Easy statistics help with SPSS
SPSS has a user-friendly interface and powerful capabilities
Conducting statistics and interpreting outputs is easy in SPSS
Statistical Package for the Social Sciences (SPSS; Armonk, NY, IBM Corp.) is a statistical software application that allows for researchers to enter and manipulate data and conduct various statistical analyses. Step by step methods for conducting and interpreting over 60 statistical tests are available in Research Engineer. Videos will be coming soon. Click on a link below to gain access to the methods for conducting and interpreting the statistical analysis in SPSS.
Comparison of independent groups on an outcome
Number of groups, scales of measurement, and meeting statistical assumptions
Between-subjects statistics are used when comparing independent groups on an outcome. Independent groups means that the groups are "different" or "independent" from each other according to some characteristic. With between-subjects designs, participants can only be part of one group (independence) and only observed once (independence of observations, IOO).
One chooses a between-subjects statistical test based on the following:
1. Number of independent groups being compared (one group, two groups, or three or more groups)
2. Scale of measurement of the outcome (categorical, ordinal, or continuous)
3. Meeting statistical assumptions (independence of observations, normality, and homogeneity of variance)
Here is a list of between-subjects statistical tests and when they are utilized in applied quantitative research:
1. Chi-square Goodness-of-fit - One group, categorical outcome, a priori hypothesis for dispersal of outcome
2. One-sample median test - One group, ordinal outcome, a priori hypothesis for median value
3. One-sample t-test - One group, continuous outcome, meet the assumption of IOO and normality, a priori hypothesis for mean value
4. Chi-square - Two independent groups, categorical outcome, and chi-square assumption (more than five observations in each cell)
5. Fisher's Exact test - Two independent groups, categorical outcome, and when the chi-square assumption is not met
6. Mann-Whitney U - Two independent groups, ordinal outcome, and when the assumption of homogeneity of variance for independent samples t-test is violated
7. Independent samples t-test - Two independent groups, continuous outcome, meet the assumption of IOO, normality (skewness and kurtosis statistics), and homogeneity of variance (also known as homoscedasticity, tested with Levene's test)
8. Unadjusted odds ratio - Three or more independent groups, categorical outcome, chi-square assumption, choose a reference category and compare each independent group to the reference
9. Kruskal-Wallis - Three or more independent groups, ordinal outcome, and when the assumption of homogeneity of variance is violated
10. ANOVA - Three or more independent groups, continuous outcome, meet the assumption of IOO, normality, and homogeneity of variance
Chi-square vs. Fisher's Exact Test
Chi-square vs. Fisher's Exact Test
Meeting chi-square assumption of at least five observations per cell
There is a fundamental difference between chi-square and Fisher's Exact test. They are often used interchangeably both in everyday empirical discourse and also in the literature. There are many calculators available for free on the internet that will calculate inferential statistics for chi-square tests of independence and fisher's exact test. Without the proper statistical competencies, researchers can employ the wrong test. Here is how to know which of these tests to use with your research data:
1. Chi-square - This non-parametric test is used when you are looking at the association between dichotomous categorical variables. The primary inference yielded from this test is the unadjusted odds ratio with 95% confidence interval. EACH CELL of the 2x2 table MUST have at least five observations.
2. Fisher's Exact Test - This non-parametric test is employed when you are looking at the association between dichotomous categorical variables. The primary inference here is also the unadjusted odds ratio with 95% confidence interval. However, the Fisher's Exact Test is used instead of chi-square if ONE OF THE CELLS in the 2x2 has LESS than five observations.
Eric Heidel, Ph.D. is Owner and Operator of Scalë, LLC.
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