SPSS Tutorial: Statistical Analysis for Dissertations

By Dr. Robert K., PhD in Civil Engineering. Reviewed by Dr. Amanda R.. 2025-12-22.

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Key takeaways

• Set up your SPSS data file correctly before running any analyses
• Always start with descriptive statistics to understand your data
• Choose inferential tests based on your research questions and data types
• Check statistical assumptions before interpreting results
• Report results in APA format with effect sizes, not just p-values
• SPSS output contains more than you need, learn what to extract and report

Getting Started with SPSS

SPSS (Statistical Package for the Social Sciences) remains one of the most widely used statistical software packages in academic research, particularly in social sciences, education, health sciences, and business. If your dissertation involves quantitative data analysis, there's a good chance your program expects or requires SPSS.

SPSS uses a point-and-click interface that makes statistical analysis accessible without requiring programming knowledge, though its syntax editor allows for advanced customization. The software consists of two main windows: the Data View (where you enter and view your data, similar to a spreadsheet) and the Variable View (where you define your variables' properties).

Before running any analyses, you need to set up your data correctly. Define each variable with: a short name (no spaces), a longer descriptive label, the measurement type (nominal, ordinal, or scale), and value labels for categorical variables (e.g., 1 = Male, 2 = Female). Proper setup at this stage saves significant time later and ensures accurate analysis.

Descriptive Statistics in SPSS

Descriptive statistics summarize your data and are always the first step in any quantitative analysis. In SPSS, navigate to Analyze > Descriptive Statistics to access these tools.

For continuous variables (scale data), use Frequencies or Descriptives to obtain means, standard deviations, ranges, and distribution information. Check for normality using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. For categorical variables, use Frequencies to get counts and percentages.

Present your descriptive statistics in APA-formatted tables. For your dissertation, you'll typically need a demographics table (frequencies and percentages for categorical demographic variables) and a descriptives table (means, standard deviations, and ranges for your main study variables). These tables appear early in your results chapter and help readers understand your sample and data characteristics.

Common Inferential Tests in SPSS

The inferential test you choose depends on your research questions, data type, and number of groups. Here's a quick decision guide:

Comparing two group means: Independent samples t-test (Analyze > Compare Means > Independent Samples T-Test). Report t-statistic, degrees of freedom, p-value, and Cohen's d effect size.

Comparing three or more group means: One-way ANOVA (Analyze > Compare Means > One-Way ANOVA). Include post-hoc tests (Tukey or Bonferroni) to identify which groups differ. Report F-statistic, degrees of freedom, p-value, and partial eta squared.

Examining relationships between variables: Correlation (Analyze > Correlate > Bivariate). Use Pearson for continuous variables, Spearman for ordinal. Report r-value, p-value, and scatterplots.

Predicting an outcome from multiple predictors: Multiple regression (Analyze > Regression > Linear). Report R², adjusted R², F-test, and individual predictor coefficients (B, β, t, p).

Interpreting and Reporting SPSS Output

SPSS output can be overwhelming, it often provides far more information than you need to report. Learning what to look for and how to report it in APA format is essential.

For t-tests, report: t(df) = value, p = value, d = value. Example: 'Students who received tutoring scored significantly higher (M = 85.3, SD = 7.2) than those who did not (M = 78.1, SD = 8.5), t(98) = 4.87, p < .001, d = 0.91.'

For ANOVA, report: F(df between, df within) = value, p = value, η² = value. Follow with post-hoc results for significant findings.

For regression, report the overall model fit (R², F-test) and individual predictors in a table with B, SE, β, t, and p values. Discuss which predictors were significant and the direction of their effects.

Always check assumptions before interpreting results. For t-tests and ANOVA: normality and homogeneity of variance. For regression: linearity, normality of residuals, homoscedasticity, and absence of multicollinearity.