Friday, February 4, 2022

Enneagram Personality Test RHETI

 


Scale name: Enneagram [Riso-Hudson Enneagram Type Indicator (RHETI)]

Scale overview: There is more than one version of the Enneagram, which purport to measure how an individual’s personality fits with nine types. The version referred to in this post is the RHETI—see above for the full name.

A study by Newgent et al. (2004) used the 144-item forced choice format.

Authors: Don Richard Riso and Russ Hudson

Response Type: Forced-choice format

Subscales: There are nine types referred to by number and a label:

1 Reformer- principled, idealistic

2 Helper- caring, interpersonal

3 Achiever- adaptable, success-oriented

4. Individualist- romantic, introspective

5 Investigator- intense, cerebral

6 Loyalist- committed, security-oriented

7 Enthusiast- busy, productive

8 Challenger- powerful, dominating

9 Peacemaker- easy-going, self-effacing

More detailed descriptions can be found at The Enneagram Institute

Sample item: (Newgent, et al., 2004, p. 228)

Item I contains the following two responses: "I've been romantic and imaginative" and "I’ve been pragmatic and down to earth." The first response is associated with the Individualist and the second response is associated with the Loyalist

Reliability: In their small study, Newgent et al. (2004) reported a range of alpha values from .56 (Achiever, Investigator) to .82 (Helper) six scales were at or above alpha .70.

 

Validity: Newgent et al. (2004) administered a version of the Big Five (NEO PI-R). They calculated correlations and performed a canonical variate analysis. They reported that all of the RHETI types were significantly correlated with at least one of the five NEO PI-R factors. See Table 1 for the details.

Notes

Hook et al. (2021) published a review of the literature on the Enneagram. They found mixed evidence regarding reliability and validity. Factor analyses have found less than nine factors.

Several have found the Enneagram useful in spiritual growth. For example, see (Kam, 2018; Singletary, 2020).

In the SCOPES model, the Enneagram fits in the O = Observable behavior pattern of functioning.

Availability

Link to the Riso-Hudson Enneagram Type Indicator version 2.5

The fee was $12 on the date of this blogpost

A shorter, 36-item version is free online as of the date of this blogpost

   Link to Open Enneagram of Personality Scales

 Related Post

Big Five Personality Test

HEXACO Personality Test

SCOPES model of human functioning

References

Hook, J. N., Hall, T. W., Davis, D. E., Van Tongeren, D. R., & Conner, M. (2021). The Enneagram: A systematic review of the literature and directions for future research. Journal of Clinical Psychology, 77(4), 865–883. https://doi.org/10.1002/jclp.23097

Kam, C. (2018). Integrating divine attachment theory and the Enneagram to help clients of abuse heal in their images of self, others, and God. Pastoral Psychology, 67(4), 341–356. https://doi.org/10.1007/s11089-018-0817-1

Newgent, R. A., Parr, P. E., Newman, I., & Higgins, K.K. (2004) The Riso-Hudson Enneagram Type Indicator: Estimates of reliability and validity. Measurement and evaluation in counseling and development, 36, 226-237.

Singletary, J. (2020). Head, heart, and hand: Understanding Enneagram centers for leadership development. Social Work & Christianity, 47(4), 3–18. https://doi.org/10.34043/swc.v47i3.126

 

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

 

 


 

Test Resource Link:  A – Z Test Index

Enneagram books on GOOGLE

Enneagram books by Riso and Hudson AMAZON

 

Links to Connections

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See my Books

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 Photo credit: Enneagram "wheel" from Microsoft Bing "Free to share and use."

 

 

Thursday, January 20, 2022

variance and standard deviation

Variance is a measure of the dispersion of values in a distribution of values.

 In psychology and behavioral science statistics, the variance is typically a reference to the extent to which numerical values vary around the arithmetic mean of a data set. Theoretically, the values vary around a population mean but in most cases, researchers work with samples.

In statistics, write sigma squared for the population variance σ2

Write final form sigma squared for the sample variance ς2

In reports, write VAR for variance.

How it works

If we have a set of different numerical values such as scores on a test we can calculate a mean, which is the average of all the scores divided by the number of scores.

The difference of one score from the mean is a deviation score. X is a score and the Greek letter mu μ is the symbol for the population mean.

In a sample, which is what we normally have in psychology, we subtract a score X from the sample mean M. Thus, X - M = the deviation score. 

If a person earns a 7 on a test where the mean is 10 then their score is 3 points below the mean. The deviation score is -3 (minus three).

To find an average deviation for all the scores on the test, we must subtract each score from the mean. We end up with a set of positive and negative values.

We want to find an average but if we add the positive and negative values we will end up with zero. To solve the problem, 

square each deviation score

add them together to get the sum of the squares (SS)

 then divide by the number of scores (n) to get the variance.

The variance = the SS divided by n

Standard Deviation (SD)

The standard deviation is the average unit by which scores are distant from the mean.

Find the standard deviation by taking the square route of the variance.

In reports, write SD for standard deviation.

Examples in tests

A common mean for IQ tests is 100 and a common standard deviation is 15 points.

In personality tests, a common standard score is a T-score. The mean is T = 50 and SD = 10.

Key concepts in this post

deviation score

mean

sum of squares

standard deviation

variance


Applied Statistics Concepts for Counselors on   AMAZON or   GOOGLE









Learn More in Creating Surveys on AMAZON or GOOGLE















Related post






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 


Factor Analysis Principal Components Analysis

 


Factor analysis (FA) is a statistical method of reducing a large set of data to a smaller set by identifying patterns in the data that have common characteristics. Factor analysis is sometimes called data reduction or dimension reduction.

The original numerical values in the data set are observed variables (also called manifest variables) such as the items in a large survey or test. Factor analysis may find patterns characterized by a shared statistical relationship representing a factor, which is also called a dimension. A researcher examines the content of the items linked to this factor and chooses a factor label such as verbal skills for related items on an intelligence test.

The factors may be treated as variables in additional research. These are secondary variables. Because they are created from the observed variables, they are considered latent variables. For example, if 5 items on a personality test are associated with one factor labeled "agreeableness" then agreeableness is a latent variable.

The set of identified factors is referred to as the structure of the data set. If the data are from a test then researchers refer to the structure of the test.

Factors are identified based on the variance they account for in the data. The amount of variance explained by a factor is represented by an eigenvalue. Researchers look for eigenvalues of 1.0 or more to consider a factor to be a valuable contribution to explaining the underlying structure of a data set.

Not all factors are equal. That is, when more than one factor have been identified, they will contribute differently to explaining the variance in the data set.


Different kinds of Factor Analysis

Exploratory Factor Analysis (EFA). When researchers do not know the structure of a data set, they use EFA to discover the set of factors.

Confirmatory Factor Analysis (CFA).  When researchers wish to test a hypothesis about a data set, they perform CFA. For example, if they believe their forgiveness questionnaire contains one factor called forgiveness, they can examine the structure to see if one factor best accounts for the data set. If one factor is the best solution then they have found support for their hypothesis.

Principal Components Analysis (PCA) is a common form of confirmatory factor analysis. 

Factor Analysis is important to understanding tests in Counseling and Psychotherapy. See

Applied Statistics Concepts for Counselors on   AMAZON or   GOOGLE








Factor Analysis is often used to reduce the data collected from survey research. 

Learn More in Creating Surveys on AMAZON or GOOGLE








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 







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 ...