Rethinking the Exploration of Dichotomous Data
Mokken Scale Analysis Versus Factorial Analysis
Bibliographic Data
| ID | 2330902 |
|---|---|
| Authors | Mirko Antino (0000-0001-7578-4899, Instituto Universitário de Lisboa, Business Research Unit, Lisboa, Portugal, corresponding author), Jesús María Alvarado-Izquierdo (0000-0003-4780-0147, Universidad Complutense de Madrid, corresponding author), Jesús M Alvarado, Rodrigo Asún (0000-0003-0903-1789, University of Chile), Rodrigo A Asún (University of Chile), Paul D Bliese (0000-0002-5384-8879, University of South Carolina), Paul Bliese (University of South Carolina) |
| Year | 2020 |
| Volume | 49 |
| Issue | 4 |
| Pages | 839-867 |
| Publication date | 2020-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methods & Research (JOURNAL) |
| Journal identifiers | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0049124118769090 |
| OpenAlex | W2800543278 |
| Language | EN |
| Citations received | 1 |
| References cited | 56 |
The need to determine the correct dimensionality of theoretical constructs and generate valid measurement instruments when underlying items are categorical has generated a significant volume of research in the social sciences. This article presents two studies contrasting different categorical exploratory techniques. The first study compares Mokken scale analysis (MSA) and two-factor-based exploratory techniques for noncontinuous variables: item factor analysis and Normal Ogive Harmonic Analysis Robust Method (NOHARM). Comparisons are conducted across techniques and in reference to the common principal component analysis model using simulated data under conditions of two-dimensionality with different degrees of correlation ( r = .0 to .6). The second study shows the theoretical and practical results of using MSA and NOHARM (the factorial technique which functioned best in the first study) on two nonsimulated data sets. The nonsimulated data are particularly interesting because MSA was used to solve a theoretical debate. Based on the results from both studies, we show that the ability of NOHARM to detect dimensionality and scalability is similar to MSA when the data comprise two uncorrelated latent dimensions; however, NOHARM is preferable when data are drawn from instruments containing latent dimensions weakly or moderately correlated. This article discusses the theoretical and practical implications of these findings
Categorical variable · Curse of dimensionality · Data mining · Econometrics · Exploratory data analysis · Exploratory factor analysis · Factor analysis · Factorial · Principal component analysis · Scale (ratio) · Statistics · Structural equation modeling · Uncorrelated · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Sensory Analysis and Statistical Methods · Statistical Methods and Applications
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Component Analysis versus Common Factor Analysis
Goodness of Fit in Item Response Models
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The Institutional Power of Western European Parliaments
Mokken Scale Analysis
The Two Faces of Government Spending
Direct Democracy and Political Efficacy Reconsidered
Public Attitudes toward Government Spending
Standard Errors and Confidence Intervals for Scalability Coefficients in Mokken Scale Analysis Using Marginal Models
Factoring items and factoring scales are different
Measuring Internal Political Efficacy in the 1988 National Election Study
Developing Multidimensional Likert Scales Using Item Factor Analysis
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |