Correlations SPSS Exercises Assignment Guide

The graded output is not the SPSS output — it is your interpretation of it. The brief says so twice over: formulate an initial interpretation of the meaning or implication of your calculations, and compare your output against the tables in the supplied Output document. That comparison step is a check on whether you ran the analysis correctly, and it should take minutes; the interpretation is the assignment. Students who paste twelve tables and write three sentences have inverted the effort. Run the analysis, verify it matches, then spend the remaining time saying what each coefficient means for the variables it connects. Paste only the tables you actually discuss, since output included without comment reads as evidence you did not know which parts mattered. The word *initial* in the brief is worth noticing too, since it signals that a tentative reading with its uncertainties stated is what is wanted rather than a confident conclusion the data cannot support.

Choosing between Pearson and Spearman is the first real decision and it is decided by the data rather than by preference. Pearson's r assumes both variables are continuous, the relationship is linear and the distributions are reasonably normal; Spearman's rho works on ranks and survives ordinal data, non-normality and monotonic-but-not-linear relationships. Nursing research datasets are full of Likert scales and skewed counts, so the choice matters more here than in a textbook example. Say which you used and why in a sentence for each analysis, because an unexplained coefficient is one the marker cannot judge. Look at a scatterplot before choosing, since linearity is an assumption you can see and a correlation coefficient computed across a curved relationship will understate it badly. Say what you would have done had the assumptions failed, because a stated fallback shows the choice was made rather than defaulted to.

Interpreting a coefficient has three parts and students routinely report only one. **Direction** — does one variable rise as the other rises, and does that make substantive sense? **Strength** — the magnitude, described against a stated convention rather than adjectivally, and ideally with r-squared, since a correlation of 0.4 explains sixteen per cent of the variance and saying so is more informative than calling it moderate. **Significance** — whether the relationship is distinguishable from zero at the chosen alpha, which in a large sample can be true of a coefficient far too small to matter. Reporting all three, and noticing when significance and magnitude disagree, is what a good interpretation looks like. Report the sample size alongside every coefficient, because the same value of r means something quite different at n equals thirty and at n equals three hundred.

The causal warning is unavoidable in a correlation assignment and it is worth making precisely rather than ritually. Saying "correlation does not imply causation" and moving on earns little; saying what *else* could produce this particular association earns the mark. For any pair of variables in a health dataset there are usually three or four candidates — a confounder affecting both, reverse causation, selection into the sample, or a shared measurement artefact — and naming the plausible one for your specific variables demonstrates that you understand the principle rather than reciting it. The brief's own resources cover risk indexes and odds ratios alongside correlation, so where a relationship would be better expressed as a risk comparison than as a coefficient, saying so shows you know what the tool is for. Direction is also where a data-entry or coding error announces itself, so a coefficient whose sign contradicts everything you know about the variables is a reason to check the data rather than to write an ingenious explanation.

A few practical points about running the exercises. The dataset is supplied, so the variables and their coding are fixed — check the coding before interpreting, because a reverse-scored item will flip the sign of every correlation it appears in and produce an interpretation that is confidently backwards. Correlation matrices generate a great many coefficients at once and scanning them for whichever is largest is a form of fishing; work from the relationships the exercise asks about. Missing data handling changes the sample size between coefficients in the same matrix, which is worth noticing. Keep a note of the exact SPSS steps you took, since Part II usually builds on Part I and reproducing a result you cannot remember producing is the slowest part of this kind of assignment. Note the version of SPSS you used as well, since default handling of missing values and of significance reporting has changed between releases and a result that will not reproduce is hard to defend.

On mechanics the brief is procedural rather than formatting-heavy: complete the Part I and Part II steps as set out in the Week 6 Correlations Exercises page, use the Polit2SetB.sav dataset supplied, compare against the SPSS Output document supplied, and note that the assignment requires SPSS software rather than a spreadsheet. The Learning Resources name the specific chapters — bivariate description with crosstabulation and risk indexes, and correlation with simple regression — and reading those before running anything is faster than reading them afterwards to work out what the output means. Where your output does not match the supplied tables, say so and investigate rather than reporting the expected numbers, because a documented discrepancy is a legitimate finding and a silently corrected one is not. Reading them first also tells you which of the two chapters the exercise is drawing on, which narrows what you have to prepare rather than leaving both to be read in full.