scholarly journals Proportion of Indicator Common Variance Due to a Factor as an Effect Size Statistic in Revised Parallel Analysis

2018 ◽  
Vol 79 (1) ◽  
pp. 85-107 ◽  
Author(s):  
Yan Xia ◽  
Samuel B. Green ◽  
Yuning Xu ◽  
Marilyn S. Thompson
Keyword(s):  
Factor Analysis ◽  
Effect Size ◽  
Past Research ◽  
Parallel Analysis ◽  
Large Sample Size ◽  
Common Variance ◽  
Minimum Rank ◽  
Additional Information ◽  
Number Of Factors ◽  
Principal Axis Factor Analysis

Past research suggests revised parallel analysis (R-PA) tends to yield relatively accurate results in determining the number of factors in exploratory factor analysis. R-PA can be interpreted as a series of hypothesis tests. At each step in the series, a null hypothesis is tested that an additional factor accounts for zero common variance among measures in the population. Integration of an effect size statistic—the proportion of common variance (PCV)—into this testing process should allow for a more nuanced interpretation of R-PA results. In this article, we initially assessed the psychometric qualities of three PCV statistics that can be used in conjunction with principal axis factor analysis: the standard PCV statistic and two modifications of it. Based on analyses of generated data, the modification that considered only positive eigenvalues ([Formula: see text]) overall yielded the best results. Next, we examined PCV using minimum rank factor analysis, a method that avoids the extraction of negative eigenvalues. PCV with minimum rank factor analysis generally did not perform as well as [Formula: see text], even with a relatively large sample size of 5,000. Finally, we investigated the use of [Formula: see text] in combination with R-PA and concluded that practitioners can gain additional information from [Formula: see text] and make more nuanced decision about the number of factors when R-PA fails to retain the correct number of factors.

New Effect Sizes for Tests of Categorical Moderation and Differential Prediction

2016 ◽  
Vol 20 (4) ◽  
pp. 639-664 ◽  
Author(s):  
Christopher D. Nye ◽  
Paul R. Sackett
Keyword(s):  
Effect Size ◽  
Past Research ◽  
Well Being ◽  
Psychological Research ◽  
Effect Sizes ◽  
Categorical Variables ◽  
Differential Prediction ◽  
Large Sample ◽  
Additional Information ◽  
Large Databases

Moderator hypotheses involving categorical variables are prevalent in organizational and psychological research. Despite their importance, current methods of identifying and interpreting these moderation effects have several limitations that may result in misleading conclusions about their implications. This issue has been particularly salient in the literature on differential prediction where recent research has suggested that these limitations have had a significant impact on past research. To help address these issues, we propose several new effect size indices that provide additional information about categorical moderation analyses. The advantages of these indices are then illustrated in two large databases of respondents by examining categorical moderation in the prediction of psychological well-being and the extent of differential prediction in a large sample of job incumbents.

scholarly journals Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis

2019 ◽  
Vol 80 (2) ◽  
pp. 217-241 ◽  
Author(s):  
W. Holmes Finch
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Optimal Number ◽  
Parallel Analysis ◽  
Fit Indices ◽  
Number Of Factors ◽  
Indicator Variables ◽  
Model Fit Indices ◽  
Fit Index ◽  
Normally Distributed

Exploratory factor analysis (EFA) is widely used by researchers in the social sciences to characterize the latent structure underlying a set of observed indicator variables. One of the primary issues that must be resolved when conducting an EFA is determination of the number of factors to retain. There exist a large number of statistical tools designed to address this question, with none being universally optimal across applications. Recently, researchers have investigated the use of model fit indices that are commonly used in the conduct of confirmatory factor analysis to determine the number of factors to retain in EFA. These results have yielded mixed results, appearing to be effective when used in conjunction with normally distributed indicators, but not being as effective for categorical indicators. The purpose of this simulation study was to compare the performance of difference values for several fit indices as a method for identifying the optimal number of factors to retain in an EFA, with parallel analysis, which is one of the most reliable such extant methods. Results of the simulation demonstrated that the use of fit index difference values outperformed parallel analysis for categorical indicators, and for normally distributed indicators when factor loadings were small. Implications of these findings are discussed.

scholarly journals DETERMINANTS OF BRAND PREFERENCE OF COMMERCIAL THREE WHEELER PASSENGER AUTO RICKSHAW DRIVERS IN ADAMA CITY, ETHIOPIA

2019 ◽  
Vol 7 (1) ◽  
pp. 484-492
Author(s):  
Mohd Arif Shaikh ◽  
Devi Prasad U ◽  
Pagadala Sugandha Devi
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Parallel Analysis ◽  
Primary Data ◽  
Brand Preference ◽  
Scree Plot ◽  
Component Solution ◽  
Number Of Factors ◽  
Eigen Values ◽  
Relevant Factors

Purpose of the study: The aim of this study is to find out the factors that influence drivers of three wheeler auto rickshaw in their brand preference towards different brands of commercial three wheeler passenger auto rickshaw in Adama City, Ethiopia Methodology:  Primary data was collected from 500 auto drivers using a pilot tested questionnaire consisting of 40 questions. Cronbach’s alpha measure was used to test constructs reliability and in order to identify brand preference, exploratory factor analysis and parallel analysis was conducted. Main Findings: PCA revealed that there are 11 factors whose Eigen values are above 1. A look at scree plot indicated that there is a need to reconsider the number of factors to be used for further analysis. This decision was supported by Parallel analysis and 8 relevant factors identified. This 8 component solution explained 64.25 % of the variance. Applications of this study: Identification of determinants of brand preference can be used by three wheeler passenger auto manufacturers and distributors in Ethiopia Novelty/Originality of this study: There is no study conducted on drivers brand preference of three wheeler passenger auto rickshaws in Ethiopia.

scholarly journals Factor Retention in Exploratory Factor Analysis With Missing Data

2021 ◽  
pp. 001316442110220
Author(s):  
David Goretzko
Keyword(s):  
Factor Analysis ◽  
Missing Data ◽  
Random Forest ◽  
Exploratory Factor Analysis ◽  
Missing Values ◽  
Small Sample ◽  
Parallel Analysis ◽  
Number Of Factors ◽  
The Impact ◽  
Comparison Data

Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process. Hence, in this study, we evaluated the performance of different factor retention criteria—the Factor Forest, parallel analysis based on a principal component analysis as well as parallel analysis based on the common factor model and the comparison data approach—in combination with different missing data methods, namely an expectation-maximization algorithm called Amelia, predictive mean matching, and random forest imputation within the multiple imputations by chained equations (MICE) framework as well as pairwise deletion with regard to their accuracy in determining the number of factors when data are missing. Data were simulated for different sample sizes, numbers of factors, numbers of manifest variables (indicators), between-factor correlations, missing data mechanisms and proportions of missing values. In the majority of conditions and for all factor retention criteria except the comparison data approach, the missing data mechanism had little impact on the accuracy and pairwise deletion performed comparably well as the more sophisticated imputation methods. In some conditions, especially small-sample cases and when comparison data were used to determine the number of factors, random forest imputation was preferable to other missing data methods, though. Accordingly, depending on data characteristics and the selected factor retention criterion, choosing an appropriate missing data method is crucial to obtain a valid estimate of the number of factors to extract.

Number of Factors Decision: Parallel Analysis Is Not the Panacea

2006 ◽  
Author(s):  
Jinyan Fan ◽  
Felix James Lopez ◽  
Jennifer Nieman ◽  
Robert C. Litchfield ◽  
Robert S. Billings
Keyword(s):  
Parallel Analysis ◽  
Number Of Factors

scholarly journals A less evaluative measure of Big Five personality: Comparison of structure and criterion validity

2021 ◽  
pp. 089020702110129
Author(s):  
Joshua K Wood ◽  
Jeromy Anglim ◽  
Sharon Horwood
Keyword(s):  
Big Five ◽  
Personality Assessment ◽  
Blood Donation ◽  
Past Research ◽  
Criterion Validity ◽  
Communication Styles ◽  
General Factor ◽  
Big Five Personality ◽  
Large Sample Size ◽  
Academic Grades

Researchers and practitioners have long been concerned about detrimental effects of socially desirable responding on the structure and criterion validity of personality assessments. The current research examined the effect of reducing evaluative item content of a Big Five personality assessment on test structure and criterion validity. We developed a new public domain measure of the Big Five called the Less Evaluative Five Factor Inventory (LEFFI), adapted from the standard 50-item IPIP NEO, and intended to be less evaluative. Participants ( n = 3164) then completed standard (IPIP) and neutralized (LEFFI) measures of personality. Criteria were also collected, including academic grades, age, sex, smoking, alcohol consumption, exercise, protesting, religious worship, music preferences, dental hygiene, blood donation, other-rated communication styles, other-rated HEXACO personality, and cognitive ability (ICAR). Evaluativeness of items was reduced in the neutralized measure. Cronbach's alpha and test-retest reliability were maintained. Correlations between the Big Five were reduced in the neutralized measure and criterion validity was similar or slightly reduced in the neutralized measure. The large sample size and use of objective criteria extend past research. The study also contributes to debates about whether the general factor of personality and agreement with socially desirable content reflect substance or bias.

Development of the Adherence to Healthy Behaviors Scale

2021 ◽  
pp. 105477382098862
Author(s):  
Chen-Hui Huang ◽  
Dhea Natashia ◽  
Tzu-Chia Lin ◽  
Miaofen Yen
Keyword(s):  
Factor Analysis ◽  
Protective Factor ◽  
Model Fit ◽  
Convenience Sample ◽  
Healthy Behaviors ◽  
Confirmatory Factor ◽  
Reliable Instrument ◽  
Principal Axis Factor Analysis ◽  
Southern Taiwan ◽  
Test Retest Reliability

Adherence to healthy behaviors is a protective factor in the disease progression of chronic kidney disease (CKD). Measuring adherence can lead to the recognition of unhealthy behaviors and the suggestion of programs to prevent poor health outcomes. An assessment measurement for patients with CKD not requiring dialysis was developed and psychometrically tested. A convenience sample ( n = 330) of patients with CKD attending a nephrology clinic in southern Taiwan completed the 13-item Adherence to Healthy Behaviors Scale (AHBS). A principal axis factor analysis and a parallel analysis demonstrated a three-factor structure accounting for 47.16% of the total variance. Confirmatory factor analysis indicated a good model fit. The criterion-related validity was adequate ( r = .51; p < .000), with a Cronbach’s alpha of .70; the test-retest reliability demonstrated good stability ( r = .70; p < .000). The AHBS is a valid, reliable instrument to assess adherence to healthy behaviors among patients with CKD.

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