How to Solve and Format Statistics Assignments Using SPSS and R

To solve a statistics assignment in SPSS or R, work in four steps: match your research question and data type to the right test, check that test's assumptions, run it and read the key numbers from the output, then report the result in APA style with the test statistic, degrees of freedom, p value and an effect size. Most lost marks come from the first and last steps, not from the software.

This guide is practical SPSS assignment help with the R equivalents alongside, so you can follow it whichever package your module uses.

Step 1: Choose the right statistical test

Start with two questions: what are you trying to find out (a difference between groups, or a relationship between variables?) and what type of data do you have (categorical, or continuous interval/ratio?). If you are unsure about the difference between research designs, our guide to quantitative vs qualitative research covers the basics.

Step 2: Check the assumptions

Markers often award marks specifically for showing that you checked assumptions. The main ones are:

• Normality of the outcome (or of residuals in regression). Check histograms and Q-Q plots, and use the Shapiro-Wilk test with care: in large samples it flags trivial departures.
• Homogeneity of variance for t-tests and ANOVA, checked with Levene's test.
• Independence of observations, which comes from your study design rather than a test.
• Expected counts of at least 5 in most cells for chi-square.
• Linearity and homoscedasticity for correlation and regression, checked with scatterplots and residual plots.
• No extreme outliers, checked with boxplots.

If an assumption is violated, say so and explain what you did: used Welch's t-test, a non-parametric alternative or a transformation.

Step 3: Running tests in SPSS (menu paths)

SPSS is menu-driven, so describe the path in your method section if the brief asks for it. Menu names vary slightly between versions; recent versions label one menu "Compare Means and Proportions".

Click Paste instead of OK to save the syntax. Including syntax in an appendix makes your analysis reproducible, which markers appreciate.

Step 4: Running the same tests in R

In R, each analysis is usually one line. Assume your data frame is called df.

• Normality: shapiro.test(df$score)
• Levene's test (car package): car::leveneTest(score ~ group, data = df)
• Welch t-test: t.test(score ~ group, data = df)
• Student t-test (equal variances): t.test(score ~ group, data = df, var.equal = TRUE)
• Paired t-test: t.test(df$after, df$before, paired = TRUE)
• Effect size (effectsize package): effectsize::cohens_d(score ~ group, data = df)
• One-way ANOVA: model <- aov(score ~ group, data = df)
• ANOVA table: summary(model)
• Post hoc comparisons: TukeyHSD(model)
• Chi-square: chisq.test(table(df$gender, df$choice))
• Correlation: cor.test(df$hours, df$score, method = "pearson")
• Regression: summary(lm(score ~ hours + attendance, data = df))
• Mann-Whitney U: wilcox.test(score ~ group, data = df)

Two R details catch students out. First, t.test() runs Welch's test by default, which does not assume equal variances, so the degrees of freedom are often decimals. Second, for 2x2 tables chisq.test() applies Yates' continuity correction unless you set correct = FALSE. Say which version you used.

Step 5: SPSS output interpretation

SPSS produces a lot of tables. You usually need only a few numbers from each.

• Independent t-test: check the Levene's test columns first. If its p value is above .05, read the "Equal variances assumed" row; otherwise use "Equal variances not assumed" (Welch). Take t, df and the two-sided p. Recent versions also give an "Independent Samples Effect Sizes" table with Cohen's d.
• ANOVA: take F, both df values (between groups and within groups) and Sig. Then use the post hoc table to see which groups differ.
• Chi-square: use the Pearson Chi-Square row and check the footnote about expected counts below 5.
• Correlation: the matrix shows r, Sig. (2-tailed) and N for each pair.
• Regression: the Model Summary gives R²; the ANOVA table tests the overall model; the Coefficients table gives B, standard error, Beta and Sig. for each predictor.

"Sig." is SPSS's label for the p value. When SPSS shows .000, report it as p < .001, never p = .000.

How to report statistics in APA style

APA 7 style has a few consistent rules: italicise statistical symbols (t, F, r, p, M, SD), give degrees of freedom in brackets, round most values to two decimal places, report exact p values to two or three decimals (except p < .001), and drop the leading zero for values that cannot exceed 1, such as p and r. Always include an effect size.

Here are worked examples based on hypothetical data:

• Independent t-test: Students who used the revision app (M = 72.4, SD = 8.0) scored higher than those who did not (M = 67.1, SD = 8.2), t(48) = 2.31, p = .025, d = 0.65.
• One-way ANOVA: There was a significant effect of teaching method on test scores, F(2, 57) = 4.87, p = .011, η² = .15. Tukey post hoc tests showed the blended group scored higher than the lecture-only group (p = .009).
• Chi-square: There was a significant association between year of study and preferred study location, χ²(1, N = 120) = 5.21, p = .022, Cramér's V = .21.
• Correlation: Hours of revision were positively correlated with exam score, r(58) = .42, p < .001.
• Regression: Revision hours significantly predicted exam score, F(1, 98) = 21.5, p < .001, R² = .18.
• Mann-Whitney U: Satisfaction was higher in the online group (Mdn = 4) than the campus group (Mdn = 3), U = 210.5, z = -2.14, p = .032.

Always add a sentence interpreting the result in words. A d of 0.65 is a medium-to-large effect by Cohen's conventions; say what that means for your research question.

Struggling to decide between tests, or unsure what your output is telling you? Our statistics assignment help team gives step-by-step guidance and worked model answers to learn from.

Formatting tables and figures

APA tables and figures follow the same pattern:

1. A bold label on its own line (Table 1 or Figure 1).
2. A title in italic title case on the next line.
3. For tables: horizontal lines only (no vertical lines), clear column headings and consistent decimal places.
4. A note underneath for abbreviations, significance markers or data sources.
5. Mention every table and figure in the text before it appears ("As shown in Table 1...").

Don't paste raw SPSS output into the main text. Rebuild the key numbers in a clean table and put full output in an appendix if required. For figures, label both axes with units, include error bars where relevant and say what they represent (for example, 95% confidence intervals). In R, ggplot2 produces publication-quality charts with little effort.

Common mistakes in SPSS and R assignments

• Running a test before checking it suits the data type.
• Reporting p = .000 or leaving out degrees of freedom.
• Saying a result "proves" a hypothesis. Statistics provide evidence; they don't prove.
• Ignoring effect sizes, so a tiny but significant effect is overstated.
• Interpreting correlation as causation.
• Pasting screenshots of output instead of formatted tables.

Get support with statistics assignments

AssignmentHelpKit offers expert guidance on test selection, worked model answers in SPSS or R, and feedback on your write-up. If you have a dataset to explore, our data analysis service can walk you through the process; share your brief on WhatsApp for a free quote.

Frequently asked questions

Should I use SPSS or R for my statistics assignment?

Use the software your module specifies, because marks are sometimes awarded for showing the correct procedure or code. SPSS is easier to learn through menus; R is free, more flexible and better for reproducible analysis. The statistical results are the same if you run the same test with the same options.

How do I report a t-test in APA format?

Give the group means and standard deviations, then the test statistic with degrees of freedom, the exact p value and an effect size, for example t(48) = 2.31, p = .025, d = 0.65. Italicise the symbols and drop the leading zero for p. Finish with a sentence explaining what the result means.

What should I do if my data are not normally distributed?

First look at plots, because small departures matter less with larger samples. If the violation is serious, use the non-parametric alternative (for example Mann-Whitney U instead of an independent t-test) or a transformation, and explain your decision in the method or results section.

How do I run a t-test in R?

Use t.test(score ~ group, data = df), which runs Welch's t-test by default. Add var.equal = TRUE for the classic Student's t-test if Levene's test supports equal variances. Calculate Cohen's d separately, for example with the effectsize package.