From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Bjarne Schmalbach ◽  
Markus Zenger ◽  
Michalis P. Michaelides ◽  
Karin Schermelleh-Engel ◽  
Andreas Hinz ◽  
...  
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Self Regulation ◽  
Model Fit ◽  
Self Report ◽  
External Criterion ◽  
Common Factor Model ◽  
Random Intercept ◽  
Using Data ◽  
Core Self

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

scholarly journals WorseThanMeasurementError

2018 ◽  
Author(s):  
Mijke Rhemtulla ◽  
Riet van Bork ◽  
Denny Borsboom
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Factor Models ◽  
Model Fit ◽  
Structural Parameter ◽  
Parameter Estimates ◽  
Psychological Variables ◽  
Common Factor Model ◽  
Composite Models ◽  
Common Factor Models

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model, such that they consist of construct variance plus error. In this paper, we explore what happens when a set of items that are not generated from a common factor construct model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical re-analysis, we show that (1) common factor models tend to produce extremely biased and highly variable structural parameter estimates when the population model is not a common factor model; (2) model fit is a poor indicator of the degree of bias; and (3) composite models are sometimes more reliable than common factor models under alternative measurement structures, though they also lead to unacceptably bad solutions in some cases.

Structural Equation Modeling with Factors and Composites

2017 ◽  
Vol 13 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Factor Model ◽  
Common Factor ◽  
Equation Modeling ◽  
A Algorithm ◽  
Multivariate Statistical ◽  
Full Information Maximum Likelihood ◽  
Common Factor Model ◽  
Path Coefficients

Recent methodological developments building on partial least squares (PLS) techniques and related ideas have significantly contributed to bridging the gap between factor-based and composite-based structural equation modeling (SEM) methods. PLS-SEM is extensively used in the field of e-collaboration, as well as in many other fields where multivariate statistical analyses are employed. The author compares results obtained with four methods: covariance-based SEM with full information maximum likelihood (FIML), factor-based SEM with common factor model assumptions (FSEM1), factor-based SEM building on the PLS Regression algorithm (FSEM2), and PLS-SEM employing the Mode A algorithm (PLSA). The comparison suggests that FSEM1 yields path coefficients and loadings that are very similar to FIML's; and that FSEM2 yields path coefficients that are very similar to FIML's and loadings that are very similar to PLSA's.

Analysis of variances and covariances is misleading as a guide to a common factor model

Intelligence ◽  
1981 ◽  
Vol 5 (2) ◽  
pp. 157-163 ◽  
Author(s):  
Lloyd G. Humphreys ◽  
Randolph K. Park
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Common Factor Model

A modified common factor model for modelling mortality jointly for both sexes

2020 ◽  
Vol 37 (2) ◽  
pp. 181-212
Author(s):  
Kenneth Wong ◽  
Jackie Li ◽  
Sixian Tang
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Common Factor Model

Validation of the Schutte Self-Report Emotional Intelligence Scale With American College Students

2016 ◽  
Vol 36 (2) ◽  
pp. 175-181 ◽  
Author(s):  
Xiaopeng Gong ◽  
Sharon E. Paulson
Keyword(s):  
Emotional Intelligence ◽  
Online Survey ◽  
Factor Model ◽  
Current Data ◽  
Model Fit ◽  
Self Report ◽  
Intelligence Scale ◽  
Confirmatory Factor Analyses ◽  
American Students ◽  
Confirmatory Factor

The current study examined the factor structure of the Schutte Self-Report Emotional Intelligence (SSREI) scale with an American college sample ( n = 404, 322 females, 88.9% Whites). Data were collected through an online survey, and confirmatory factor analyses were conducted to test several proposed factor models from previous studies. The results showed that the Ng et al. two-level factor model fit the current data best. Implications of the study and the usefulness of SSREI scale among American students were discussed.

scholarly journals The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 80
Author(s):  
Uditha Balasooriya ◽  
Johnny Siu-Hang Li ◽  
Jackie Li
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Model Uncertainty ◽  
Factor Model ◽  
Common Factor ◽  
Weighted Average ◽  
Pension Plan ◽  
Basis Risk ◽  
Common Factor Model ◽  
The Impact

We investigate the impact of model uncertainty on hedging longevity risk with index-based derivatives and assessing longevity basis risk, which arises from the mismatch between the hedging instruments and the portfolio being hedged. We apply the bivariate Lee–Carter model, the common factor model, and the M7-M5 model, with separate cohort effects between the two populations, and various time series processes and simulation methods, to build index-based longevity hedges and measure the hedge effectiveness. Based on our modeling and simulations on hypothetical scenarios, the estimated levels of hedge effectiveness are around 50% to 80% for a large pension plan, and the model selection, particularly in dealing with the computed time series, plays a very important role in the estimation. We also experiment with a modified bootstrapping approach to incorporate the uncertainty of model selection into the modeling of longevity basis risk. The hedging results under this approach may approximately be seen as a “weighted” average of those calculated from the different model candidates.

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