Exploratory factor analysis of large data matrices

Nickolay T. Trendafilov; Sara Fontanella

doi:10.1002/sam.11393

Exploratory Factor Analysis of Data Matrices With More Variables Than Observations

Journal of Computational and Graphical Statistics ◽

10.1198/jcgs.2011.09211 ◽

2011 ◽

Vol 20 (4) ◽

pp. 874-891 ◽

Cited By ~ 17

Author(s):

Nickolay T. Trendafilov ◽

Steffen Unkel

Keyword(s):

Factor Analysis ◽

Exploratory Factor Analysis ◽

Data Matrices

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Measuring Impact of Stress in Sandwich Generation Caring for Demented Parents

GeroPsych ◽

10.1024/1662-9647/a000114 ◽

2014 ◽

Vol 27 (4) ◽

pp. 171-179 ◽

Cited By ~ 6

Author(s):

Laurence M. Solberg ◽

Lauren B. Solberg ◽

Emily N. Peterson

Keyword(s):

Factor Analysis ◽

Exploratory Factor Analysis ◽

Social Worker ◽

Resource Availability ◽

Well Being ◽

Sandwich Generation ◽

Item Questionnaire ◽

Significant Difference ◽

Adult Caregivers ◽

The Impact

Stress in caregivers may affect the healthcare recipients receive. We examined the impact of stress experienced by 45 adult caregivers of their elderly demented parents. The participants completed a 32-item questionnaire about the impact of experienced stress. The questionnaire also asked about interventions that might help to reduce the impact of stress. After exploratory factor analysis, we reduced the 32-item questionnaire to 13 items. Results indicated that caregivers experienced stress, anxiety, and sadness. Also, emotional, but not financial or professional, well-being was significantly impacted. There was no significant difference between the impact of caregiver stress on members from the sandwich generation and those from the nonsandwich generation. Meeting with a social worker for resource availability was identified most frequently as a potentially helpful intervention for coping with the impact of stress.

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The Hierarchical Model of Exercise Dependence

Journal of Individual Differences ◽

10.1027/1614-0001/a000172 ◽

2015 ◽

Vol 36 (4) ◽

pp. 247-257 ◽

Cited By ~ 1

Author(s):

Gayatri Kotbagi ◽

Laurence Kern ◽

Lucia Romo ◽

Ramesh Pathare

Keyword(s):

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Physical Exercise ◽

Exploratory Factor Analysis ◽

Convergent Validity ◽

Large Population ◽

Assessment Tools ◽

Negative Consequences ◽

Confirmatory Factor ◽

Preliminary Version

Abstract. Physical exercise when done excessively may have negative consequences on physical and psychological wellbeing. There exist many scales to measure this phenomenon. The purpose of this article is to create a scale measuring the problematic practice of physical exercise (PPPE Scale) by combining two assessment tools already existing in the field of exercise dependency but anchored in different approaches (EDS-R and EDQ). This research consists of three studies carried out on three independent sample populations. The first study (N = 341) tested the construct validity (exploratory factor analysis); the second study (N = 195) tested the structural validity (confirmatory factor analysis) and the third study (N = 104) tested the convergent validity (correlations) of the preliminary version of the PPPE scale. Exploratory factor analysis identified six distinct dimensions associated with exercise dependency. Furthermore, confirmatory factor analysis validated a second order model consisting of 25 items with six dimensions and four sub-dimensions. The convergent validity of this scale with other constructs (GLTEQ, EAT26, and The Big Five Inventory [BFI]) is satisfactory. The preliminary version of the PPPE must be administered to a large population to refine its psychometric properties and develop scoring norms.

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Subclinical Sadism and the Dark Triad

Journal of Individual Differences ◽

10.1027/1614-0001/a000284 ◽

2019 ◽

Vol 40 (3) ◽

pp. 127-133 ◽

Cited By ~ 6

Author(s):

Laura K. Johnson ◽

Rachel A. Plouffe ◽

Donald H. Saklofske

Keyword(s):

Factor Analysis ◽

University Students ◽

Personality Traits ◽

Exploratory Factor Analysis ◽

Convergent Validity ◽

Dark Triad ◽

Antisocial Personality ◽

Factor Solution ◽

Online Study ◽

Antisocial Personality Traits

Abstract. The Dark Triad is a constellation of three antisocial personality traits: Machiavellianism, narcissism, and psychopathy. Recently, researchers have introduced a “Dark Tetrad” that includes subclinical sadism, although others suggest considerable overlap between psychopathy and sadism. To clarify the position of sadism within the Dark Triad, an online study was conducted with 615 university students. Exploratory factor analysis revealed that a six-factor solution fit the data best, representing Machiavellianism, psychopathy, physical sadism, verbal sadism, narcissism, and vicarious sadism. Furthermore, convergent validity was supported through sadism’s correlations with the HEXACO personality traits. The results support sadism’s inclusion within the Dark Tetrad as a unique construct but with some conceptual overlap with psychopathy.

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Equivalent Factorial Structure of the Brief Symptom Inventory (BSI) in Clinical and Nonclinical Jordanian Populations

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000016 ◽

2010 ◽

Vol 26 (2) ◽

pp. 116-121 ◽

Cited By ~ 6

Author(s):

Fawzi S. Daoud ◽

Amjed A. Abojedi

Keyword(s):

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Exploratory Factor Analysis ◽

Clinical Sample ◽

Brief Symptom Inventory ◽

Factorial Structure ◽

Symptom Inventory ◽

Confirmatory Factor ◽

Item Checklist ◽

General Psychological Distress

This study investigates the equivalent factorial structure of the Brief Symptom Inventory (BSI) in clinical and nonclinical Jordanian populations, using both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The 53-item checklist was administered to 647 nonclinical participants and 315 clinical participants. Eight factors emerged from the exploratory factor analysis (EFA) for the nonclinical sample, and six factors emerged for the clinical sample. When tested by parallel analysis (PA) and confirmatory factor analysis (CFA), the results reflected a unidimensional factorial structure in both samples. Furthermore, multigroup CFA showed invariance between clinical and nonclinical unidimensional models, which lends further support to the evidence of the unidimensionality of the BSI. The study suggests that the BSI is a potentially useful measure of general psychological distress in clinical and nonclinical population. Ideas for further research are recommended.

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A Comparison of Simple Structure Rotation Criteria in Temporal Exploratory Factor Analysis for Event-Related Potential Data

Methodology ◽

10.1027/1614-2241/a000175 ◽

2019 ◽

Vol 15 (Supplement 1) ◽

pp. 43-60 ◽

Cited By ~ 3

Author(s):

Florian Scharf ◽

Steffen Nestler

Keyword(s):

Factor Analysis ◽

Exploratory Factor Analysis ◽

Simple Structure ◽

Ground Truth ◽

Event Related Potential ◽

Suitable Alternative ◽

Factor Rotation ◽

Rotation Methods ◽

Simple Structure Rotation ◽

Temporal Overlap

Abstract. It is challenging to apply exploratory factor analysis (EFA) to event-related potential (ERP) data because such data are characterized by substantial temporal overlap (i.e., large cross-loadings) between the factors, and, because researchers are typically interested in the results of subsequent analyses (e.g., experimental condition effects on the level of the factor scores). In this context, relatively small deviations in the estimated factor solution from the unknown ground truth may result in substantially biased estimates of condition effects (rotation bias). Thus, in order to apply EFA to ERP data researchers need rotation methods that are able to both recover perfect simple structure where it exists and to tolerate substantial cross-loadings between the factors where appropriate. We had two aims in the present paper. First, to extend previous research, we wanted to better understand the behavior of the rotation bias for typical ERP data. To this end, we compared the performance of a variety of factor rotation methods under conditions of varying amounts of temporal overlap between the factors. Second, we wanted to investigate whether the recently proposed component loss rotation is better able to decrease the bias than traditional simple structure rotation. The results showed that no single rotation method was generally superior across all conditions. Component loss rotation showed the best all-round performance across the investigated conditions. We conclude that Component loss rotation is a suitable alternative to simple structure rotation. We discuss this result in the light of recently proposed sparse factor analysis approaches.

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