How to Interpret SPSS Output Tables: Complete Beginner Guide for Medical Research

Understanding SPSS Output Structure

Every SPSS analysis lands in the Output Viewer — a separate window from your data, split into two panes. The left pane is an outline navigator listing every procedure you have run, in order, like a table of contents. The right pane holds the actual results: pivot tables, charts, and warning notes, stacked chronologically down the page. Before interpreting any single number, it helps to understand three structural habits SPSS follows in almost every output it generates.

Every Result Is a Pivot Table

SPSS output is built from "pivot tables" — grids with row and column headers that can be rearranged, but which follow a predictable internal logic. Nearly every inferential test produces at least two tables: a descriptive table (means, counts, percentages — what your data looks like) and a test table (the statistic, degrees of freedom, and significance — whether an observed pattern is likely to be real). Learning to separate "this table describes my sample" from "this table tests my hypothesis" is the first real skill in reading SPSS output.

Footnotes Are Not Optional Reading

Small superscript letters (a, b, c) next to a table title or a cell link to footnotes at the bottom of that table. These footnotes are where SPSS quietly reports assumption violations — the percentage of cells with expected count below 5 in a crosstab, which group was coded as the "positive" category in a logistic regression, or which post-hoc method was used. Researchers who skip footnotes routinely misreport results without realizing an assumption was violated.

The "Sig." Column Is Always Your p-Value

Across every procedure, the column labeled Sig., Sig. (2-tailed), Asymp. Sig. (2-sided), or Exact Sig. (2-sided) contains the p-value for that specific test. SPSS always rounds this figure to three decimal places and displays .000 whenever the true value is smaller than 0.0005. This is a display limitation, not a real zero — a p-value can approach zero but never equals it exactly.

Descriptive Statistics: Reading the Descriptives / Explore Table

Run via Analyze → Descriptive Statistics → Descriptives (or Explore for a version that also includes normality tests), this is usually the very first table generated in a medical thesis and forms the numeric backbone of Table 1.

What to Interpret, What to Ignore

Interpret the Mean and Std. Deviation together — never report a mean without its spread. Check Minimum and Maximum for impossible values (age = 0, SBP = 400) as a fast data-entry sanity check. If you requested Skewness and Kurtosis, use them only as a rough normality clue (values beyond ±1 to ±2 suggest non-normality), never as the formal decision — run Shapiro-Wilk via Explore for that. Ignore Std. Error Mean in your write-up; it exists to build confidence intervals, not to be reported on its own.

Frequency Tables for Categorical Variables

Run via Analyze → Descriptive Statistics → Frequencies, this produces a count-and-percentage breakdown for every category of a nominal or ordinal variable — sex, blood group, disease stage, treatment arm.

Crosstabs: Reading Two-Way Contingency Tables

Run via Analyze → Descriptive Statistics → Crosstabs, this table cross-classifies two categorical variables — for example, treatment group by clinical response. It is the descriptive table that sits underneath both the Chi-Square and Fisher's Exact tests covered further down.

Independent Samples T-Test Output

Use the independent t-test to compare a normally distributed continuous outcome between two separate, unrelated groups — for example, mean hemoglobin in males versus females. Run via Analyze → Compare Means → Independent-Samples T Test. SPSS produces two tables: Group Statistics (descriptives per group) and Independent Samples Test (the actual test).

What to Interpret, What to Ignore

Interpret Levene's Sig. only as a router — it tells you which of the two rows below it to read, it is never itself the result you report. Interpret the Mean Difference and its 95% CI alongside t, df, and Sig. — the mean difference and CI convey clinical magnitude, which the p-value alone cannot.

Paired Samples T-Test Output

Use the paired t-test for two related measurements on the same subjects — before and after treatment, pre- and post-intervention scores. Run via Analyze → Compare Means → Paired-Samples T Test. SPSS generates three tables, and the middle one is where most beginners go wrong.

One-Way ANOVA Output

Use one-way ANOVA to compare a normally distributed continuous outcome across three or more independent groups. Run via Analyze → Compare Means → One-Way ANOVA, requesting Tukey post-hoc (equal variances) or Games-Howell (unequal variances) under Post Hoc, and Homogeneity of Variance under Options.

What to Interpret, What to Ignore

Interpret F, df (between, within), and Sig. from the ANOVA table as the overall omnibus result. Ignore the raw Sum of Squares and Mean Square columns in your write-up — they are computation steps, not reportable statistics on their own. SPSS does not compute eta-squared (η²) automatically; calculate it as SS_between / SS_total from the Sum of Squares column, or use an effect size calculator.

Repeated Measures ANOVA Output

Use repeated measures ANOVA when the same continuous outcome is measured on the same subjects at three or more time points — for example, pain score at baseline, week 4, and week 8. Run via Analyze → General Linear Model → Repeated Measures. This is the output beginners most often misread, because it hides an extra assumption check that the independent-groups tests above do not have.

Chi-Square Test Output

Chi-Square tests association between two categorical variables and is generated alongside Crosstabs by checking Chi-square under Statistics. The output table, Chi-Square Tests, lists several rows — only one is usually correct for a given table.

Which Row to Read

Fisher's Exact Test Output

For a 2×2 table, Fisher's Exact Test appears as a row inside the same Chi-Square Tests table shown above — no separate procedure is needed. For tables larger than 2×2, request it explicitly via Exact... in the Crosstabs Statistics dialog (requires the Exact Tests add-on module in older SPSS versions; built in from SPSS 27 onward for most table sizes).

Pearson Correlation Output

Use Pearson correlation for the linear association between two normally distributed continuous variables. Run via Analyze → Correlate → Bivariate with Pearson checked. The output is a symmetric matrix — every variable pair appears twice, and the diagonal is always r = 1.000 (a variable perfectly correlates with itself).

Spearman Correlation Output

Use Spearman's rho (ρ) for ordinal variables, Likert-type items, or continuous variables that violate normality. Run the same Analyze → Correlate → Bivariate dialog, checking Spearman instead of (or alongside) Pearson.

Linear Regression Output

Use linear regression to predict a continuous outcome from one or more predictors. Run via Analyze → Regression → Linear. Three tables matter: Model Summary, the regression ANOVA table, and Coefficients.

What to Interpret, What to Ignore

Interpret Adjusted R² (not plain R²) as your headline "variance explained" figure once you have more than one predictor. Interpret unstandardized B when describing real-world clinical magnitude (e.g., "each additional year of age adds 0.71 mmHg"). Interpret standardized Beta only when comparing the relative strength of predictors measured in different units against each other. Ignore the Constant row's Sig. value in most clinical write-ups — it rarely has a meaningful clinical interpretation on its own. Check the Coefficients table's Tolerance/VIF columns (if requested) for multicollinearity — VIF above 5–10 is a warning sign.

Logistic Regression Output

Use binary logistic regression to predict a yes/no outcome from one or more predictors. Run via Analyze → Regression → Binary Logistic. This produces the most tables of any procedure in this guide — focus on four: Omnibus Tests, Model Summary, Hosmer-Lemeshow Test, and Variables in the Equation.

What to Interpret, What to Ignore

Interpret Exp(B) — this is the adjusted odds ratio, the number readers actually want. Ignore the raw B (log-odds) column in your write-up; it is a computational intermediate, not a clinically meaningful number on its own. Interpret Hosmer-Lemeshow Sig. the same way as a normality test: a non-significant result (p > 0.05) is the desired outcome, meaning the model's predicted probabilities match observed outcomes reasonably well. Interpret Nagelkerke R² as an approximate, not literal, "variance explained" figure — logistic regression has no exact equivalent of linear regression's R².

ROC Curve Analysis Output

Use ROC curve analysis to evaluate how well a continuous or ordinal test discriminates between two outcome states — for example, a biomarker predicting disease presence. Run via Analyze → ROC Curve. Two tables matter most: Area Under the Curve and Coordinates of the Curve.

Cronbach's Alpha (Reliability Analysis) Output

Use Cronbach's alpha to check internal consistency of a multi-item questionnaire scale before analyzing composite scores. Run via Analyze → Scale → Reliability Analysis, requesting Scale if item deleted under Statistics.

Common Interpretation Mistakes Across All SPSS Output

Complete SPSS Output Interpretation Checklist

Before writing a single sentence in your results section, work through this checklist for every table you plan to cite.