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Showing posts with the label ANCOVA

### Post Hoc Tests and Data Analyses

A post hoc test is a statistical test used to determine if a pair of values are significantly different from each other after the primary analysis has been completed. The term post hoc is a Latin phrase meaning after the event. A common use of post hoc tests is the comparison of group means after an F -test in an ANOVA has revealed significant differences among the groups. The reason to test for differences after an overall test like ANOVA is to reduce the risk of finding a significant difference by chance. That is, if researchers perform a large number of tests on a sample, they may find one or more tests significant by chance. There are many post hoc tests. Following are some examples of tests that compare the means of two groups. Bonferroni Test This is a popular test. By dividing the significance level by the number of comparisons, the risk of finding a significant difference by chance is reduced. This procedure is called the Bonferroni Correction. Tukey's Honest Significant

### Covariate

Covariate . A variable that is correlated with a dependent variable in a research study. When a covariate is identified and measured, the value of the dependent variable can be adjusted for the contribution of the covariate during data analysis. Example: If researchers collect information on the variable age in a study about forgiveness and if age is related to forgiveness, then age can be treated as a covariate to identify how people scored on a forgiveness survey after the scores have been adjusted for age. The word adjusted is a key word to look for in reading research that includes a covariate. Procedures that include covariates are ANCOVA and MANCOVA . Cite this post. Sutton, G. W. (2021, January 14). Covariate. Statistics .  https://statistics.suttong.com/2021/01/covariate.html Please subscribe to this blog for updated statistics and measures. Applied Statistics Concepts for Counselors on  AMAZON  or  GOOGLE Creating Surveys on AMAZON    or   GOOGLE  Worldwide Links to Connect

### MANCOVA

MANCOVA (Multivariate Analysis of Covariance). A statistical procedure for analyzing results when there are one or more independent variables , two or more dependent variables , and one or more covariates . Basic components of MANCOVA Independent or grouping Variable = 1 or more Dependent or criterion Variable = 2 or more Covariates = 1 or more Overall tests are used to determine significant effects or differences among the grouping variables. An F test indicates significance overall and for specific effects or relationships. A commonly reported measure of effect size is eta squared. A p value reveals the probability of a significant relationship-- one that is not due to chance factors. Applied Statistics Concepts for Counselors on  AMAZON  or  GOOGLE Creating Surveys on AMAZON    or   GOOGLE  Worldwide Links to Connections   Checkout My Website     www.suttong.com    See my Books      AMAZON             GOOGLE STORE   JOIN me on      FACEBOOK     Geoff W. Sutton          TWITTER

### ANCOVA in Counseling & Behavioral Research

ANCOVA ANCOVA is a procedure like ANOVA except researchers can study the effects of one or more independent variables on a dependent variable after adjusting for other variables, called covariates , which were not a primary focus of the study. The letter C in ANCOVA stands for covariate . There can be several covariates in a study. In testing for differences among groups experiencing different leadership styles, we could study the effects on employee satisfaction after adjusting for a covariate of years of employment. A key word in ANCOVA studies is adjusting . Analysts adjust the scores based on information about the covariate before testing for significant differences. Basic features of an ANCOVA: Independent or grouping Variable = 1 or more Dependent or criterion Variable = 1 Covariates = 1 or more An  F  test indicates significance overall and for specific effects or relationships. A commonly reported measure of effect size is eta squared. A  p  value reveals the probabilit