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 Difference Test (HSD)

The Tukey HSD is a commonly used test, which adjusts for the number of comparisons.

Scheffés Test

Scheffés Test is similar to the Tukey HSD but it is slightly more conservative.

More post hoc tests

Additional post hoc tests are available. I will list them so you can recognize the test as one that evaluates a pair of means for significant differences after an overall test (such as an ANOVA).

Duncan's New Multiple Range Test (MRT)

Dunn's Multiple Comparison Test

Fisher's Least Significant Difference (LSD)

Holm-Bonferroni Procedure

Newman-Keuls

Rodger's Method

Dunnett's correction

Benjamin-Hochberg (BH) procedure

Cite this post

Sutton, G. (2022, July 24). Post hoc tests and data analyses. Assessment, Statistics, and Research. Retrieved from https://statistics.suttong.com/2022/07/post-hoc-tests-and-data-analyses.html 

Reference for using scales in research:

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Applied Statistics Concepts for Counselors on

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Effect Sizes (ES) in statistics

In statistics, an effect size (ES) indicates the strength of the relationship between two variables.

In psychological experiments, researchers are interested in the strength of the effect of the Independent Variable on the Dependent Variable.

In psychotherapy studies, researchers may be interested in the effects of treatment on a measure of the dependent variable. A research questions may be framed: How effective is a set of 6 CBT sessions on the reduction of depression?

Psychologists have often described effect sizes as small, medium, or large.

Cohen's d

Cohen's d is a measure of effect size between two groups. The mean of one group is subtracted from a second group and divided by the pooled standard deviation of the two groups.

ES = (M1 - M2) / SD

Effect Size  Label

0.2     Small

0.5     Medium

0.8     Large

Pearson Correlation Coefficient (r)

0.1 to 0.3  Small

0.3 to 0.5  Medium

0.5 to 1.0   Large

Converting Cohen's d to the correlation coefficient

r =  d/ √ d² + 4

Note. Negative values also indicate strength of a relationship.

Cite this post

Sutton, G. (2022, July 20). Effect sizes (ES) in statistics. Assessment, Statistics, and Research. Retrieved from https://statistics.suttong.com/2022/07/effect-sizes-es-in-statistics.html

References

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

Sutton, G. W. (2020). Applied statistics: Concepts for counselors, second edition. Springfield, MO: Sunflower.

 Link to an Index of Statistical Concepts in Psychology, Counseling, and Education

 Reference for using scales in research:

Creating Surveys on AMAZON      or     GOOGLE

 

Reference for clinicians on understanding assessment

Applied Statistics Concepts for Counselors on

 AMAZON 

or GOOGLE

 

Resource Link:  A-Z Statistical Terms

Resource Link:  A – Z Test Index

Related Terms

Standard Deviation

 

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Factor Analysis and Assessment EFA and CFA

 

Factor Analysis and Assessment

In testing, factor analysis is a mathematical strategy to analyze groups of items within a large test to see how well they relate to each other. The goal will be to reduce the large number of items to a set of factors that appear to measure different but related constructs; hence, factor analysis is a method of data reduction. (Sutton, 2020)

A large test of various abilities may be analyzed for ways to group different abilities. Short tests of vocabulary, verbal analogies, and synonyms might form a factor that a researcher could label as "Verbal Abilities."

A factor is a group of variables that are highly correlated with each other and, although different, they appear to have something in common. Researchers choose names for groups of variables based on the content of the variables in the factor. In large research projects, each participant may have scores on a large number of variables. Factor analysis can be used to identify patterns among the variables. Thus, it may be possible to reduce 30 variables to 5 or 6 groups of variables (that is factors).

A research database may contain several variables considered relevant to understanding the risk of child sexual abuse. Such variables may include prior abuse by a person in a close relationship to the child, age of a child, family problems, child problems, family structure, parenting difficulties, sex of the child, and so forth. Theoretically, researchers could look for patterns that may suggest ways to identify key risk factors.  (Sutton, 2020)

 

Exploratory Factor Analysis (EFA)

In the early phases of creating a test or questionnaire, researchers use EFA to explore or discover the structure of the measure. That is, they are looking for the number of factors that best fit the set of data.

 

Confirmatory Factor Analysis (CFA)

 After the data have been explored and the number of factors that best fit the data have been determined, researchers perform a CFA on a new sample. The purpose of CFA is to confirm or reject the factor structure previously thought to be the best fit for the data.

 Link to an Index of Statistical Concepts in Psychology, Counseling, and Education

Reference

Sutton, G. W. (2020). Applied statistics: Concepts for counselors, second edition. Springfield, MO: Sunflower.

AMAZON  Paperback ISBN-10: 168821772X, ISBN-13: 978-168217720

More information:  Book website:   counselorstatistics

 

 Reference for using scales in research:

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Reference for clinicians on understanding assessment

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Resource Link:  A-Z Statistical Terms

Resource Link:  A – Z Test Index

 

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Attitudes to Disability Scale (ADS)

 

Scale name: Attitudes to Disability Scale (ADS)

Scale overview: The Attitudes to Disability Scale (ADS) is a 16-item rating scale designed to measure attitudes toward disability. The ADS was translated into multiple languages.

 Response Type: Items are rated on a 5-point Likert scale of agreement.

 

Scale items = 16

     Scale subscales = 4

Using factor analysis, the authors identified four factors in the 16-item Attitudes to Disability Scale: Inclusion, Discrimination, Gains, and Prospects.

Inclusion

People with a disability find it harder than others to make new friends

Discrimination

People often make fun of disabilities

Gains

Having a disability can make someone a stronger person

Prospects

People with a disability have less to look forward to than

others

 Reliability: The authors reported ADS Cronbach’s alpha values of.795 and .764 in two samples.

Validity: The authors examined the structure of the scale using Confirmatory Factor Analysis. They also reported the results of IRT analyses.

 

Availability:

At the time of this post, I found two links to the article in addition to the journal reference.

Tilburg University Link

Another Link address

Reference for the ADS scale

Power, M. J., Green, A. M., van Heck, G. L., de Vries, J., & den Oudsten, B. L. (2010). The Attitudes to Disability Scale (ADS): Development and psychometric properties. Journal of Intellectual Disability Research, 54(9), 860-874. https://doi.org/10.1111/j.1365-2788.2010.01317.x

Reference for a study using the ADS

Zheng, Q., Tian, Q., Hao, C. et al. Comparison of attitudes toward disability and people with disability among caregivers, the public, and people with disability: findings from a cross-sectional survey. BMC Public Health 16, 1024 (2016). https://doi.org/10.1186/s12889-016-3670-0

 

Contact possibility

Author Mick Power provided information about the scale on ResearchGate.net

 

Reference for using scales in research:

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AMAZON

 

 

 

 

Reference for clinicians on understanding assessment

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Test Resource Link:  A – Z Test Index

 

 

NOTICE:

The information about scales and measures is provided for clinicians and researchers based on professional publications. The links to authors, materials, and references can change. You may be able to locate details by contacting the main author of the original article or another author on the article list.

 

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See my Books

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Read published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton