scholarly journals A genetic algorithm for optimal assembly of pairwise forced-choice questionnaires

2021 ◽  
Author(s):  
Rodrigo Schames Kreitchmann ◽  
Francisco J. Abad ◽  
Miguel A. Sorrel
Keyword(s):  
Genetic Algorithm ◽  
Factor Model ◽  
Five Factor Model ◽  
Computation Time ◽  
Optimization Procedure ◽  
Forced Choice ◽  
Combinatorial Problems ◽  
Real Problem ◽  
Item Pool ◽  
Average Standard Error

AbstractThe use of multidimensional forced-choice questionnaires has been proposed as a means of improving validity in the assessment of non-cognitive attributes in high-stakes scenarios. However, the reduced precision of trait estimates in this questionnaire format is an important drawback. Accordingly, this article presents an optimization procedure for assembling pairwise forced-choice questionnaires while maximizing posterior marginal reliabilities. This procedure is performed through the adaptation of a known genetic algorithm (GA) for combinatorial problems. In a simulation study, the efficiency of the proposed procedure was compared with a quasi-brute-force (BF) search. For this purpose, five-dimensional item pools were simulated to emulate the real problem of generating a forced-choice personality questionnaire under the five-factor model. Three factors were manipulated: (1) the length of the questionnaire, (2) the relative item pool size with respect to the questionnaire’s length, and (3) the true correlations between traits. The recovery of the person parameters for each assembled questionnaire was evaluated through the squared correlation between estimated and true parameters, the root mean square error between the estimated and true parameters, the average difference between the estimated and true inter-trait correlations, and the average standard error for each trait level. The proposed GA offered more accurate trait estimates than the BF search within a reasonable computation time in every simulation condition. Such improvements were especially important when measuring correlated traits and when the relative item pool sizes were higher. A user-friendly online implementation of the algorithm was made available to the users.

The Multidimensional Mortality Awareness Measure and Model

2015 ◽  
Vol 70 (3) ◽  
pp. 317-341 ◽  
Author(s):  
Oona Levasseur ◽  
Mark R. McDermott ◽  
Kathryn D. Lafreniere
Keyword(s):  
Construct Validity ◽  
Risk Taking ◽  
Factor Model ◽  
Five Factor Model ◽  
Demographic Variables ◽  
Underlying Structure ◽  
Item Pool ◽  
Health Attitudes ◽  
Internal Reliability ◽  
Multidimensional Measure

For each of eight literature-identified conceptual dimensions of mortality awareness, questionnaire items were generated, producing 89 in all. A total of 359 participants responded to these items and to questionnaires measuring health attitudes, risk taking, rebelliousness, and demographic variables. Multivariate correlational analyses investigated the underlying structure of the item pool and the construct validity as well as the reliability of the emergent empirically derived subscales. Five components, rather than eight, were identified. Given the item content of each, the associated mortality awareness subscales were labeled as legacy, fearfulness, acceptance, disempowerment, and disengagement. Each attained an acceptable level of internal reliability. Relationships with other variables supported the construct validity of these empirically derived subscales and more generally of this five-factor model. In conclusion, this new multidimensional measure and model of mortality awareness extends our understanding of this important aspect of human existence and supports a more integrative and optimistic approach to mortality awareness than previously available.

The Effects of Parameter Settings on the Performance of Genetic Algorithm through Experimental Design and Statistical Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5994-5999 ◽  
Author(s):  
Farhad Kolahan ◽  
Marziyeh Hassani Doughabadi
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Biological Evolution ◽  
Optimization Procedure ◽  
Performance Measure ◽  
Combinatorial Problems ◽  
Characteristic Analysis ◽  
Solution Quality ◽  
Tuning Parameters ◽  
The Relationship

Genetic algorithm (GA) is a meta-heuristic inspired by the efficiency of natural selection in biological evolution. It is one of the most widely used optimization procedure which has successfully been applied on a variety of complex combinatorial problems. The main drawback of GA, however, is its several tuning variables which need to be correctly set. The performance of GA largely depends on the proper selection of its parameters values; including crossover mechanism, probability of crossover, population size and mutation rate and selection percent. The objective of this research is to evaluate the effects of tuning parameters on the performance of genetic algorithm using the data collected as per Central Composite Design (CCD) matrix. To gather the required data, the proposed approach is implemented on a well-known travelling salesman problem with 48 cities. Then, regression modeling has been employed to relate GA variables settings to its performance characteristic. Analysis of Variance (ANOVA) results indicate that the function can properly represent the relationship between GA important variables and its performance measure (solution quality).

Forced-Choice Versus Likert Responses on an Occupational Big Five Questionnaire

2019 ◽  
Vol 40 (3) ◽  
pp. 134-148
Author(s):  
Luc Watrin ◽  
Mattis Geiger ◽  
Maik Spengler ◽  
Oliver Wilhelm
Keyword(s):  
Factor Model ◽  
Five Factor Model ◽  
Forced Choice ◽  
Model Fit ◽  
Self Report ◽  
Design Strategies ◽  
Applied Assessment ◽  
Response Biases ◽  
Occupational Settings ◽  
Potential Remedy

Abstract. Conventional self-report measures are prone to response biases, which distort measurement in any applied assessment. The forced-choice (FC) format was proposed as a potential remedy for these biases. The purpose of these studies was to develop and evaluate a FC questionnaire for the occupational context based on the five factor model of personality. A single-stimulus Likert questionnaire was contextualized for occupational settings and psychometrically optimized in Study 1 ( N = 401). Considering optimal design strategies, we subsequently used this questionnaire to construct and validate a FC questionnaire in Study 2 ( N = 517). Methodological add-ons to established approaches were applied to achieve decent confirmatory model fit. The new questionnaire shows good psychometric qualities and strong validity. We make suggestions for further applications and studies.

Assessing the Big Five

2006 ◽  
Vol 214 (3) ◽  
pp. 133-149 ◽  
Author(s):  
Ralf Schulze ◽  
Richard D. Roberts
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Big Five ◽  
Convergent Validity ◽  
Factor Model ◽  
Five Factor Model ◽  
Big Five Personality ◽  
Item Pool ◽  
Confirmatory Factor ◽  
Initial Item

Abstract. A new measure of the Big Five personality constructs, the Openness Conscientiousness Extraversion Agreeableness Neuroticism Index Condensed (OCEANIC), was developed and validated. In Study 1 (N = 166), the convergent validity with the Big Five as assessed by the NEO-FFI was established. Study 2 (N = 3 808) served to investigate the structure of the instrument with stepwise exploratory factor analysis and confirmatory factor analysis. The incremental predictive validity with respect to objective university grades was examined in Study 3 (N = 145). The results show that a) the scales of the initial item pool converge with those of an established measure of the Big Five, b) the Big Five factor model fits the data both at the item and facet level and both for subsamples of students and workers, and c) consistent with previous research, the Conscientiousness factor of the OCEANIC predicts university grades beyond intelligence measures.

Modularity of the Personality Network

2020 ◽  
Vol 36 (6) ◽  
pp. 998-1008
Author(s):  
Daniel Castro ◽  
Filipa Ferreira ◽  
Tiago Bento Ferreira
Keyword(s):  
Topological Structure ◽  
Functional Organization ◽  
Factor Model ◽  
Five Factor Model ◽  
Personality Characteristics ◽  
Modular Structure ◽  
Item Pool ◽  
Personality Item ◽  
Regularized Method

Abstract. The Five Factor Model (FFM) is the most widely used personality model; it proposes a hierarchical structure of personality with personality characteristics, facets, and factors. An increasing number of studies have challenged the FFM and a plethora of factor models with varying numbers of facets and factors have been proposed, leading to uncertainties about the structure of personality. The networked system of interactions between personality characteristics has stimulated promising progresses, however, the methodological developments needed to map the topological structure and functional organization remain scarce. This study aims to explore the hierarchical modular structure of the personality network and the functional role of personality characteristics. A sample of 345,780 individuals ( Mage = 24.99, SDage = 10.00; 59.18% female) that completed the International Personality Item Pool – NEO-120 in a previous study was reanalyzed. A non-regularized method was used to estimate the personality network and ModuLand was used to characterize its modular structure. Results revealed a modular structure comprising three levels: one level with the 120 personality characteristics, a second level with 35 modules, and a third with 9 modules. Such results suggest that specific personality characteristics and modules have specialized roles in the topological structure of the personality network.

scholarly journals Effects of Faking on the Predictive Validity of a Quasi-Ipsative Forced-Choice Personality Inventory: Implications for Sustainable Personnel Selection

2021 ◽  
Vol 13 (8) ◽  
pp. 4398
Author(s):  
Alexandra Martínez ◽  
Silvia Moscoso ◽  
Mario Lado
Keyword(s):  
Predictive Validity ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Rating Scales ◽  
Factor Model ◽  
Five Factor Model ◽  
Reliability And Validity ◽  
Forced Choice ◽  
Grade Point ◽  
The Impact

Faking behavior is one of the main problems of personality measures. For this reason, determining the potential effects of faking on personality assessment procedures is relevant. The aim of this study has been to examine the impact of faking, induced in a laboratory setting, on the predictive validity of a quasi-ipsative forced-choice (FC) inventory based on the five-factor model. It also examined whether the magnitude of the predictive validity varied depending on the type of criteria analyzed (self-reported performance ratings and grade point average). The participants were 939 students from the University of Santiago de Compostela. As expected, the results showed that: (1) conscientiousness is the best predictor of performance even under faking response conditions; (2) conscientiousness predicts performance better when it is assessed using rating scales; and (3) reliability and validity were attenuated under faking conditions. Finally, we discuss the implications of these findings for the research and practice of personnel selection.

Social Desirability in Personality Inventories

2014 ◽  
Vol 35 (3) ◽  
pp. 144-157 ◽  
Author(s):  
Martin Bäckström ◽  
Fredrik Björklund
Keyword(s):  
Social Desirability ◽  
Big Five ◽  
Work Performance ◽  
Factor Model ◽  
Five Factor Model ◽  
Peer Ratings ◽  
Personality Inventories ◽  
Model Study ◽  
The Difference ◽  
New Variables

The difference between evaluatively loaded and evaluatively neutralized five-factor inventory items was used to create new variables, one for each factor in the five-factor model. Study 1 showed that these variables can be represented in terms of a general evaluative factor which is related to social desirability measures and indicated that the factor may equally well be represented as separate from the Big Five as superordinate to them. Study 2 revealed an evaluative factor in self-ratings and peer ratings of the Big Five, but the evaluative factor in self-reports did not correlate with such a factor in ratings by peers. In Study 3 the evaluative factor contributed above the Big Five in predicting work performance, indicating a substance component. The results are discussed in relation to measurement issues and self-serving biases.

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