Q de COCHRAN en SPSS | Explicación, interpretación y reporte en APA

Cochran's Q Test: An Overview

Introduction to Cochran's Q Test

• The Cochran's Q test is a statistical method used to compare categorical variables, particularly for evaluating changes in binary responses over three or more time points.

• It is specifically designed for longitudinal studies where measurements are taken on the same subjects at multiple times.

Application of Cochran's Q Test

• In a study assessing the effectiveness of a new anxiety treatment, researchers measure participants' anxiety states before, after, and during follow-up assessments. This allows for tracking changes over time.

• The test helps determine if there is a significant reduction in anxiety symptoms among participants post-treatment by comparing the number of individuals who no longer exhibit anxiety symptoms across the three measurement points.

Criteria for Using Cochran's Q Test

• Paired Data: Measurements must be from the same subjects at least three different times, suitable for longitudinal analysis.

• Dichotomous Variables: The data should consist of two categories (e.g., "anxiety yes" or "anxiety no"). If more than two categories exist, consider using the Friedman test instead.

• The analysis focuses on comparing proportions to see how many individuals continue or cease to show anxiety symptoms over time. This can be applied in experimental settings where interventions occur between measurement periods.

Conducting Analysis with SPSS

• To perform the Cochran’s Q analysis in SPSS, input data from 50 participants who underwent a 3-month intervention program with pretest and posttest measures recorded as binary outcomes (yes/no).

• Navigate through SPSS menus to select non-parametric tests and set up your variables accordingly; ensure that you choose Cochran’s Q when dealing with binary categorical scales across multiple time points.

Interpreting Results

• After running the analysis, results will display frequencies of each category within your anxiety variable; this includes counts of those without versus with anxiety at each measurement point. For instance, initial results showed 13 without anxiety compared to 37 with it before treatment intervention.

• Subsequent follow-ups indicated an increase in individuals reporting no anxiety post-intervention (from 74% initially down to 8% after follow-up), suggesting potential effectiveness of the treatment applied during the study period.

Reporting Findings

Analysis of Q Cochrane in Anxiety Program Evaluation

Overview of Q Cochrane Analysis

• The analysis requires the value of Q Cochrane and its corresponding asymptotic significance related to the p-value. Results can be presented in a table or text format.

Results Presentation

• An example analysis indicates that during the pretest, 13 individuals (percentage not specified) did not have anxiety, while 37 individuals reported having anxiety.

• In the posttest phase, 6 participants reported anxiety, whereas 44 did not. By follow-up, 46 participants indicated they were free from anxiety, with only 4 continuing to experience it.

Statistical Significance

• The comparison test results were statistically significant, showing an effect of the program on anxiety symptoms over time. Both Q Cochrane and p-values should be included in results.

Table Inclusion Guidelines

• If presenting data in a table format, include frequencies, total percentages, Q Cochrane values, and p-values. Note that if the statistic exceeds one, omit values before the decimal point.

Handling P-values