? - > test. · For read-output items, the conclusion earns the mark: p vs a, then which mean is higher / sign of B. · Reports use means + t/F/r + p - effect sizes & nonparametric tests aren't drilled; don't over- answer. SIA + 50% of your grade is one skill: read the question's verb & the variables' scales, then name the test, run it, read the right cell, write the APA line. Drill that loop until it's automatic. asksia. ai/cheatsheet/ unimelb-mktg90011 · side 2/2 AskSia CHEAT SHEET SERIES confirm your current exam cover sheet & assessment . @ 2026 good luck. name the test. defined segment SIDE 2/2 THE TESTS, RUN & READ . Qualitative methods + thematic analysis . Research designs + experiments . x2 . t-tests . ANOVA . Correlation . Regression . PCA . H1-H6 project map . Output-reading
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你现在给我 2 个信息，我就能把它“再细化到逐题型清单 + 你专属复习计划”
- 你 Final 的日期是哪一天（具体日期）？（我好按还剩几天排每天刷什么）
- 你最怕的 3 块是哪 3 个（选即可）：
- A 选检验决策树（which-test）
- B t / ANOVA（尤其 Levene、post-hoc）
- C 卡方（期望频数、解释）
- D 相关（Pearson vs Spearman、如何解释）
- E 回归（读三张表、B vs Beta、VIF）
- F PCA（KMO/Bartlett、eigenvalue、载荷解读）
- （可选）如果你愿意，我也可以把你“最怕的三块 + 最常踩的陷阱”记住，以后你每次来复习我就直接按这个顺序带你冲刺。
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