Correlation and regression benchmark project: a guide

Everything in this project follows from step 1, and step 1 has a sub-clause most students read past. You select at least three variables you believe have a linear relationship, and then — 1a — you specify how you will measure each one, naming the instrument and giving an APA reference for it. That is a real constraint. "Stress" measured by asking people to rate their stress out of ten is not an instrument with a reference; a published scale is. Choosing variables for which validated measures exist and are accessible to you is what makes steps 2 through 5 straightforward, and choosing variables you cannot properly measure is what makes them impossible. Decide the variables and the instruments in the same sitting, and treat an instrument you cannot find or cannot administer as a reason to change the variable.

Choose variables that are plausibly linearly related and that vary in your sample. A correlation cannot appear between things that barely differ across participants, and a relationship that is real but curved will produce a weak Pearson coefficient and a confusing regression. Think about direction too: the design asks you to identify predictor variables and a criterion variable, so decide in advance which variable you are treating as the outcome. That decision is conceptual rather than statistical — the software will run whatever you tell it — and the competency being assessed is scientific reasoning about psychological phenomena, not button-pressing. Write down the reason you expect each relationship before collecting anything; it is the difference between a hypothesis and a description of your output.

Step 2 asks you to describe the data collection technique, why it was appropriate, and why the sample size was best. The second and third clauses are the ones that get skipped. Appropriateness is an argument about fit between the method and the variables: self-report suits attitudes and internal states, observation suits behaviour, records suit anything already collected. Sample size in a student project is almost always a convenience decision, and the honest answer says so and then discusses what it costs — reduced power to detect a real relationship, and limits on generalising to anyone outside the sample. Claiming a small convenience sample was optimal is less impressive than explaining accurately why it was chosen and what it forecloses. The word best in the brief is a trap in that respect: best given the constraints is a defensible answer, best in principle almost never is.

Step 3 asks for the correlation coefficient for each possible pairing, with strength and direction described. Report all the pairs rather than only the interesting one, since the pattern across pairings is informative: two predictors that correlate strongly with each other are telling you something about your model before you build it. Describe strength and direction in words as well as numbers, and resist reading causation into any of it — a correlation between stress and drinking is compatible with several causal stories and the design distinguishes none of them. Say which alternative explanations remain open — reverse direction, a common cause, or both variables tracking something you did not measure — because naming them is exactly the scientific reasoning being assessed.

Steps 4 and 5 are the regression and its interpretation. Naming the predictor and criterion variables is required and should match the reasoning you set out earlier. When you report the model, give the equation, R squared, the significance of the overall model and the individual coefficients, and say what each coefficient means in the units of your variables — a slope is a statement about how much the outcome changes per unit change in the predictor, and translating it back into plain language is where interpretation actually happens. APA style has specific conventions for reporting statistics, and the brief asks for correct APA style explicitly here. Report non-significant results as fully as significant ones; a model that does not fit is a finding about your variables, and hiding it is both bad practice and easy for a marker to notice from the output you attach.

Two practical points. First, the SPSS data file must be submitted with the assignment, so keep the dataset clean and labelled as you build it rather than tidying it afterwards. Second, this is a benchmark assessed against a competency about interpreting psychological phenomena using scientific reasoning, which means the marker is reading for whether your conclusions are warranted by your design — a modest, well-qualified conclusion from a small convenience sample will score better than a confident one the data cannot support. Close by saying what you would do differently with more time or a larger sample, which is the natural place for the limitations to sit.