MMPI-A Scale-Level Factor Structure: Replication in a Clinical Sample

Assessment ◽  
1997 ◽  
Vol 4 (4) ◽  
pp. 337-349 ◽  
Author(s):  
Robert P. Archer ◽  
Radhika Krishnamurthy
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Minnesota Multiphasic Personality Inventory ◽  
Clinical Sample ◽  
Correlation Coefficients ◽  
Normal Sample ◽  
Clinical Settings ◽  
Scale Level ◽  
Similar Factor ◽  
Structural Summary

The adolescent form of the Minnesota Multiphasic Personality Inventory (MMPI-A) Structural Summary was developed from the results of a scale-level factor analysis conducted by Archer, Belevich, and Elkins based on the MMPI-A normative sample. The present study examined the scale-level factor structure of the MMPI-A in a clinical sample of 358 adolescents receiving outpatient or inpatient psychiatric services. A Principal Factor Analysis was performed using the raw score intercorrelation matrix from the 69 scales and subscales of the MMPI-A. The procedure yielded nine factors that accounted for 75.6% of the total variance in scale and subscale raw scores. Results from this clinical sample indicated that seven of the eight dimensions that appear on the MMPI-A Structural Summary were replicated in terms of producing highly similar factor structure correlation coefficients with those reported by Archer, Belevich, et al. for a normal sample. The present findings support the use of the MMPI-A Structural Summary for the assessment of adolescents in clinical settings.

The Development of the Scale of Protective Factors: Resilience in a Violent Trauma Sample

2015 ◽  
Vol 30 (5) ◽  
pp. 735-755 ◽  
Author(s):  
Elisabeth Ponce-Garcia ◽  
Amy N. Madewell ◽  
Shelia M. Kennison
Keyword(s):  
College Students ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Protective Factors ◽  
Factor Structure ◽  
Individual Factors ◽  
Clinical Settings ◽  
Clinical Criteria ◽  
Resilience Scale ◽  
Confirmatory Factor

This research developed the Scale of Protective Factors (SPF-24) to measure protective factors contributing to resilience. We investigated the factor structure of 35 items. After exploratory factor analysis, we subjected 25 items representing 2 social-interpersonal and 2 cognitive-individual factors to confirmatory factor analysis. The sample consisted of 942 college students from 3 studies and 2 institutions. To examine the diagnostic function of the SPF, we used clinical criteria to identify a subsample of participants who had experienced violent trauma and scored low, moderate, or high on an established resilience scale. Results showed that the low-resilient group scored significantly lower on all subscales of the SPF with marked differences in prioritizing/planning behavior. Implications for the research and clinical settings are discussed.

Giving G a Meaning: An Application of the Bifactor-(S-1) Approach to Realize a More Symptom-Oriented Modeling of the Beck Depression Inventory–II

Assessment ◽  
2018 ◽  
Vol 27 (7) ◽  
pp. 1429-1447 ◽  
Author(s):  
Manuel Heinrich ◽  
Pavle Zagorscak ◽  
Michael Eid ◽  
Christine Knaevelsrud
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Beck Depression Inventory ◽  
Factor Structure ◽  
Clinical Sample ◽  
First Order ◽  
Confirmatory Factor ◽  
Bifactor Models ◽  
Current Article ◽  
Analysis Models

The Beck Depression Inventory–II is one of the most frequently used scales to assess depressive burden. Despite many psychometric evaluations, its factor structure is still a topic of debate. An increasing number of articles using fully symmetrical bifactor models have been published recently. However, they all produce anomalous results, which lead to psychometric and interpretational difficulties. To avoid anomalous results, the bifactor-(S-1) approach has recently been proposed as alternative for fitting bifactor structures. The current article compares the applicability of fully symmetrical bifactor models and symptom-oriented bifactor-(S-1) and first-order confirmatory factor analysis models in a large clinical sample ( N = 3,279) of adults. The results suggest that bifactor-(S-1) models are preferable when bifactor structures are of interest, since they reduce problematic results observed in fully symmetrical bifactor models and give the G factor an unambiguous meaning. Otherwise, symptom-oriented first-order confirmatory factor analysis models present a reasonable alternative.

scholarly journals The Adolescent Resilience Questionnaire: Validation of a Shortened Version in U.S. Youths

2020 ◽  
Vol 11 ◽  
Author(s):  
Jacqueline R. Anderson ◽  
Michael Killian ◽  
Jennifer L. Hughes ◽  
A. John Rush ◽  
Madhukar H. Trivedi
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Correlation Coefficients ◽  
Internal Consistency Reliability ◽  
Self Report ◽  
Questionnaire Validation ◽  
Cross Sectional ◽  
Confirmatory Factor ◽  
Report Measure

IntroductionResilience is a factor in how youth respond to adversity. The 88-item Adolescent Resilience Questionnaire is a comprehensive, multi-dimensional self-report measure of resilience developed with Australian youth.MethodsUsing a cross-sectional adolescent population (n = 3,222), confirmatory factor analysis was conducted to replicate the original factor structure. Over half of the adolescents were non-white and 9th graders with a mean age of 15.5.ResultsOur exploratory factor analysis shortened the measure for which we conducted the psychometric analyses. The original factor structure was not replicated. The exploratory factor analysis provided a 49-item measure. Internal consistency reliability for all 12 factors ranged from acceptable (α> 0.70–0.80). The revised factor total scores were highly and significantly correlated with item–total correlation coefficients (r > 0.63, p < 0.001).ConclusionThis revised shorter 49-item version of the Adolescent Resilience Questionnaire could be deployed and has acceptable psychometric properties.

Metacognitive beliefs in depressed in-patients: adaptation and validation of the short version of the Metacognitions Questionnaire (MCQ-30) for French clinical and non-clinical samples

2020 ◽  
Vol 48 (4) ◽  
pp. 498-502
Author(s):  
Axel Baptista ◽  
Charlotte Soumet-Leman ◽  
Arnauld Visinet ◽  
Roland Jouvent
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Structure ◽  
Convergent Validity ◽  
Clinical Sample ◽  
Clinical Samples ◽  
French Version ◽  
Confirmatory Factor ◽  
Metacognitive Beliefs ◽  
Method Analysis

AbstractBackground:The short form of the Metacognitions Questionnaire (MCQ-30) is a brief multi-dimensional measure which explores the metacognitive processes and beliefs about worry and cognition that are central to the vulnerability and maintenance of emotional disorders.Aims:The first aim of the study was to create and validate a French version of the MCQ-30 in a non-clinical and a clinical sample of depressed in-patients.Method:A French adaptation of the MCQ-30 was administered to a sample of 467 individuals from the general population and 73 hospitalized patients with major depressive disorder. Internal consistency was measured by Cronbach’s alpha reliability coefficients. Factor structure was assessed using a confirmatory factor analysis on the non-clinical group and a multi-trait–multi-method analysis on the psychiatric group. Criterion validity was explored by comparing the scores of the two samples. Measures of rumination, worry and depression were used to explore convergent validity.Results:Confirmatory factor analysis in the non-clinical sample indicated that the French version of the MCQ-30 has the same factor structure as the MCQ-30’s original five-factor solution. In the clinical sample, the multi-trait–multi-method analysis revealed discrepancies with the original factor structure, and the MCQ-30 and its subscales were less reliable. Our results provide evidence of a convergent validity. The MCQ-30 scores were also able to discriminate between psychiatric and non-clinical samples.Conclusions:Our results show that the French version of the MCQ-30 is a valid instrument for measuring metacognitive beliefs in non-clinical population. Further research is needed to support its use among depressed in-patients.

Explanatory and Confirmatory Factor Structure of Beck Anxiety Inventory in College Sample

2017 ◽  
Vol 41 (S1) ◽  
pp. S108-S108
Author(s):  
S. Kareemi ◽  
B.M. Alansari
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Clinical Sample ◽  
Self Report ◽  
Beck Anxiety Inventory ◽  
College Sample ◽  
Two Samples ◽  
Confirmatory Factor ◽  
Males And Females ◽  
Competing Interest

IntroductionThe Beck anxiety inventory (BAI) is a widely used 21-item self-report inventory used to assess anxiety levels in adults and adolescents in both clinical and non-clinical populations. The values for each item are summed yielding an overall or total score for all 21 symptoms that can range between 0 and 63 points. A total score of 0–7 is interpreted as a “Minimal” level of anxiety; 8–15 as “Mild”; 16–25 as “Moderate”, and; 26–63 as “Severe”. There is no study until this date that examines the Explanatory and confirmatory factor structure factor structure of BAI in college student in Kuwaiti.ObjectivesThe current study investigated the original four-factor structure of the (BAI) in non-clinical sample of college students.MethodsSample one consisted of 540 males and females while sample two consisted of 600 males and females from Kuwait University undergraduates. The Arabic version of BAI was administered to participants. Explanatory factor analysis based on sample one and conformity factor analysis based on sample 2.ResultsThe results revealed four factor structures of BAI in the two samples of Kuwaiti students. Which included neurophysiological, subjective, autonomic, and panic factors.ConclusionsThe results of both confirmatory and exploratory factor analysis indicated that the original four-factor structures of the BAI do provide the best fit for the college sample.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Bayesian and Maximum-Likelihood Modeling and Higher-Level Scores of Interpersonal Problems With Circumplex Structure

2021 ◽  
Vol 12 ◽  
Author(s):  
Anneke C. Weide ◽  
Vera Scheuble ◽  
André Beauducel
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Confirmatory Factor Analysis ◽  
Factor Structure ◽  
Clinical Sample ◽  
Level Structure ◽  
Interpersonal Problems ◽  
Online Questionnaire ◽  
Confirmatory Factor ◽  
Three Factor

Difficulties in interpersonal behavior are often measured by the circumplex-based Inventory of Interpersonal Problems. Its eight scales can be represented by a three-factor structure with two circumplex factors, Dominance and Love, and a general problem factor, Distress. Bayesian confirmatory factor analysis is well-suited to evaluate the higher-level structure of interpersonal problems because circumplex loading priors allow for data-driven adjustments and a more flexible investigation of the ideal circumplex pattern than conventional maximum likelihood confirmatory factor analysis. Using a non-clinical sample from an online questionnaire study (N = 822), we replicated the three-factor structure of the IIP by maximum likelihood and Bayesian confirmatory factor analysis and found great proximity of the Bayesian loadings to perfect circumplexity. We found additional support for the validity of the three-factor model of the IIP by including external criteria-Agreeableness, Extraversion, and Neuroticism from the Big Five and subclinical grandiose narcissism-in the analysis. We also investigated higher-level scores for Dominance, Love, and Distress using traditional regression factor scores and weighted sum scores. We found excellent reliability (with Rtt ≥ 0.90) for Dominance, Love, and Distress for the two scoring methods. We found high congruence of the higher-level scores with the underlying factors and good circumplex properties of the scoring models. The correlational pattern with the external measures was in line with theoretical expectations and similar to the results from the factor analysis. We encourage the use of Bayesian modeling when dealing with circumplex structure and recommend the use of higher-level scores for interpersonal problems as parsimonious, reliable, and valid measures.

Factor Structure of Problem Behavior for Mildly Handicapped and Non-Handicapped Students

1983 ◽  
Vol 52 (3) ◽  
pp. 703-707 ◽  
Author(s):  
Jerry B. Hutton ◽  
Timothy Roberts
Keyword(s):  
Factor Analysis ◽  
Problem Behavior ◽  
Problem Behaviors ◽  
Emotional Disturbance ◽  
Factor Structure ◽  
Principal Component ◽  
Principal Component Factor Analysis ◽  
Component Factor ◽  
Similar Factor

Mildly handicapped ( n = 521) and non-handicapped students ( n = 561) in Grades 1 to 12 were rated by 52 teachers on a checklist of items corresponding to Bower's list of characteristics of emotional disturbance. An unrotated principal component factor analysis identified 8 factors for both groups of students with eigenvalues greater than 1.00. Coefficients of congruence indicated a similar factor structure for the two groups of students on the first three factors. The mildly handicapped students were reported to have significantly more problem behaviors than the non-handicapped students. Those who work with both groups of students should be trained to cope with behaviors that suggest emotional disturbance.

Explanatory and Confirmatory Factor Structure of Beck Scale for Suicide Ideation in Non-clinical Sample

2017 ◽  
Vol 41 (S1) ◽  
pp. S139-S139 ◽  
Author(s):  
R. Alsalman ◽  
B. Alansari
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Suicide Ideation ◽  
Factor Model ◽  
Clinical Sample ◽  
Self Report ◽  
Kuwait University ◽  
Factor Ii ◽  
Confirmatory Factor ◽  
Three Factor

IntroductionThe Beck Scale for Suicidal Ideation (BSS) is a self-report 19-item scale preceded by five screening items. The BSS and its screening items are intended to assess a patient's thoughts, plans and intent to commit suicide. All 24 items are rated on a three-point scale (0 to 2). In this study, scores from the five screening items were included in the overall score. Therefore, total scores could range from 0 to 48.There is no study until this date that examines the factor structure of BSS among Kuwaitis.ObjectivesThe current study investigated the original three-factor model of the (BSS) in non-clinical sample.MethodsSample one consisted of 285 males and 300 female students from Kuwait University while sample two consisted of 201 males and 302 females from Kuwait University. The Arabic version of BSS was administered to participants. Explanatory factor based on sample 1 and conformity factor analysis based on sample 2 of BSS were used in this study.ResultsThe explanatory and confirmatory factor analysis of (BSS) extracts three factors and three structures of BSS. Factor I = active suicidal desire; Factor II = preparation; Factor III = passive suicidal desire. In addition, the results revealed no significant gender differences in the factor structure of BSS.ConclusionsThe results of both confirmatory and exploratory factor analysis indicated that the original three-factor model provided the best fit for the two genders.

scholarly journals Technology proficiency self-assessments of teachers becoming professional in the 21st century: A scale adaptation study

2020 ◽  
Vol 10 (2) ◽  
pp. 465-492
Author(s):  
Mustafa Fidan ◽  
Murat Debbağ ◽  
Barış Çukurbaşı
Keyword(s):  
Factor Analysis ◽  
Public Schools ◽  
Factor Structure ◽  
21St Century ◽  
Correlation Coefficients ◽  
21St Century Learning ◽  
Measurement Tool ◽  
Technology Proficiency ◽  
National Education ◽  
Fit Indexes

In this study, it was aimed to adapt the Technology Proficiency Self-Assessment Scale for 21st Century Learning (TPSA C-21), developed by Christensen and Knezek, into Turkey's conditions. The study sample consisted of 606 teachers working in 58 public schools affiliated to the Ministry of National Education in Bartın province. The original scale had a six-factor structure consisting of 34 items. In the adaptation stage, the Turkish version of the scale was initially created and then retranslated back into the source language. Next, the scale items were evaluated by linguists. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to determine the construct validity of the scale. As a result of the EFA, some of the items having inappropriate values were excluded from the scale and finally a structure with 24 items and four factors named "E-mail", "WWW", "Integrated Applications", and "Teaching with Technology" was obtained. The results of the CFA showed that the factor structure had adequate fit indexes. For reliability, internal consistency and test-retest correlation coefficients of the whole scale and its sub-dimensions were examined and these values were found to be acceptable. After all, this scale has become a measurement tool that can be used for research which enables educators to make relevant inferences about technology proficiency in 21st century learning.

scholarly journals Factor Analysis of the Texas Functional Living Scale in an Outpatient Clinical Sample

2019 ◽  
Vol 35 (1) ◽  
pp. 116-121 ◽  
Author(s):  
Deborah A Lowe ◽  
Christopher M Nguyen ◽  
Christopher T Copeland ◽  
John F Linck
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Clinical Sample ◽  
Functional Abilities ◽  
Five Factors ◽  
Principal Axis Factor Analysis ◽  
Education Effects ◽  
Military Veteran ◽  
Math Concepts ◽  
Promax Rotation

Abstract Objective Prior factor analysis of the Texas Functional Living Scale (TFLS), a performance-based measure of functional abilities, in a military veteran sample supported four factors discrepant from the published subscales. This study analyzed TFLS factor structure in a non-veteran clinical sample. Method Two hundred seventy adult outpatients completed the TFLS during neuropsychological evaluation. Principal axis factor analysis with oblique promax rotation was conducted with age and education effects partialed out. Results Parallel analysis indicated five factors for extraction that accounted for a combined 48% of the variance. The first factor independently explained 26% of the total variance. Inspection of factor loadings suggested the following factor interpretations: complex calculations/time, complex visual search, praxis, memory, and basic calculations/math concepts. Five items did not significantly load onto any of the factors. Conclusions Current results did not entirely correspond to the published subscales or prior results in a veteran sample. Further clarification of the TFLS factor structure is warranted.

Sign in / Sign up

Export Citation Format

Share Document