Particle Selection in Suspension-Feeding Bivalves: Does One Model Fit All?

2020 ◽  
Vol 238 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Julie Jones ◽  
Bassem Allam ◽  
Emmanuelle Pales Espinosa
Keyword(s):  
Model Fit ◽  
Suspension Feeding ◽  
Particle Selection

scholarly journals Particle Selection in Suspension-Feeding Bivalves

1981 ◽  
Vol 5 ◽  
pp. 291-296 ◽  
Author(s):  
T Kiørboe ◽  
F Møhlenberg
Keyword(s):  
Suspension Feeding ◽  
Particle Selection

Exploring the Multidimensional Facets of Authoritarianism: Authoritarian Aggression and Social Dominance Orientation

2008 ◽  
Vol 67 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Stefano Passini
Keyword(s):  
Social Dominance ◽  
Factor Model ◽  
Three Dimensional ◽  
Social Dominance Orientation ◽  
Model Fit ◽  
Regression Analyses ◽  
One Dimensional ◽  
Confirmatory Factor ◽  
The One ◽  
Better Than

The relation between authoritarianism and social dominance orientation was analyzed, with authoritarianism measured using a three-dimensional scale. The implicit multidimensional structure (authoritarian submission, conventionalism, authoritarian aggression) of Altemeyer’s (1981, 1988) conceptualization of authoritarianism is inconsistent with its one-dimensional methodological operationalization. The dimensionality of authoritarianism was investigated using confirmatory factor analysis in a sample of 713 university students. As hypothesized, the three-factor model fit the data significantly better than the one-factor model. Regression analyses revealed that only authoritarian aggression was related to social dominance orientation. That is, only intolerance of deviance was related to high social dominance, whereas submissiveness was not.

Following the Ants

2017 ◽  
Vol 33 (6) ◽  
pp. 409-421 ◽  
Author(s):  
Anne B. Janssen ◽  
Martin Schultze ◽  
Adrian Grötsch
Keyword(s):  
Short Form ◽  
Supervisor Support ◽  
Proactive Personality ◽  
Work Behavior ◽  
Model Fit ◽  
Item Selection ◽  
Short Version ◽  
Innovative Work Behavior ◽  
Innovative Work ◽  
Two Measures

Abstract. Employees’ innovative work is a facet of proactive work behavior that is of increasing interest to industrial and organizational psychologists. As proactive personality and supervisor support are key predictors of innovative work behavior, reliable, and valid employee ratings of these two constructs are crucial for organizations’ planning of personnel development measures. However, the time for assessments is often limited. The present study therefore aimed at constructing reliable short scales of two measures of proactive personality and supervisor support. For this purpose, we compared an innovative approach of item selection, namely Ant Colony Optimization (ACO; Leite, Huang, & Marcoulides, 2008 ) and classical item selection procedures. For proactive personality, the two item selection approaches provided similar results. Both five-item short forms showed a satisfactory reliability and a small, however negligible loss of criterion validity. For a two-dimensional supervisor support scale, ACO found a reliable and valid short form. Psychometric properties of the short version were in accordance with those of the parent form. A manual supervisor support short form revealed a rather poor model fit and a serious loss of validity. We discuss benefits and shortcomings of ACO compared to classical item selection approaches and recommendations for the application of ACO.

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.

Mindfulness or Mindlessness?

2011 ◽  
Vol 27 (1) ◽  
pp. 59-64 ◽  
Author(s):  
Volkmar Höfling ◽  
Helfried Moosbrugger ◽  
Karin Schermelleh-Engel ◽  
Thomas Heidenreich
Keyword(s):  
Construct Validity ◽  
Model Fit ◽  
Factor Analyses ◽  
Confirmatory Factor Analyses ◽  
Mindful Attention ◽  
Confirmatory Factor ◽  
Item Wording ◽  
Method Effects

The 15 items of the Mindful Attention and Awareness Scale (MAAS; Brown & Ryan, 2003 ) are negatively worded and assumed to assess mindfulness. However, there are indications of differences between the original MAAS and a version with the positively rephrased MAAS items (“mirror items”). The present study examines whether the mindfulness facet “mindful attention and awareness” (MAA) can be measured with both positively and negatively worded items if we take method effects due to item wording into account. To this end, the 15 negatively worded items of the MAAS and additionally 13 positively rephrased items were assessed (N = 602). Confirmatory factor analyses (CFA) models with and without regard to method effects were carried out and evaluated by means of model fit. As a result, the positively and negatively worded items should be seen as different methods that influence the construct validity of mindfulness. Furthermore, a modified version of the MAAS (MAAS-Short) with five negatively worded items (taken from the MAAS) and five positively worded items (“mirror items”) was introduced as an alternative to assess MAA. The MAAS-Short appears superior to the original MAAS. The results and the limitations of the present study are discussed.

Factorial Structure of the Therapist Adherence Measure-Revised (TAM-R) Within Multisystemic Therapy

2020 ◽  
Vol 36 (2) ◽  
pp. 427-431
Author(s):  
Aurelie M. C. Lange ◽  
Marc J. M. H. Delsing ◽  
Ron H. J. Scholte ◽  
Rachel E. A. van der Rijken
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Factor Model ◽  
Multisystemic Therapy ◽  
Model Fit ◽  
Validity And Reliability ◽  
Adherence Measure ◽  
Therapist Adherence ◽  
Confirmatory Factor ◽  
The Us

Abstract. The Therapist Adherence Measure (TAM-R) is a central assessment within the quality-assurance system of Multisystemic Therapy (MST). Studies into the validity and reliability of the TAM in the US have found varying numbers of latent factors. The current study aimed to reexamine its factor structure using two independent samples of families participating in MST in the Netherlands. The factor structure was explored using an Exploratory Factor Analysis (EFA) in Sample 1 ( N = 580). This resulted in a two-factor solution. The factors were labeled “therapist adherence” and “client–therapist alliance.” Four cross-loading items were dropped. Reliability of the resulting factors was good. This two-factor model showed good model fit in a subsequent Confirmatory Factor Analysis (CFA) in Sample 2 ( N = 723). The current finding of an alliance component corroborates previous studies and fits with the focus of the MST treatment model on creating engagement.

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

Is That Measure Really One-Dimensional?

Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 188-196 ◽  
Author(s):  
Esther T. Beierl ◽  
Markus Bühner ◽  
Moritz Heene
Keyword(s):  
Model Fit ◽  
Nuisance Parameters ◽  
Factorial Validity ◽  
Fit Indices ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
One Dimensional ◽  
Confirmatory Factor Analysis Model ◽  
Cutoff Values ◽  
Confirmatory Factor

Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factorial invalidity. Measures of model fit showed only small misfit because nuisance parameters, magnitude of factor loadings and a balanced/imbalanced number of indicators per factor, also influenced the degree of misfit. Drawing from our results, we discuss challenges in the assessment of factorial validity.

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