SPSS & Excel Output Guide
A guide for Business, Psychology, and Nursing students on reporting statistical results in APA format.
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My first SPSS t-test was confusing. A wall of charts and numbers appeared—Levene’s Test, “Sig. (2-tailed),” “Mean Difference.” I had no idea what to do. My professor wanted a “Results” section, not a screenshot. Many students get stuck here. Running the test is easy; knowing what it means and how to report it is the real challenge.
Statistical interpretation is translating complex data output into a clear, concise, and meaningful narrative. For students in Nursing, Business, and Psychology, this is an essential skill. This guide is for students intimidated by that wall of numbers. We will break down common statistical outputs, show you what to look for, and provide templates to write your Results section in APA format. This is the core of our data analysis assignment help.
Core Statistical Concepts
When you look at any statistical output, you are looking for three key pieces of information. Instructors look for this in your research papers.
1. Statistical Significance (p-value)
This is the first thing to look for. The p-value (often “Sig.” in SPSS) is the probability your results occurred by chance. The common threshold is 0.05. If p < .05, your results are “statistically significant” (reject the null hypothesis). If p > .05, your results are “not significant” (accept the null hypothesis).
2. Effect Size (The Magnitude)
The p-value only tells you if there *is* an effect; the effect size tells you how *big* that effect is. A 2024 article on statistical significance stresses that p-values alone are not enough. Common effect sizes are Cohen’s d (for t-tests), eta squared (for ANOVA), and r (for correlations).
3. Direction and Descriptives
This is the “plain English” part. If you found a difference, which group was higher? You must look at the descriptive statistics (the mean scores) to explain the direction of the finding.
Reporting an Independent Samples T-Test
A t-test compares the means of two groups. (e.g., “Does the experimental group have higher test scores than the control group?”).
Reading the SPSS Output
You get two tables. The second, “Independent Samples Test,” is key. First, look at Levene’s Test for Equality of Variances. If “Sig.” is > .05, read the top row (“Equal variances assumed”). If < .05, read the bottom row.
| Levene’s Test | t-test for Equality of Means | |||||||
|---|---|---|---|---|---|---|---|---|
| F | Sig. | t | df | Sig. (2-tailed) | Mean Diff. | 95% CI | ||
| Test Score | Equal variances assumed | 1.22 | .273 | -2.54 | 98 | .013 | -4.50 | [-7.99, -1.01] |
| Equal variances not assumed | -2.51 | 92.4 | .014 | -4.50 | [-8.02, -0.98] | |||
APA 7th Edition Write-Up
Report the means (M) and standard deviations (SD) from the *first* table, and the t, df, and p-values from the *second* table.
An independent-samples t-test was conducted to compare [Dependent Variable] between [Group 1] and [Group 2]. Results indicated a significant difference between the [Group 1] (M = [mean], SD = [std dev]) and [Group 2] (M = [mean], SD = [std dev]) conditions, t([df]) = [t-value], p = [p-value].
Example: “Levene’s test was not significant (p = .273), so equal variances were assumed. An independent-samples t-test showed the experimental group (M = 82.5, SD = 3.1) scored significantly lower than the control group (M = 87.0, SD = 2.9), t(98) = -2.54, p = .013.”
Reporting a One-Way ANOVA
An ANOVA compares the means of three or more groups. (e.g., “Do test scores differ between Group A, B, and C?”).
Reading the SPSS Output
The “ANOVA” table tells you if there is a difference, not where. Look for the F-statistic, degrees of freedom (df), and the “Sig.” value.
| Sum of Squares | df | Mean Square | F | Sig. | |
|---|---|---|---|---|---|
| Between Groups | 88.1 | 2 | 44.05 | 4.72 | .011 |
| Within Groups | 803.4 | 86 | 9.34 | ||
| Total | 891.5 | 88 |
If “Sig.” is < .05, look at the "Post Hoc Tests" table (e.g., Tukey HSD) to see which specific groups differ.
APA 7th Edition Write-Up
A one-way ANOVA was conducted to examine the effect of [Independent Variable] on [Dependent Variable]. There was a statistically significant difference between groups, F([df_between], [df_within]) = [F-value], p = [p-value]. Post hoc tests using the Tukey HSD indicated that [Group A] (M = [mean], SD = [std dev]) was significantly [higher/lower] than [Group B] (p = [p-value]).
Example: “A one-way ANOVA revealed a significant effect of teaching method on test scores, F(2, 86) = 4.72, p = .011. A Tukey HSD post hoc test showed Group A’s scores (M = 90.1, SD = 2.5) were significantly higher than Group C’s (M = 85.2, SD = 2.8), p = .009.”
Reporting a Pearson Correlation
A correlation measures the statistical relationship between two continuous variables. (e.g., “Is there a relationship between hours studied and exam grade?”).
Reading the SPSS Output
In the “Correlations” matrix, look for three things where your variables intersect:
- Pearson Correlation (r): The strength and direction (+1 to -1).
- Sig. (2-tailed): The p-value.
- N: The sample size.
APA 7th Edition Write-Up
A Pearson correlation assessed the relationship between [Variable 1] and [Variable 2]. There was a [positive/negative], [strong/moderate/weak] correlation, r([N-2]) = [r-value], p = [p-value]. This indicates that as [Variable 1] increases, [Variable 2] tends to [increase/decrease].
Example: “A Pearson correlation revealed a strong, positive correlation between hours studied and final exam grade, r(128) = .65, p < .001. This indicates that students who studied more tended to achieve higher exam grades.”
Reporting a Linear Regression
A regression is used to predict an outcome from one or more variables. (e.g., “Can we predict a grade based on attendance and prior GPA?”).
Reading the SPSS Output
You’ll get several tables. Look at:
- Model Summary: Look at R Square. This is the percentage of variance your model explains (e.g., R Square = .45 means 45% of the variance in grades is explained).
- ANOVA: Look at the “Sig.” value. This tells you if your overall model is significant.
- Coefficients: The most important table. Look at “Sig.” to see which predictors are significant. Look at “B” (Unstandardized) or “Beta” (Standardized) for the direction and strength.
A 2024 article on regression models in health research provides background on their application.
APA 7th Edition Write-Up
A multiple linear regression was run to predict [Dependent Variable] from [Predictor 1] and [Predictor 2]. The overall model was significant, F([df_reg], [df_resid]) = [F-value], p = [p-value], and accounted for [R-Square]% of the variance (R² = [R²-value]). [Predictor 1] was a significant predictor (β = [Beta-value], p = [p-value]), while [Predictor 2] was not (β = [Beta-value], p = [p-value]).
Reporting a Chi-Square Test
A Chi-Square (χ²) test is for categorical data. It tells you if observed frequencies differ from what’s expected by chance. (e.g., “Are first-year students more likely to live on campus than seniors?”).
Reading the SPSS Output
Look at the “Chi-Square Tests” table. You need three numbers: The Pearson Chi-Square “Value,” the “df”, and the “Asymp. Sig. (2-sided)” (the p-value).
APA 7th Edition Write-Up
A chi-square test of independence examined the relationship between [Variable 1] and [Variable 2]. The relationship was [significant/not significant], χ²([df], N = [Sample Size]) = [Chi-Square Value], p = [p-value]. This indicates that [plain English explanation].
Example: “A chi-square test showed a significant relationship between class year and on-campus housing, χ²(1, N = 200) = 8.12, p = .004. First-year students were significantly more likely to live on campus than seniors.”
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Statistics FAQs
What does a p-value of .000 mean?
SPSS often outputs “.000,” but the probability is never zero. In APA format, you must report this as p < .001. Never write “p = .000.”
What is the null hypothesis?
The null hypothesis (H0) is the default assumption that there is no effect, no difference, or no relationship. Your goal is to collect enough evidence (a p-value < .05) to reject this null hypothesis.
T-test vs. ANOVA?
A t-test compares the means of two groups. A One-Way ANOVA compares the means of three or more groups.
R vs. R-Squared?
In a simple regression, R is the correlation coefficient (strength and direction). R-Squared (R²) is the coefficient of determination. It tells you the percentage of variance in the dependent variable that is “explained” by your independent variable(s).
Do I include the SPSS tables?
Never copy and paste a raw SPSS output table. You must re-create the important data in a clean, APA-formatted table. Refer to the APA’s guidelines on tables. Many professors will ask you to include your raw SPSS output in an Appendix.
What is “effect size”?
The p-value only tells you if a finding is statistically significant, not if it’s important. The effect size (like Cohen’s d or R²) tells you the magnitude or practical importance of your finding. APA 7 requires reporting effect sizes.
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