A Simulation of Factor Analytic Reliability Varying Sample Size and Number of Variables

1979 ◽  
Vol 45 (2) ◽  
pp. 471-478 ◽  
Author(s):  
George E. Manners ◽  
Donald H. Brush
Keyword(s):  
Sample Size ◽  
Factor Structure ◽  
Random Sampling ◽  
Factor Model ◽  
Sampling Error ◽  
Factor Models ◽  
Factor Analytic ◽  
Number Of Factors ◽  
Alpha Factor ◽  
Analytic Models

An examination of four factor analytic models employing random sampling experiments is undertaken using a methodology and hypothetical population factor structure first employed by Browne (2). The factor models are each explored under four separate conditions, varying sample size and number of variables. Under these limited conditions, it is argued that there are no practical differences among the factor models considered with respect to sampling error in the absence of a Heywood variable. However, with respect to the ability of each model to capture, early and at convergence, the number of factors in the population, the alpha factor model is shown to have the greatest reliability.

scholarly journals Identification of Facet Models by Means of Factor Rotation: A Simulation Study and Data Analysis of a Test for the Berlin Model of Intelligence Structure

2020 ◽  
Vol 80 (5) ◽  
pp. 995-1019
Author(s):  
André Beauducel ◽  
Martin Kersting
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Sample Size ◽  
Simulation Study ◽  
Simple Structure ◽  
Factor Models ◽  
Minimum Entropy ◽  
Factor Rotation ◽  
Number Of Factors ◽  
Target Rotation

We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor analysis with subsequent Varimax, Equamax, Parsimax, Factor Parsimony, Tandem I, Tandem II, Infomax, and McCammon’s minimum entropy rotation. Samples drawn from oblique populations were submitted to factor analysis with subsequent Geomin rotation and a Promax-based Tandem II rotation. As a benchmark, we investigated a target rotation of the sample loadings toward the corresponding faceted population loadings. The three conditions were sample size ( n = 400, 1,000), number of factors ( q = 4-12), and main loading size ( l = .40, .50, .60). For less than six orthogonal factors Infomax and McCammon’s minimum entropy rotation and for six and more factors Tandem II rotation yielded the highest congruence of sample loading matrices with faceted population loading matrices. For six and more oblique factors Geomin rotation and a Promax-based Tandem II rotation yielded the highest congruence with faceted population loadings. Analysis of data of 393 participants that performed a test for the Berlin Model of Intelligence Structure revealed that the faceted structure of this model could be identified by means of a Promax-based Tandem II rotation of task aggregates corresponding to the cross-products of the facets. Implications for the identification of faceted models by means of factor rotation are discussed.

scholarly journals What is the p-factor of psychopathology? Some risks of general factor modeling

2017 ◽  
Vol 27 (6) ◽  
pp. 759-773 ◽  
Author(s):  
Riet van Bork ◽  
Sacha Epskamp ◽  
Mijke Rhemtulla ◽  
Denny Borsboom ◽  
Han L. J. van der Maas
Keyword(s):  
Factor Structure ◽  
Factor Model ◽  
Common Factor ◽  
Psychological Disorders ◽  
General Factor ◽  
Personality Research ◽  
Factor Analytic ◽  
History Of ◽  
Factor Modeling ◽  
P Factor

Recent research has suggested that a range of psychological disorders may stem from a single underlying common factor, which has been dubbed the p-factor. This finding may spur a line of research in psychopathology very similar to the history of factor modeling in intelligence and, more recently, personality research, in which similar general factors have been proposed. We point out some of the risks of modeling and interpreting general factors, derived from the fields of intelligence and personality research. We argue that: (a) factor-analytic resolution, i.e., convergence of the literature on a particular factor structure, should not be expected in the presence of multiple highly similar models; and (b) the true underlying model may not be a factor model at all, because alternative explanations can account for the correlational structure of psychopathology.

Factor Structure of the New Environmental Paradigm Scale: Evidence from an Urban Sample in Southern California

2004 ◽  
Vol 94 (1) ◽  
pp. 125-130 ◽  
Author(s):  
Chang-Ho C. Ji
Keyword(s):  
Factor Structure ◽  
Southern California ◽  
Factor Model ◽  
Factor Models ◽  
Two Factors ◽  
Urban Sample ◽  
Original Scale ◽  
The One ◽  
Three Factor ◽  
New Environmental Paradigm

This study examined the factor structure of the New Environmental Paradigm Scale using responses from 261 urban subjects from southern California. The analysis yielded findings inconsistent with many previous studies of the original scale. This study supported an 8-item two-factor model of the scale rather than the one-factor and three-factor models proposed earlier. A subsequent validation study provides evidence for this short form's validity, as the two factors were predictive of commitment to preservation of nature.

scholarly journals Factor Structure of the “Top Ten” Positive Emotions of Barbara Fredrickson

2021 ◽  
Vol 12 ◽  
Author(s):  
Leopold Helmut Otto Roth ◽  
Anton-Rupert Laireiter
Keyword(s):  
Positive Psychology ◽  
Factor Structure ◽  
Positive Emotions ◽  
Factor Model ◽  
Factor Models ◽  
Cluster Solution ◽  
Special Role ◽  
Single Factor ◽  
Theoretical Significance

In order to contribute to the consolidation in the field of Positive Psychology, we reinvestigated the factor structure of top 10 positive emotions of Barbara Fredrickson. Former research in experimental settings resulted in a three-cluster solution, which we tested with exploratory and confirmatory methodology against different factor models. Within our non-experimental data (N = 312), statistical evidence is presented, advocating for a single factor model of the 10 positive emotions. Different possible reasons for the deviating results are discussed, as well as the theoretical significance to various subfields in Positive Psychology (e.g., therapeutical interventions). Furthermore, the special role of awe within the study and its implications for further research in the field are discussed.

Polish adaptation of the Geneva Emotional Music Scale: Factor structure and reliability

2020 ◽  
pp. 030573562092747
Author(s):  
Maria Chełkowska-Zacharewicz ◽  
Maciej Janowski
Keyword(s):  
Factor Structure ◽  
Factor Model ◽  
High Reliability ◽  
Factor Solution ◽  
Linguistic Differences ◽  
Number Of Factors ◽  
Confirmatory Factor ◽  
Measurement Tools ◽  
Polish Adaptation ◽  
Being Moved

One of the most popular measurement tools used in music-emotion studies is the Geneva Emotional Music Scale (GEMS). The authors conducted a series of studies on Polish samples to confirm the factor structure and the reliability of the Polish adaptation of the GEMS (GEMS-PL). Study 1 ( n = 262) revealed in the confirmatory factor analysis a good fit for both 9- and 10-factor models, with an indication to a better fit of the 10-factor solution. Additional statistical analyses were performed to explore the differences in the number of factors between the GEMS-PL and the original GEMS. Study 2 ( n = 944) performed in laboratory and Internet settings, confirmed the 10-factor model and revealed that all obtained scales had high reliability. The GEMS-PL scales peacefulness, tenderness, tension, joyful activation, nostalgia, sadness, power, transcendence, and wonder correspond with the original GEMS. The additional scale being moved abstracted from the sadness factor is discussed in the context of possible linguistic differences and growing literature on the state of being moved.

scholarly journals Examining the Factor Structure of the Pittsburgh Sleep Quality Index in a Multi-Ethnic Working Population in Singapore

2019 ◽  
Vol 16 (23) ◽  
pp. 4590
Author(s):  
Dunleavy ◽  
Bajpai ◽  
Tonon ◽  
Chua ◽  
Cheung ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Sleep Quality ◽  
Factor Structure ◽  
Quality Index ◽  
Goodness Of Fit ◽  
Pittsburgh Sleep Quality Index ◽  
Factor Model ◽  
Factor Models ◽  
Working Population ◽  
Three Factor

The Pittsburgh Sleep Quality Index (PSQI) is a widely used measure for assessing sleep impairment. Although it was developed as a unidimensional instrument, there is much debate that it contains multidimensional latent constructs. This study aims to investigate the dimensionality of the underlying factor structure of the PSQI in a multi-ethnic working population in Singapore. The PSQI was administered on three occasions (baseline, 3 months and 12 months) to full-time employees participating in a workplace cohort study. Exploratory factor analysis (EFA) investigated the latent factor structure of the scale at each timepoint. Confirmatory factor analysis (CFA) evaluated the model identified by EFA, and additionally evaluated it against a single factor and a three-factor model. The EFA identified a two-factor model with similar internal consistency and goodness-of-fit across each timepoint. In the CFA, the two- and three-factor models were both superior to the unidimensional model. The two- and three-factor models of the PSQI were reliable, consistent and provided similar goodness-of-fit over time, and both models were superior to the unidimensional measure. We recommend using the two-factor model to assess sleep characteristics in working populations in Singapore, given that it performs as well as the three-factor model and is simpler compared to the latter.

Factor Structure of the Memory for Intentions Test (MIsT) in Samples of Older Adults and People with HIV Disease

2021 ◽  
Author(s):  
Kelli Sullivan ◽  
Matthew Gallagher ◽  
Romola S. Bucks ◽  
Michael Weinborn ◽  
Steven Paul Woods
Keyword(s):  
Older Adults ◽  
Factor Structure ◽  
Prospective Memory ◽  
Factor Model ◽  
Factor Models ◽  
Community Dwelling ◽  
People Living With Hiv ◽  
Hiv Disease ◽  
Memory For Intentions ◽  
The One

Objective: The Memory for Intentions Test (MIsT) is a clinical measure of prospective memory that has strong evidence for convergent, discriminative, and ecological validity. This study evaluates the latent structure of the MIsT among two samples who may experience prospective memory deficits: older adults and people living with HIV disease. Participants and Methods: Study participants included 303 people with HIV disease (ages 18-67) and 267 community-dwelling older adults (ages 50-91). Confirmatory factor analyses of the MIsT were conducted separately in each sample. We evaluated a one-factor model, as well as three two-factor models with the MIsT items loading onto each factor based on cue type, delay interval, or response modality, respectively. Results: The one-factor model provided the best (and most parsimonious) fit to the data in both study samples. All two-factor models also demonstrated good fit statistics, although correlations between the two factors in each model were high and none of the two-factor models provided a significantly better fit than the one-factor model. Conclusions: Results provide support for the factor structure of the MIsT in older adults and people with HIV disease. A total score for the MIsT provides the most parsimonious solution, although available evidence and theory also support the use of subscales (e.g., cue type). Future studies of the MIsT would be useful to determine its psychometrics in different clinical populations.

Factor Analysis (R-Technique) of Ipsatized Data May Be Misleading

1981 ◽  
Vol 49 (2) ◽  
pp. 643-647 ◽  
Author(s):  
Todd M. Davis ◽  
Brad Chissom
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Empirical Support ◽  
Factor Analytic ◽  
Ipsative Data ◽  
Number Of Factors

Despite cautions in the literature against factor analyzing (R-technique) non-independent or ipsative data, several such studies have reported apparently meaningful results. This paper examines the results of factor analysis for six different sets of contrived data with known properties. Comparisons between the known factor structure and obtained results suggest that the number of factors initially extracted will be significantly under- or overestimated and that less than 60% of factored items will be correctly loaded. These findings provide empirical support for rational notes of caution in the use of factor analytic procedures as applied to ipsative data.

Currency Factors

2021 ◽  
Author(s):  
Arash Aloosh ◽  
Geert Bekaert
Keyword(s):  
Factor Structure ◽  
Mean Squared Error ◽  
Factor Model ◽  
Block Structure ◽  
Emerging Market ◽  
Factor Models ◽  
The Other ◽  
Squared Error ◽  
Trading Volumes ◽  
Better Than

We examine the ability of existing and new factor models to explain the comovements of G10 currency changes, measured using “currency baskets.” A clustering technique reveals a clear two-block structure in currency comovements, with the first block containing mostly the dollar currencies and the other the European currencies. A factor model incorporating this “clustering” factor and two additional factors, a commodity currency factor and a “world” factor based on trading volumes, fits currency basket correlations much better than extant factors, such as value and carry, do. In particular, it explains on average about 60% of currency variation and generates a root mean squared error relative to sample correlations of only 0.11. The model also fits comovements in emerging market currencies well. Economically, the correlations between currency baskets underlying the factor structure are inversely related to the physical distances between countries. This paper was accepted by Kay Giesecke, finance.

Sign in / Sign up

Export Citation Format

Share Document