APA-style statistical reporting is a core requirement in academic writing, especially in fields such as psychology, nursing, business, education, public health, and social sciences. Yet despite its importance, most students are never taught exactly how to convert SPSS output into clean, correct APA results.
The challenge is not running SPSS analyses; it is understanding which numbers matter, formatting them correctly, and explaining them in clear academic language.
This guide provides the foundation every student needs before writing results in APA format. You will learn:
- Why APA reporting is required
- Why students struggle
- What SPSS outputs mean
- APA symbols & definitions
- How to check assumptions BEFORE running tests
- The general rules APA requires for every statistical result
This is the starting point for understanding the structure and logic behind APA reporting.
Why APA Statistical Reporting Matters
APA style is not just an academic preference; it is the international reporting standard for communicating scientific findings clearly and consistently. When students report results in APA:
- Readers instantly understand the findings
- Research becomes comparable across studies
- Results remain transparent and replicable
- The work meets university and journal standards
- Complex SPSS output becomes easy to interpret
Most importantly, APA ensures that numbers are not left as raw statistics, but are turned into meaningful sentences that help the reader understand the study’s results.
Professors expect APA reporting because:
- it shows statistical understanding
- it demonstrates academic professionalism
- it ensures correct interpretation
- it reduces errors and miscommunication
Poor APA formatting, unclear reporting, or incorrect values are among the top reasons students lose marks in their research assignments, projects, theses, and dissertations.
This guide ensures your APA results will be clear, correct, and ready for submission.
Why Students Struggle With APA Reporting
Most students find APA statistical reporting difficult because it combines statistics, software interpretation, and academic writing. This section explains why it becomes overwhelming.
Table 1. Why APA Reporting Is Difficult for Students
| Problem Students Face | Why It Happens | Impact on Results |
|---|---|---|
| SPSS output is confusing | Too many tables & irrelevant values | Students report wrong numbers |
| APA rules are strict | Exact formatting required | Marks lost for minor errors |
| No training provided | Courses teach statistics but not APA writing | Students copy incorrect examples from online sources |
| Too many symbols | Students don’t know what each symbol means | Incorrect or incomplete reporting |
| No clarity on assumptions | Students skip assumption checks | Results become invalid or misleading |
| Difficult to interpret | Students list numbers without explanation | APA results lack meaning |
This guide solves all these problems with explanations, examples, and tables designed for clarity.
Essential APA Symbols & What They Mean
One major reason APA feels confusing is that students do not understand statistical symbols. This table solves that.
Table 2. APA Statistical Symbols and Their Meanings
| Symbol | Name | What It Means in SPSS | When It’s Used | Example APA Reporting |
|---|---|---|---|---|
| M | Mean | Average score | Descriptive statistics | M = 3.84 |
| SD | Standard Deviation | Spread of scores | Descriptive stats | SD = 0.71 |
| N | Sample Size | Number of participants | All reporting | N = 120 |
| t | t-Statistic | Test value for t-tests | Comparing 2 means | t(98) = 2.31 |
| F | F-Statistic | ANOVA test value | Comparing 3+ means | F(2, 87) = 4.56 |
| df | Degrees of Freedom | Value linked to sample size | All inferential tests | df = 98 |
| p | p-Value | Statistical significance | All tests | p = .012 |
| r | Correlation Coefficient | Strength of relationship | Correlation tests | r(118) = .46 |
| β | Beta Coefficient | Predictor strength | Regression | β = .32, p = .004 |
| η² | Eta-Squared | Effect size for ANOVA | ANOVA | η² = .09 |
| d | Cohen’s d | Effect size for t-tests | t-tests | d = 0.45 |
| χ² | Chi-Square | Test for categorical variables | Chi-square tests | χ²(2, N = 150) = 8.71 |
| R² | R-Squared | Variance explained | Regression | R² = .18 |
| CI | Confidence Interval | Range of expected values | Advanced reporting | 95% CI [3.12, 4.01] |
The Foundation of APA Reporting — Test Assumptions
Before writing ANY results in APA format, you MUST check whether the data meet the statistical assumptions of the test.
This determines whether:
- Your test is valid
- You should switch to a different test
- You must explain violations in APA writing
Table 3. Core Assumptions for SPSS Tests
| Test | Key Assumptions | Why They Matter | What Happens if Violated |
|---|---|---|---|
| t-test | Normality, Homogeneity of Variance, Independence | Ensures fair comparison between 2 groups | Must use nonparametric tests or corrected values |
| ANOVA | Normality, Equal Variances, Independence | Valid comparison across 3+ groups | Switch to Welch’s ANOVA or Kruskal–Wallis |
| Correlation | Linearity, Normal distribution | Ensures meaningful relationships | Correlation may be inaccurate |
| Regression | Linearity, Multicollinearity, Homoscedasticity, Normal residuals | Ensures valid predictions | β coefficients become unstable |
| Chi-Square | Expected frequencies > 5 | Ensures chi-square accuracy | Use Fisher’s Exact Test |
1. Normality
The normality assumption states that the dependent variable should follow a distribution that resembles a smooth, bell-shaped curve. In statistical terms, this means the data should be symmetrically distributed around the mean, with most observations falling near the center and fewer at the extremes. Normality is essential because tests like t-tests, ANOVA, correlation, and regression are built upon the mathematical assumption of symmetry. When data is heavily skewed, contains extreme outliers, or shows a distorted pattern, the resulting p-values may become inaccurate, leading to misleading conclusions.
SPSS provides multiple tools to evaluate whether a variable follows a normal distribution. The Shapiro–Wilk test is one of the most common tests for smaller samples; a nonsignificant result (p > .05) indicates that the distribution does not significantly deviate from normality. In addition to statistical tests, SPSS offers histograms, Q–Q (quantile–quantile) plots, and skewness/kurtosis values, all of which give visual clues about the shape of the distribution. Histograms should show a bell-shaped curve, while Q–Q plots should reveal points that closely follow a diagonal line.
APA Reporting Examples
- Normality satisfied: Shapiro–Wilk tests were nonsignificant, suggesting that the variable was approximately normally distributed.
- Normality violated: Shapiro–Wilk tests indicated a statistically significant deviation from normality; therefore, nonparametric alternatives were considered.
2. Homogeneity of Variance
The homogeneity of variance assumption is required when comparing two or more groups, particularly in independent samples t-tests and ANOVA. This assumption means that all groups being compared should display similar levels of variability. If one group has much larger or smaller variance than another, it introduces inequality that can bias the statistical test, inflating or deflating the likelihood of finding significance.
SPSS evaluates this assumption using Levene’s Test for Equality of Variances. If Levene’s test is not significant (p > .05), the assumption of equal variances is met, and standard parametric results may be reported. However, when Levene’s test is significant (p < .05), the assumption is violated. In such cases, SPSS provides adjusted results; for example, using Welch’s t-test or Welch’s ANOVA, which are more robust when variances differ.
APA Reporting Examples
- Assumption met: Levene’s test was nonsignificant (p = .48), supporting the assumption of equal variances.
- Assumption violated: Levene’s test indicated significantly different variances across groups (p = .02). As a result, Welch’s t-test was used.
3. Independence
The independence of observations assumption states that each participant’s response must be unrelated to the response of any other participant. This assumption is critical across nearly all statistical tests because dependent observations can artificially reduce variability, inflate significance, or distort real effects. Unlike the other assumptions, independence cannot be tested directly in SPSS; instead, it is established through proper study design.
For example, if a researcher collects data from students seated in pairs, and one student influences the other’s answers, this becomes a violation of independence. Similarly, repeated measures taken from the same participant without using a paired method would also break this assumption. Any violation invalidates the results and requires alternative statistical approaches, such as paired analyses or cluster-adjusted models.
3. Linearity
Linearity is one of the most important assumptions when using correlation or regression. It requires that the predictor and outcome variables follow a straight-line relationship. When this assumption is met, increases in one variable correspond to predictable increases (or decreases) in the other. If the relationship is curved or irregular, regression coefficients become misleading, and predictions lose accuracy.
SPSS allows students to check linearity through scatterplots, where the data points should form a roughly straight-line pattern. Partial regression plots can also be used when multiple predictors are included in the model. If the scatterplot reveals a nonlinear pattern, such as a curve, S-shape, or cluster, then the researcher may need to transform variables or use more advanced methods.
4. Homoscedasticity
Homoscedasticity refers to the assumption that regression residuals have consistent variance across all predicted values. In simpler terms, the “spread” of errors should be roughly equal from low to high predictions. When residuals widen or narrow into a funnel shape, this indicates heteroscedasticity, which can distort standard errors and lead to inaccurate statistical conclusions.
How to Report Descriptive Statistics in APA Format
Descriptive statistics provide a clear summary of your dataset before performing any inferential test. APA requires that you present descriptive results in a clean, structured format, usually including mean (M), standard deviation (SD), sample size (N), and sometimes minimum, maximum, and range.
SPSS produces rich descriptive output, but only a small portion belongs in APA reporting. Students often paste large SPSS tables into their assignments, which is incorrect. In APA style, you must create a polished table or write a narrative summary.
APA Table Example for Descriptive Statistics
Table 1
Descriptive Statistics for Job Satisfaction (N = 120)
| Variable | Mean (M) | SD | Min | Max |
|---|---|---|---|---|
| Job Satisfaction | 3.82 | 0.71 | 2.00 | 5.00 |
APA Narrative Example (Descriptive Statistics)
In APA format, descriptive statistics must also be incorporated into a narrative paragraph. For example:
Participants reported moderately high job satisfaction (M = 3.82, SD = 0.71). Scores ranged from 2.00 to 5.00, indicating that most participants rated their satisfaction above the midpoint of the scale.
Common Student Mistakes
Many students lose marks because they misunderstand how APA descriptive reporting works. One common error is copying the entire SPSS output table into the results section. APA style prohibits the use of raw SPSS tables, which often contain unnecessary statistics. Instead, you should summarize the results using only M, SD, N, and other essential values. Another frequent mistake is reporting too many decimal places; APA generally recommends rounding to two decimals for readability. Students also commonly list statistics without providing interpretation, but APA requires results to be meaningful rather than purely numerical. Finally, unclear variable names can confuse readers, so avoid using abbreviations or SPSS variable codes. Instead, rewrite them in plain, descriptive language.
How to Report t-Tests in APA Format
t-tests are widely used in academic research because they help determine whether observed differences between means are statistically meaningful. These tests answer practical questions such as whether male and female students differ in exam performance, whether a training program improved test scores, or whether one group performs significantly better than another. There are two main types of t-tests. The independent samples t-test compares two separate or unrelated groups (e.g., males vs. females), while the paired samples t-test compares repeated measures from the same participants (e.g., pre-test vs. post-test scores).
Independent Samples t-Test
An independent samples t-test is used when you want to compare the means of two distinct groups on a continuous outcome variable. For example, you might use this test to compare confidence levels between male and female students. Before conducting the analysis, you must ensure that the test assumptions such as normality and equal variances are satisfied. Once the analysis is complete, APA format requires you to report several components, including the group means, standard deviations, the t value, the degrees of freedom, the p value, and the effect size (Cohen’s d). These values collectively describe both the statistical significance and the practical importance of the difference.
What to Report in APA Format
A complete APA t-test report must contain all essential numerical components. You should begin by stating the mean and standard deviation for each group, followed by the results of the t-test itself. The statistical portion must include the exact t value, degrees of freedom in parentheses, and the p value. APA style also encourages reporting Cohen’s d, which indicates how large or meaningful the difference is. Finally, you should include a brief interpretation explaining what the numbers imply about your research question.
Independent Samples t-Test APA Table Example
Table 2
Self-Confidence Scores for Male and Female Students
| Group | N | M | SD |
|---|---|---|---|
| Male | 100 | 4.12 | 0.63 |
| Female | 100 | 3.84 | 0.71 |
APA Narrative Example (Independent Samples t-Test)
An independent samples t-test compared self-confidence scores for male and female students. Male students (M = 4.12, SD = 0.63) reported significantly higher confidence than female students (M = 3.84, SD = 0.71), t(198) = 2.43, p = .016, d = 0.34. This indicates a moderate gender difference in self-confidence levels.
Paired Samples t-Test
A paired samples t-test is used when the same group of participants is measured twice. This might include pre-training and post-training scores, or performance under two different conditions. Because the same participants contribute data at both time points, this test accounts for within-person variability. APA reporting for a paired t-test follows the same structure as the independent t-test, but emphasizes the comparison of two related measurements from the same participants.
APA Narrative Example (Paired Samples t-Test)
A paired samples t-test evaluated the effect of a training program on performance. Scores improved significantly from pre-test (M = 69.2, SD = 8.34) to post-test (M = 76.4, SD = 9.12), t(42) = 5.87, p < .001, d = 0.79. This indicates a large positive effect of the training intervention.
Common APA Mistakes in t-Test Reporting
A typical mistake is forgetting to include degrees of freedom, which are required in APA format and must appear directly after the t statistic. Students also incorrectly write p = .000, but APA rules prohibit this because a p-value can never be exactly zero; instead, it must be reported as p < .001. Another mistake is omitting effect size, even though APA strongly recommends including Cohen’s d for all t-tests. Finally, many students fail to identify which groups were compared, leaving readers unsure about the context of the results. Clear group identification is essential for correct interpretation.
How to Report Correlation in APA Format
Correlation analysis is used to examine whether two continuous variables are related and how strong that relationship is. Common examples include correlations between height and weight, hours spent studying and exam performance, or stress levels and burnout. The most frequently used statistic is the Pearson correlation coefficient (r), which ranges from -1 to +1. Positive values indicate that as one variable increases, the other also increases, while negative values indicate the opposite. In APA format, correlation results must include the value of r, the p-value, the direction of the relationship, and an interpretation of its strength.
Strength is typically described as very weak, weak, moderate, strong, or very strong. These terms should be based on the actual numerical value of r, not subjective impressions. Students are encouraged to follow established guidelines when interpreting correlation strength.
Correlation APA Table Example
Table 3
Correlation Between Stress and Burnout
| Variable | Burnout |
|---|---|
| Stress | .46*** |
APA Narrative Example (Correlation)
A Pearson correlation revealed a moderate positive association between stress and burnout, r(118) = .46, p < .001. Higher stress levels were associated with higher reported burnout among participants.
Common Correlation Errors
Students often describe correlations as “strong” or “weak” without reporting the corresponding numerical value, which makes the statement subjective and unclear. Always pair verbal descriptions with the actual r value. Another common error is reporting p-values incorrectly — p = .000 must never appear in APA formatting. Students also forget to describe whether the relationship is positive or negative, leaving readers without directionality. Finally, correlation results must include interpretation; listing only numbers does not fulfill APA requirements.
How to Choose Between t-Tests and Correlation
Students sometimes struggle to decide which statistical test is appropriate for their research question. The distinction is straightforward when you consider the purpose of each method. If your goal is to compare two groups, such as males and females or experimental and control groups, then a t-test is appropriate. If the same group is measured twice, the paired t-test is the correct choice. However, if you are examining the relationship between two continuous variables, such as stress and burnout or hours studied and exam score, then a correlation is the correct analysis. Understanding this difference helps prevent methodological errors and ensures accurate APA reporting.
How to Report ANOVA in APA Format (One-Way & Post Hoc)
Analysis of Variance (ANOVA) is used when comparing three or more groups on a continuous variable. While a t-test compares only two groups, ANOVA evaluates whether the mean differences among multiple groups are statistically significant. For example, a researcher may compare exam performance across three teaching methods, stress levels across income categories, or reaction times across age groups. ANOVA determines whether at least one group mean differs from the others, but it does not immediately show which groups differ, which requires post hoc tests.
In APA format, reporting ANOVA involves describing the F-value, degrees of freedom, p-value, and effect size (usually η² or partial η²). These elements show the magnitude and significance of group differences. Before reporting, it is important to verify assumptions such as normality and homogeneity of variance; unequal variances may require using Welch’s ANOVA instead of the standard one-way ANOVA.
One-Way ANOVA — APA Narrative Reporting Example
A one-way ANOVA was conducted to compare anxiety levels across three teaching methods. There was a significant effect of teaching method on anxiety, F(2, 117) = 4.83, p = .010, η² = .08. Post hoc comparisons using Tukey’s HSD revealed that students in the interactive group (M = 2.68, SD = 0.75) reported significantly lower anxiety than those in the online learning group (M = 3.41, SD = 0.91), p = .008. No other group differences were significant.
Common APA Errors in ANOVA
Students often report ANOVA results without including effect size, but APA requires effect size to contextualize the magnitude of the difference. Another frequent mistake is reporting ANOVA results without specifying which post hoc test was used. Some students also forget to report degrees of freedom, which must appear in parentheses immediately following the F statistic. Finally, many forget to clarify which group differences were statistically significant, leaving ANOVA results incomplete.
How to Report Regression in APA Format
Regression analysis examines how one or more predictor variables explain variation in an outcome variable. Unlike correlation, which measures association, regression allows prediction and evaluation of individual predictors. In APA format, regression reporting requires presenting the model summary, unstandardized coefficients (B), standardized coefficients (β), t-values, p-values, and R², which represents how much variance in the outcome is explained by the predictors.
There are two main forms of regression:
- Simple Regression: one predictor predicting an outcome
- Multiple Regression: two or more predictors predicting an outcome
Before running regression, assumptions such as multicollinearity, homoscedasticity, linearity, and residual normality must be examined to ensure validity.
APA Narrative Example (Multiple Regression)
A multiple regression was performed to examine whether motivation, stress, and years of experience predicted job performance. The overall model was significant, F(3, 146) = 19.84, p < .001, and explained 29% of the variance in performance (R² = .29). Motivation positively predicted job performance (β = .44, p < .001), while stress negatively predicted performance (β = −.27, p = .006). Experience did not significantly predict job performance (β = .10, p = .091). These findings indicate that emotional and motivational factors play a stronger role in predicting performance than work tenure.
Common APA Regression Errors
Many students mistakenly report only standardized coefficients (β) without providing unstandardized coefficients (B), which are required for reproducibility. Others fail to include the R² value, making it difficult to interpret model strength. Forgetting to specify whether the regression was simple or multiple is another common issue. Additionally, some students list only the significant predictors and ignore nonsignificant ones, but APA requires reporting every predictor included in the model.
How to Report Logistic Regression in APA Format
Logistic regression is used when the outcome variable is categorical, typically binary (e.g., pass/fail, yes/no, employed/unemployed). Instead of predicting a continuous value, logistic regression predicts the odds of an event occurring. This model uses odds ratios (OR), coefficients (B), and significance values to show how predictors influence the likelihood of an outcome.
APA reporting for logistic regression involves presenting the χ² model test, Nagelkerke R², and odds ratios with confidence intervals. Unlike linear regression, standardized coefficients are not typically used.
APA Narrative Example
A binary logistic regression was conducted to determine whether study hours, stress levels, and attendance predicted the likelihood of passing an exam. The model was significant, χ²(3) = 22.41, p < .001, and explained 27% of the variance (Nagelkerke R² = .27). Study hours significantly increased the odds of passing (OR = 1.86, 95% CI [1.28, 2.71], p < .001), while stress levels significantly decreased the odds (OR = 0.68, 95% CI [0.52, 0.90], p = .007). Attendance did not significantly predict exam outcomes.
How to Report Chi-Square Tests in APA Format
Chi-Square analysis is used to examine relationships between categorical variables. It determines whether distributions of frequencies differ from what would be expected by chance. For instance, you might analyze whether gender is associated with voting preference or whether region is associated with satisfaction levels.
APA reporting for chi-square requires including the χ² statistic, degrees of freedom, p-value, and a clear interpretation of the association. If expected frequencies are too small, Fisher’s Exact Test may be required.
APA Narrative Example
A chi-square test of independence examined the association between gender and course passing rates. The association was not statistically significant, χ²(1) = 2.14, p = .144, indicating that gender and pass/fail outcomes were independent of each other.
Common APA Chi-Square Errors
Students often forget to mention the test of independence and simply report the χ² value, leaving the purpose unclear. Others fail to present the contingency table, which is essential for interpreting the distribution of frequencies. Another frequent mistake is using chi-square when expected cell counts are too low; APA requires acknowledging if Fisher’s Exact Test was used instead.
How to Report Reliability Analysis in APA Format (Cronbach’s Alpha)
Reliability analysis evaluates the internal consistency of items that are intended to measure the same underlying construct. In most undergraduate and graduate research, the preferred measure of reliability is Cronbach’s alpha (α). This statistic determines how well a set of items “hang together” as a scale. High reliability indicates that items measure the same concept consistently across respondents.
In APA reporting, Cronbach’s alpha should be presented as a single value between 0 and 1. Values above 0.70 are considered acceptable, values above 0.80 are good, and values above 0.90 are excellent. However, extremely high values (e.g., above .95) may suggest redundancy among items, meaning the items may be too similar.
Before reporting reliability, researchers often conduct item-level checks. These include examining item-total correlations, alpha-if-item-deleted, and descriptive statistics for each item. SPSS produces these automatically, but APA style requires summarizing them in narrative form rather than copying raw tables.
Reliability APA Table Example
Table 8
Internal Consistency for the Five-Item Stress Scale
| Scale | Number of Items | Cronbach’s α |
|---|---|---|
| Stress Scale | 5 | .84 |
APA Narrative Example (Cronbach’s Alpha)
The five-item stress scale demonstrated good internal consistency, with a Cronbach’s alpha of .84. Item-total correlations indicated that all items contributed meaningfully to the scale, and no improvements in reliability were observed if any item was removed.
Common APA Reliability Errors
Students often misreport alpha by writing “0.840” or even “84%,” which is incorrect in APA style. Alpha must always be reported as α = .84, using two decimals. Another common error is failing to mention how many items were included in the scale or what construct the scale measures. Finally, some students copy full SPSS reliability tables directly into their assignments; APA requires summarizing results, not pasting raw output.
How to Report Factor Analysis in APA Format (EFA & PCA)
Factor analysis is used to understand the underlying structure of a set of variables. It identifies clusters of items that group together (factors), which helps determine whether survey items measure distinct constructs. Students commonly use Exploratory Factor Analysis (EFA) or Principal Component Analysis (PCA) for scale development, validation, or data reduction.
When reporting factor analysis in APA format, it is essential to include:
- The extraction method (e.g., principal axis factoring, PCA)
- Rotation method (e.g., varimax, oblimin)
- Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy
- Bartlett’s test of sphericity
- The number of factors extracted
- Eigenvalues and variance explained
- Factor loadings
If rotation improves interpretability, the rotated factor matrix should be summarized in a table with clean, high-loading items.
APA Narrative Example (EFA/PCA)
An exploratory factor analysis using principal axis factoring with oblimin rotation was conducted to assess the structure of the motivation scale. Sampling adequacy was confirmed with a KMO value of .81, and Bartlett’s test of sphericity was significant (χ²(10) = 214.63, p < .001), indicating that the data were suitable for factor analysis. Two factors were extracted based on eigenvalues greater than 1, accounting for 62% of the total variance. The rotated factor solution revealed clear loadings, with three items loading strongly on Factor 1 (Intrinsic Motivation) and two items loading strongly on Factor 2 (Teamwork Motivation).
Common APA Factor Analysis Errors
One typical error is failing to report diagnostic statistics such as KMO and Bartlett’s test, which are essential for justifying factor analysis. Students also frequently misinterpret factor loadings, assuming that cross-loadings automatically invalidate an item. In APA style, cross-loadings should be discussed, not ignored. Another error is listing factor loadings without explaining what each factor represents. In academic writing, factors must be named based on conceptual meaning.
Special & Advanced Tests Students Often Report in APA
Graduate students, especially in psychology, business, education, public health, and social sciences, often encounter more advanced tests. Below are APA-style explanations for several commonly used analyses.
1. MANOVA (Multivariate Analysis of Variance)
MANOVA evaluates whether groups differ on multiple dependent variables simultaneously. APA reporting requires including the multivariate test statistic (Wilks’ Lambda, Pillai’s Trace, etc.), F-value, degrees of freedom, and significance.
APA Example
A MANOVA examined whether teaching method affected both confidence and engagement. The multivariate effect was significant using Wilks’ Λ, Λ = .87, F(4, 232) = 4.12, p = .003, partial η² = .07.
2. ANCOVA (Analysis of Covariance)
ANCOVA compares group means while controlling for covariates.
APA Example
After controlling for prior GPA, an ANCOVA revealed a significant effect of study method on final exam scores, F(2, 144) = 6.40, p = .002, partial η² = .08.
3. Mediation Analysis (PROCESS or Regression-Based)
APA reporting for mediation requires:
- effect of X on M
- effect of M on Y
- indirect effect with bootstrapped CI
APA Example
A mediation analysis revealed that motivation mediated the relationship between stress and job performance. The indirect effect was significant (β = .14, SE = .05), 95% CI [.05, .26], indicating that lower stress improves motivation, which in turn increases performance.
4. Moderation Analysis
Moderation tests whether the effect of one predictor depends on another variable.
APA Example
The interaction between stress and social support was significant, β = −.19, p = .014, suggesting that the negative effect of stress on performance was weaker for individuals with high social support.
5. Nonparametric Tests
Nonparametric tests are used when assumptions such as normality are violated.
Examples (APA format):
- Mann–Whitney U Test: A Mann–Whitney U test indicated that satisfaction differed significantly between groups, U = 412.00, p = .021.
- Wilcoxon Signed Rank Test: Scores improved significantly from pre-test to post-test, Z = −3.12, p = .002.
- Kruskal–Wallis Test: A Kruskal–Wallis test showed significant differences in stress across income groups, χ²(2) = 9.44, p = .009.
Full APA Templates for Students
Below are polished templates you can paste directly into your assignment.
Template: t-Test (Independent)
An independent samples t-test compared [variable] for [Group 1] and [Group 2]. Results showed that [Group 1] (M = xx, SD = xx) scored [higher/lower] than [Group 2] (M = xx, SD = xx), t(df) = xx, p = xx, d = xx.
Template: Correlation
A Pearson correlation showed a [weak/moderate/strong] [positive/negative] association between [Variable A] and [Variable B], r(df) = xx, p = xx.
Template: ANOVA
A one-way ANOVA revealed a significant effect of [factor] on [dependent variable], F(df1, df2) = xx, p = xx, η² = xx. Post hoc tests indicated that [Group X] differed significantly from [Group Y].
Template: Regression
A multiple regression showed that the model significantly predicted [outcome], F(df1, df2) = xx, p = xx, R² = xx. [Predictor A] significantly predicted the outcome (β = xx, p = xx), while [Predictor B] did not.
Template: Chi-Square
A chi-square test of independence indicated that [Variable A] and [Variable B] were/weren’t significantly associated, χ²(df) = xx, p = xx.
Final Summary
Reporting SPSS results in APA format requires far more than copying numbers from output tables. It involves translating statistical findings into clear, clean, academically defensible narratives that communicate meaning. Across descriptive statistics, t-tests, ANOVA, regression, logistic regression, chi-square, reliability, factor analysis, and advanced modeling techniques, APA style ensures that your results are structured, concise, transparent, and easy to understand.
Understanding APA reporting not only improves your grades but also prepares you for academic writing in theses, dissertations, and professional research. Each section of this guide has shown you what to report, how to report it, and how to avoid common mistakes, ensuring you can produce polished, publishable statistical results confidently.
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