Sunday, August 21, 2022

Reading Experimental Research - A Student Guide

 

READING EXPERIMENTAL RESEARCH:

QUESTIONS TO GUIDE YOUR ANALYSIS

Geoffrey W. Sutton, Ph.D.

 

Use the following questions to help you read psychological experiments. With experience, the questions should become a natural part of your analysis.

 

Who are the authors?

When was the study published?

Where do the authors write?

How do you contact the lead author?

Which journal published the article?

How was the research funded?

What might the above situation suggest about the research?

What was studied (variables)?

Why was it studied (need, importance)?

What theory or theories provide the context for the study?

What have previous studies found?

What was expected (purpose, hypotheses)?

Whom (describe the participants)?

Age

Gender

Ethnicity

Other key variables

How did the authors operationally define their variables?

How to (what procedures were followed)?

How did they control for possible confounding effects (internal validity)?

How were the data analyzed?

What happened (what did the authors discover in each experiment)?

So what (how are the findings related to the theory and hypotheses)?

How far can we generalize (limitations and external validity)?

What’s next (what do the authors suggest we should no next)?

How credible are their sources (relevance of the reference list)?

 

Learn more about research in Creating Surveys    Buy on GOOGLE BOOKS


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Please check out my website   www.suttong.com

   and see my books on   AMAZON       or  GOOGLE STORE

Also, consider connecting with me on    FACEBOOK   Geoff W. Sutton    

   TWITTER  @Geoff.W.Sutton    

You can read many published articles at no charge:

  Academia   Geoff W Sutton     ResearchGate   Geoffrey W Sutton 



Thursday, August 18, 2022

Self-Censorship Orientation (SCO)

 


Scale name: Self-Censorship Orientation (SCO)

Scale overview: The Self-Censorship Orientation (SCO) is a 14-item scale designed to measure self-censorship, which the authors define as “intentionally and voluntarily withholding information from others in absence of formal obstacles.”

Authors: Keren Sharvit et al. See the 2018 reference for the list of authors.

Response Type: Items are rated on a scale of agreement from 1 = disagree to 4 = agree and 5 = undecided.

Subscales and items

  The authors identified two factors or subscales.

1. Self-censorship

The first dimension, labeled “self-censorship”, reflects the tendency to conceal information that is seen as threatening.” (p. 347)

Example: 1 If I would encounter problematic conduct among my group members, I would feel responsible to bring that information to light.

2. Disclosure

The second dimension, labeled “disclosure”, reflects the tendency to disseminate critical information.”

Example: 9. People who disclose credible information to external sources, which exposes my group to criticism, should be condemned.

 

Reliability:

Values from stage 2:

Self-censorship: Alpha = .84, Rtt =.61

Disclosure: Alpha = .90, Rtt =.56

 

Validity: The authors reported exploratory and confirmatory factor analysis. 

They also report correlations with other measures (see Table 4) used in studies described in the 2018 article. Read more about test validity.

 

Availability:

The items are included in the Sharvit et al. (2018) article listed below.

 

Permissions:

The author contact in the article is: ksharvit@psy.haifa.ac.il

 

Reference for the scale

Sharvit, K., Bar-Tal, D., Hameiri, B., Zafran, A., Shahar, E., & Raviv, A. (2018). Self-Censorship Orientation: Scale development, correlates and outcomes. Journal of Social and Political Psychology, 6(2), 331–363. https://doi.org/10.5964/jspp.v6i2.859

Related reference

Hayes, A. F., Glynn, C. J., & Shanahan, J. (2005a). Willingness to self-censor: A construct and measurement tool for public opinion research. International Journal of Public Opinion Research, 17, 298-323. doi:10.1093/ijpor/edh073

 

Comment:

This scale focuses on self-censorship of information in contrast to the Willingness to self-censor (WTSC) measure, which focused on opinions (Hayes et al. 2005).

Reference for using scales in research:

Creating Surveys on     

   AMAZON 

or  GOOGLE BOOKS

 


 

 Reference for clinicians on understanding assessment

Applied Statistics Concepts for Counselors 

on AMAZON 


or GOOGLE

 


 

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.

 

Links to Connections


Checkout My Website   www.suttong.com

  

See my Books

  AMAZON      

 

  GOOGLE STORE

 

FOLLOW me on

   FACEBOOK   Geoff W. Sutton  

  

   TWITTER  @Geoff.W.Sutton

 

   PINTEREST  www.pinterest.com/GeoffWSutton

 

Read published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton 

 

photo note: From Bing images free to share and use

 

 

Wednesday, August 17, 2022

Christian Sociomoral Values Index

 



Scale name: Christian Sociomoral Values Index

Scale overview: This 13-item rating scale aims to measure the importance of select moral values commonly held among conservative Christians.

 

Response Type: Items are rated on a scale of agreement as follows:

1 = strongly disagree

2 = disagree

3 =  Neither Agree nor Disagree

4 = Agree

5 = Strongly agree

Scale items = 13



1. All forms of birth control are sinful.

2. Birth control methods are acceptable if they do not cause an abortion.

3. Abortion is always sinful.

4. Premarital sex is always sinful.

5. Cohabitation is always sinful.

6. A biblical marriage is between one man and one woman.

7. Same-sex marriage is sinful.

8. Divorce is sinful.

9. Sexual orientation is a choice.

10. In a Christian marriage, a man and a woman submit to each other, but the man is always the head of the marriage.

11. Women have a vital role in Christian ministry, but they should not be priests or pastors.

12. Women have an important role in churches and Christian organizations, but they should not have authority over a man.

13. Women should seek counseling from women and men should seek counseling from men.

  

Psychometric data:  The scale mean was 40.32 (SD = 8.85), Skew = -.69, Kurtosis = .30 (Sutton et al., 2016).

Reliability: In a sample of 220 Christian counselors, Cronbach’sAlpha = .85 (Sutton et al., 2016).

Validity: Data from table 2 (Sutton et al., 2016) show significant positive correlations with the following related measures of conservative Christian spirituality. (Read about test validity)

Christian Beliefs Index (CBI)

Intratextual Fundamentalism Scale

Christian Service Scale

Personal Christian Practices Index

Healing Experiences Scale

Basic Spiritual Practices Index

Evidence Supported Christian Accommodative Treatments Index

Extra-Session Christian Assignments Index

Spiritual Assessment Index

Availability:

The items and detailed responses are included in Sutton (2021). The statistical data reported above can be found in Sutton et al. 2016.

The items are listed above and available for use in research and teaching.

Permission:

The index may be used in research, teaching, and clinical practice. Users should cite Sutton (2021) and Sutton et al. (2016) in all publications and presentations that use this index. There is no need to contact the authors for non-commercial use.

To include the index in books and commercial publications, request permission from suttong@evangel.edu.


References for the scale

Sutton, G. W. (2021). Counseling and psychotherapy with Pentecostal and Charismatic Christians: Culture & Research | Assessment & Practice. Springfield, MO: Sunflower.  ISBN-13 : 979-8681036524 AMAZON

Sutton, G. W., Arnzen, C., & Kelly, H. (2016). Christian counseling and psychotherapy: Components of clinician spirituality that predict type of Christian intervention. Journal of Psychology and Christianity, 35, 204-214.  ResearchGate

                        Academia

 

Example of the details available in Counseling and Psychotherapy with Pentecostal and Charismatic Christians.

For item #2:   Birth control methods are acceptable if they do not cause an abortion.

The table values are percentages from a total of 208 responses.

The number of people Christians were:

  Evangelical 82

  Pentecostal/ Charismatic 48

  Nondenominational  43

  Other  35

 The "other" group includes respondents who identified as Catholics or a mainline Protestant Denomination


See the text for details on the other items.

Research source







ad. Reference for using scales in survey research:

Creating Surveys on

     AMAZON    

or   GOOGLE BOOKS

 


  

ad. Reference for clinicians on understanding assessment

Applied Statistics Concepts for Counselors on

  AMAZON 

or GOOGLE BOOKS

 


  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.

 

Links to Connections


Please checkout My Website   www.suttong.com

  

See my Books

  AMAZON      

 

  GOOGLE STORE

 

FOLLOW me on

   FACEBOOK   Geoff W. Sutton  

  

   TWITTER  @Geoff.W.Sutton

 

   PINTEREST  www.pinterest.com/GeoffWSutton

 

Read published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton 

 Photo credit: bing.com images free to share and use

 

 

 

Sunday, July 24, 2022

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.

Scheff├ęs Test

Scheff├ęs 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:

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 Test Index



Please check out my website   www.suttong.com

   and see my books on   AMAZON       or  GOOGLE STORE

Also, consider connecting with me on    FACEBOOK   Geoff W. Sutton    

   TWITTER  @Geoff.W.Sutton    

You can read many published articles at no charge:

  Academia   Geoff W Sutton     ResearchGate   Geoffrey W Sutton 

 




Wednesday, July 20, 2022

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

 



Links to Connections

Checkout My Website   www.suttong.com

  

See my Books

  AMAZON      

 

  GOOGLE STORE

 

FOLLOW me on

   FACEBOOK   Geoff W. Sutton  

  

   TWITTER  @Geoff.W.Sutton

 

   PINTEREST  www.pinterest.com/GeoffWSutton

 

Read published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton 

Monday, July 11, 2022

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:

Buy Creating Surveys on

GOOGLE BOOKS

 

AMAZON

 


 

 




 

Reference for clinicians on understanding assessment

Buy Applied Statistics for Counselors

 

GOOGLE BOOKS

 

AMAZON

 


 

 





Resource Link:  A-Z Statistical Terms


Resource Link:  A – Z Test Index

 

Links to Connections

Checkout My Website   www.suttong.com

  

See my Books

  AMAZON      

 

  GOOGLE STORE

 

FOLLOW me on

   FACEBOOK   Geoff W. Sutton  

  

   TWITTER  @Geoff.W.Sutton

 

   PINTEREST  www.pinterest.com/GeoffWSutton

 

Read published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton 

 

 

 

 

Belief in God Scale

  Assessment name: Belief in God Scale Scale overview: Authors: D. Randles et al. (2015). Response Type: Items are rated on a scale ...