Sample Size Requirements for Structural Equation Models

Wolf, Erika J.; Harrington, Kelly; Clark, Shaunna L.; Miller, Mark W.

doi:10.1177/0013164413495237

Cited by 2,117 publications

(906 citation statements)

References 35 publications

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“…Furthermore, we used pre-validated questionnaires from earlier studies with relatively high factor loadings and many items per factor, our models did not assume indicator normality (a WLS estimator was used), regressive effects were moderately large, and there was no missing data. Using these properties to extrapolate findings presented by Wolf et al (2013) and Sideridis et al (2014), we argue our sample size is in the right ballpark. We also made sure that the different models for our studies were consistent in the items used for each latent construct and the structural model as much as possible, which makes the results more robust from a reproducibility standpoint.…”

Section: Limitationsmentioning

confidence: 65%

Understanding the role of latent feature diversification on choice difficulty and satisfaction

Willemsen

Graus

Knijnenburg

2016

User Model User-Adap Inter

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People like variety and often prefer to choose from large item sets. However, large sets can cause a phenomenon called "choice overload": they are more difficult to choose from, and as a result decision makers are less satisfied with their choices. It has been argued that choice overload occurs because large sets contain more similar items. To overcome this effect, the present paper proposes that increasing the diversity of item sets might make them more attractive and satisfactory, without making them much more difficult to choose from. To this purpose, by using structural equation model methodology, we study diversification based on the latent features of a matrix factorization recommender model. Study 1 diversifies a set of recommended items while controlling for the overall quality of the set, and tests it in two online user experiments with a movie recommender system. Study 1a tests the effectiveness of the latent feature diversification, and shows that diversification increases the perceived diversity and attractiveness of the item set, while at the same time reducing the perceived difficulty of choosing from the set. Study 1b subsequently shows that diversification can increase users' satisfaction with the chosen option, especially when they are choosing from small, diverse item sets. Study 2 extends these results by testing our diversification algorithm against traditional Top-N recommendations, and finds that diverse, The acting editor for the review of this article has been Dietmar Jannach, Technical University of Dortmund, Germany. small item sets are just as satisfying and less effortful to choose from than Top-N recommendations. Our results suggest that, at least for the movie domain, diverse small sets may be the best thing one could offer a user of a recommender system.

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Section: Limitationsmentioning

confidence: 65%

Understanding the role of latent feature diversification on choice difficulty and satisfaction

Willemsen

Graus

Knijnenburg

2016

User Model User-Adap Inter

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“…Finally, the sample size in the present study can be viewed as relatively modest. Simulation studies have shown that the sample size required for structural equation modeling depends on multiple factors, including the number of latent factors, the number of indicators, and the magnitude of factor loadings and correlations (e.g., Wolf, Harrington, Clark, & Miller, 2013). Hence, although we attempted to model five latent factors, which increases sample size demands compared with models with fewer factors, the use of up to four tasks to indicate each factor in turn decreases the required sample size.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Shifting between mental sets: An individual differences approach to commonalities and differences of task switching components.

Bastian¹,

Druey²

2017

Journal of Experimental Psychology: General

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Switching between mental sets has been extensively investigated in both experimental and individual differences research using a wide range of task-switch paradigms. However, it is yet unclear whether these different tasks measure a unitary shifting ability or reflect different facets thereof. In this study, 20 task pairs were administered to 119 young adults to assess 5 proposed components of mental set shifting: switching between judgments, stimulus dimensions, stimulus-response mappings, response sets, and stimulus sets. Modeling latent factors for each of the components revealed that a model with 5 separate yet mostly correlated factors fit the data best. In this model, the components most strongly related to the other latent factors were stimulus-response mapping shifting and, to a lesser degree, response set shifting. In addition, both factors were statistically indistinguishable from a second-order general shifting factor. In contrast, shifting between judgments as well as stimulus dimensions consistently required separate factors and could, hence, not fully be accounted for by the general shifting factor. Finally, shifting between stimulus sets was unrelated to any other shifting component but mapping shifting. We conclude that tasks assessing shifting between mappings are most adequate to assess general shifting ability. In contrast, shifting between stimulus sets (e.g., as in the Trail Making Test) probably reflects shifts in visual attention rather than executive shifting ability.Keywords: task switching, individual differences, executive functions, unity and diversity Supplemental materials: http://dx.doi.org/10.1037/xge0000333.supp It is common experience that we rarely focus on only one activity at the time uninterruptedly. Rather, most of our daily life involves several things going on in quick succession or even concurrently. For example, while driving a car, we may simultaneously listen to music, quickly shift our attention from the street to the navigation system, and back on the road in an attempt to orient in an unfamiliar city. The mental processes behind such task switching and multitasking are obviously complex and have been one of the main topics in cognitive research of the last decades. Terms as, for instance, executive functions, cognitive control, or mental flexibility have been used to refer to the set of "shifting abilities" required in such situations, and many different paradigms have been developed to experimentally investigate these skills (e.g., Logan, 1985;Pashler, 2000).Set shifting abilities have also been of central interest in individual differences studies and studies on so-called frontal-lobe functions (e.g., Alvarez & Emory, 2006;Miyake et al., 2000). In these contexts, tasks have frequently been used that already have a long tradition in the assessment of frontal-executive functioning such as the Wisconsin Card Sorting Test (WCST) and its derivatives, or the Trail Making Test (TMT).In the present study, we try to bridge the two fields by using an individual differences...

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“…Some authors have suggested that knowledge of immediate proactive approaches is inaccessible to applied researchers (Wolf, Harrington, Clark, & Miller, 2013); I have not generally found this to be true. Rather, I see applied researchers struggling with the poor quality of existing preliminary proactive methods to align the general scope of their research with what might be feasible or realistic to implement.…”

Section: Discussionmentioning

confidence: 98%

Preliminary Proactive Sample Size Determination for Confirmatory Factor Analysis Models

Koran

2016

Measurement and Evaluation in Counseling and Development

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Sample Size Requirements for Structural Equation Models

Cited by 2,117 publications

References 35 publications

Understanding the role of latent feature diversification on choice difficulty and satisfaction

Understanding the role of latent feature diversification on choice difficulty and satisfaction

Shifting between mental sets: An individual differences approach to commonalities and differences of task switching components.

Preliminary Proactive Sample Size Determination for Confirmatory Factor Analysis Models

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