PSY1023 Chap.11 Quantitative Research Principles
Quantitative Research Principles
Introduction to Psychological Inquiry spends most of the semester on qualitative research, but Weeks 10 to 12 open a window onto the quantitative tradition. The Week 12 learning objectives ask for a basic understanding of quantitative methods, Week 11 practises decoding statistical results, and Week 10 compares how the two traditions report their methods and findings.
None of the assessment tasks requires you to run statistics; the value of this chapter lies in reading published evidence with confidence and in preparing for later psychology units.
The chapter begins with the logic behind numbers-based research.
Quantitative work is deductive: it starts from theory, turns ideas into measurable variables and tests whether data fit, an approach the unit traces to empiricism and positivism.
You will learn to separate the independent variable, the factor that groups or is manipulated, from the dependent variable, the outcome that is measured, and to use those two facts to choose between a t-test, an analysis of variance, a correlation and a regression.
A simple decision map shows how the type of predictor steers that choice, and why a categorical outcome needs a different family of tests.
Next comes reading results. The chapter decodes the standard APA result line, covering means and standard deviations, the test value with its degrees of freedom, and the p-value, and explains the convention that p below .05 counts as statistically significant.
It then pairs each quantitative starting reference with its design, sample and named analysis, so you can see why a repeated-measures design calls for one test and a group comparison of yes-or-no answers calls for another.
Those papers serve as reading material only; the unit asks that starting references stay out of your assessments, so treat them as models of design rather than sources to cite.
The final part turns critical. Five reading questions give any article a structure, and the strengths and limits the unit attaches to quantitative methods explain why interviews remain the right tool for your own project.
Worked interpretation of an invented result and practice items on choosing tests and decoding non-significant findings complete the chapter.
Every number used for practice is either quoted from a seminar or published study or clearly labelled as an invented illustration, and no formulas or new calculations are involved: the skill is reading values that researchers have already reported and saying, in careful prose, what they do and do not show.
What this chapter covers
- 01
Deductive logic, empiricism and positivism behind quantitative research
- 02
Independent and dependent variables and operationalisation
- 03
Choosing among t-test, ANOVA, correlation and regression
- 04
Reading an APA result line: M, SD, t(df) and p
- 05
Designs and analyses in the quantitative starting references
- 06
Five questions for reading an article critically
- 07
Strengths and limitations of quantitative methods
Worked example · free
Decode a paired-samples result and state its limits
- 1Name the design: the same people are measured on two occasions, so time is a within-subjects independent variable and concentration is a continuous dependent variable.
- 1Read the degrees of freedom: for a paired test they equal participants minus one, so t(29) signals 30 students.
- 1State the direction from the means: average concentration was higher after the phone-free week (M = 6.0) than before it (M = 5.1).
- 1Judge significance: p = .004 sits below the conventional .05 threshold, so the change is statistically significant.
- 2Limit the claim: with no comparison group, other changes during that week, such as a lighter workload or simply rating themselves twice, could explain the rise, so the result cannot show that removing phones caused it.
Key terms
- Independent Variable
- The factor a researcher manipulates or uses to form groups, treated as the possible influence on an outcome.
- Dependent Variable
- The measured outcome in a quantitative study, expected to change with the independent variable.
- Degrees of Freedom
- A number reported in brackets after a test value; in one-sample and paired t-tests it equals participants minus one.
- Statistical Significance
- A conventional verdict, usually applied when p falls below .05, that a result would be unlikely if no real effect existed.
- Operationalisation
- The translation of an abstract idea such as wellbeing into a specific measurable variable.
Quantitative Research Principles FAQ
Will I need to calculate statistics for any assessment task?
No. None of the three assessment tasks involves statistics. The Week 12 Moodle page describes the quantitative readings and videos as optional resources that may not help much with current assessments but should be useful in future psychology units.
What does t(11) mean in a reported result?
The t is the test statistic and the number in brackets is the degrees of freedom. For a one-sample or paired-samples t-test the degrees of freedom are the number of participants minus one, so t(11) indicates twelve people.
Does a non-significant result prove there is no effect?
No. A p-value above .05 means the study did not detect a difference at the conventional threshold. The sample may have been too small, which is why quantitative Method sections report an intended sample size from a power analysis.
How does a quantitative Method section differ from a qualitative one?
It operationalises ideas as independent and dependent variables, states inclusion and exclusion criteria and a planned sample size, and names each statistical test, the variables it uses, why it is run and the software, such as SPSS, R or JASP.
Which test compares the same people measured at several time points?
A repeated-measures design compares one group with itself over time. Bewick and colleagues (2010) used repeated-measures ANOVA to track student well-being across a degree; with only two occasions, a paired-samples t-test does the same job.
Assessment move
Treat this chapter as reading practice rather than calculation practice. Begin by learning the two questions that drive every test choice: what is the outcome, and what kind of variable is the predictor? Sketch the decision map from memory until you can place a t-test, an analysis of variance, a correlation and a regression without looking.
Then invent three short study descriptions from your own university life, such as comparing study habits across year levels or relating sleep to mood, and label the independent and dependent variables, their types and the test each one needs.
For results, copy any APA line you meet in a journal article and annotate it: underline the means, circle the degrees of freedom and work out the number of participants where the design allows, and write whether p clears .05. Finish each annotation with one sentence of what the result shows and one sentence of what it cannot show, especially about cause.
When you read a starting reference, apply the five reading questions in order and note the design, sample and named analysis in a single row, the same habit the unit encouraged with its research matrix.
Keep quantitative vocabulary out of your qualitative report: a small interview study describes patterns and meanings, so terms such as significant, representative or causal belong to the papers you read, not to your own write-up.
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