5 Love Languages Personal Profile for Couples

 

Scale name: Love Languages Personal Profile for Couples (LLPP)

Scale overview: The LLPP is a 50 item forced-choice measure developed by Chapman. There are 12 statements for each love language and each language is paired 3 times.

Author: Gary Chapman

Response Type

Subscales: There are 5 subscales corresponding to the 5 Love Languages:

Words of Affirmation (e.g., words of appreciation and affirmation; express kindness)

Quality Time (e.g., focused attention without interruption and without giving advice)

Receiving Gifts (giving gifts, which need not be expensive; the gift of time during a crisis)

Acts of Service (e.g., completing tasks for them)

Physical Touch (e.g., sexual and nonsexual)

__________

Sample items:

10. It's more meaningful to me when...

     A. I hear my partner tell me, "I'm proud of you."

     D. my partner helps me with a task.

7.  It's more meaningful to me when...

C. my partner gives me a gift.

A. I hear "l love you" from my partner.

__________

Reliability: Scales based on the 5 love languages have reported acceptable alpha levels. See Bland and McQueen (2018) for a summary.

Validity: The model of the 5 love languages has received general support in studies using different measures of the 5 love languages. Bland and McQueen (2018) examined the LLPP and found evidence that couples with similar love language preferences were more likely to report relationship satisfaction.

Availability: Link to the: love language quiz available online.

For a 30-item  pdf version of the LLPP for couples, see the link to Chapman 2015 below.

Permissions -- if identified

 References

Bland, A. M., & McQueen, K. S. (2018). The distribution of Chapman’s love languages in couples: An exploratory cluster analysis. Couple and Family Psychology: Research and Practice, 7(2), 103–126. https://doi.org/10.1037/cfp0000102

Chapman, G. (2015). Love Languages Personal Profile for Couples. Link to pdf version. Note: This link worked when posted but it has not always worked. Suggestion, search for Love Languages Personal Profile for Couples pdf.

Love Languages books by Gary Chapman. Chapman has a number of books explaining the five love languages.

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Resource Link for more tests:  A – Z Test Index

Link to Sternberg's Triangular Love Scale

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Behaviors Toward God Scale (BTGS)

 

Scale name:    Behaviors Toward God Scale (BTGS)

Scale overview: The Behaviors Toward God Scale (BTGS) is an 18-item self-report measure of  behavioral responses toward God when people experience a spiritual or religious struggle. The items are grouped into four subscales.

Authors: Julie J. Exline et al. (2021)

Response Type:  

Participants read a statement and rate their behavioral response from 0 = not at all to 10 = extremely.

Subscales: Four (sample item in parentheses)

1. Approach – draw close (tried to trust God)

2. Disengage - turn away or exit (ignored God)

3. Protest-  complain, argue, or question (asked God "Why?")

4. Suppress – avoid or minimize negative feelings toward God (tried to hide feelings of anger or disappointment toward God)

 

Reliability: alpha values for the four subscales ranged from .80 to .89 in study 1 and .79 to .91 in study 2 (Exline et al., 2021)

Validity: The Exline  et al. 2021 article includes the results of factor analyses and correlations with other measures. A technical report by Stauner & Exline (2020) provides a look at the factor structure of a 23-item version, which was reduced to 18-items.

Availability: Author contact email Dr. Julie J. Exline   julie.exline@case.edu

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 References

Exline, J. J., Wilt, J. A., Stauner, N., & Pargament, K. I. (2021). Approach, disengagement, protest, and suppression: Four behaviors toward God in the context of religious/spiritual struggle. Psychology of Religion and Spirituality. https://doi.org/10.1037/rel0000445.supp (Supplemental)

Stauner, N. & Exline, J.J. (2020). Factor analysis of behaviors toward God. Unpublished technical report available from osf.io

 

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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 purports 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

 

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Test Resource Link:  A – Z Test Index

Enneagram books on GOOGLE

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

 

 

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

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Normal distribution

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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

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

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Perceived Conflict between Evolution and Religion Scale (PCoRE)

 

Scale name: Perceived Conflict between Evolution and Religion (PCoRE)

Scale overview:

Authors: M. Elizabeth Barnes, K. Supriya, Yi Zheng, Julie A. Roberts, and Sara E. Brownell

Response Type: Participants rate each item on a 5-point Likert-type scale from strongly disagree to strongly agree.

 

Subscales with a sample item: There are four subscales as follows:

  1. Perceived conflict between evolution and belief in God

My belief in God makes it harder to believe that all of life on Earth evolved from ancient microscopic life.

  2. Perceived conflict between evolution and religious teachings

The teachings of my religion contradict that all of life on Earth evolved from ancient microscopic life.

  3. Perceived conflict with evolution among religious community

My religious community does not believe that all of life on Earth evolved from ancient microscopic life.

  4. Perceived conflict between evolution and religious beliefs

My personal religious beliefs make it harder to believe that humans evolved from ancient ape ancestors.

Reliability: The article includes reliability calculations.

Validity: The article contains information about process, content, concurrent, and construct validity.

Availability: The full scale is available is available in a pdf supplement https://www.lifescied.org/doi/suppl/10.1187/cbe.21-02-0024

Permissions – See this link for general permissions. https://www.lifescied.org/permissions

Author contact:  liz.barnes@mtsu.edu

 

Article Reference

Barnes, M. E., Supriya, K., Zheng, Y., Roberts, J.A., & Brownell, S.E. (2021). A New Measure of Students’ Perceived Conflict between Evolution and Religion (PCoRE) Is a Stronger Predictor of Evolution Acceptance than Understanding or Religiosity. CBE—Life Sciences Education, 20, 3. https://doi.org/10.1187/cbe.21-02-0024

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