Determinants of Visitors’ Loyalty to Religious Sacred Event Places: A Multigroup Measurement Invariance Model

Places hosting religious sacred events provide opportunities for visitors to find spiritual growth and also afford glimpses into the local culture, community, and hosting religious group. This study looks at tourists’ intended behavioral loyalty to a religious sacred event place as determined through motivations, shared beliefs, and emotional solidarity with other visitors, and memorable religious experiences. Data were collected from 985 visitors (556 domestic and 429 international) during the 2019 Kumbh Mela, held in Prayagraj, India. Contrary to previous studies, results indicated that emotional solidarity did not significantly influence attendees’ intended behavioral loyalty (among domestic visitors). Furthermore, in employing an invariance structural test for paths mentioned in the model, results revealed that the effects of shared beliefs, motivations, emotional solidarity, and memorable religious experiences differed among domestic and international visitors. Study implications and limitations are provided at the close of the paper, giving way to future research opportunities.

Invarianza multicultural y validación del índice de fortaleza mental en deportistas norteamericanos y mexicanos (Multicultural Invariance and Validation of the Mental Toughness Index in North American and Mexican Athletes)

La fortaleza mental (MT) es un tema ampliamente indagado y relacionado con profesiones, personalidades y la actividad deportiva. El trabajo del psicólogo del deporte es apoyar en el manejo de emociones, el afrontamiento a estresores y establecimiento de metas, haciendo uso de las características multiculturales de los deportistas. Se examinó la invariancia de MT entre atletas estadounidenses y mexicanos a través de la prueba de invarianza, mediante análisis factorial confirmatorio de grupos múltiples con modelos cada vez más restrictivos. El ajuste de los datos del modelo en ambas muestras fue satisfactorio (CFI mexicano = .988, RMSEA mexicano = .085; CFI US = .998, RMSEA US = .032). El modelo de invariancia escalar se seleccionó como el mejor ajuste (escalar CFI = .981, escalar RMSEA = .077). Los resultados implican que el significado del constructo es igual en ambas culturas y sus puntajes pueden compararse directamente. Abstract. Mental toughness (TM) is a widely investigated topic related to professions, personalities and sports activity. The work of the sports psychologist is to support the management of emotions, coping with factors and setting goals, making use of the multicultural characteristics of athletes. The invariance of TM was examined between American and Mexican athletes through the invariance test. It was performed by confirmatory factor analysis of multiple groups with increasingly restrictive models. The fit of the model data in both samples was very good (Mexican CFI = .988, Mexican RMSEA = .085; US CFI = .998, US RMSEA = .032). The scalar invariance model was selected as the best fit (CFI scalar = .981, RMSEA scalar = .077). The results imply that the meaning of the construct is the same in both cultures and its scores can be directly compared.

Statistical and sequence learning lead to persistent memory in children after a one-year offline period

Extraction of environmental patterns underlies human learning throughout the lifespan and plays a crucial role not only in cognitive but also perceptual, motor, and social skills. At least two types of regularities contribute to acquiring skills: (1) statistical, probability-based regularities, and (2) serial order-based regularities. Memory performance of probability-based and/or serial order-based regularities over short periods (from minutes to weeks) has been widely investigated across the lifespan. However, long-term (months or year-long) memory performance of such knowledge has received relatively less attention and has not been assessed in children yet. Here, we aimed to test the long-term memory performance of probability-based and serial order-based regularities over a 1-year offline period in neurotypical children between the age of 9 and 15. Participants performed a visuomotor four-choice reaction time task designed to measure the acquisition of probability-based and serial order-based regularities simultaneously. Short-term consolidation effects were controlled by retesting their performance after a 5-h delay. They were then retested on the same task 1 year later without any practice between the sessions. Participants successfully acquired both probability-based and serial order-based regularities and retained both types of knowledge over the 1-year period. The successful retention was independent of age. Our study demonstrates that the representation of probability-based and serial order-based regularities remains stable over a long period of time. These findings offer indirect evidence for the developmental invariance model of skill consolidation.

Stability of the Data-Model Fit over Increasing Levels of Factorial Invariance for Different Features of Design in Factor Analysis

The aim of this study is to provide an empirical evaluation of the influence of different aspects of design in the context of factor analysis in terms of model stability. The overall model stability of factor solutions was evaluated by the examination of the order for testing three levels of Measurement Invariance (MIV) starting with configural invariance (model 0). Model testing was evaluated by the Chi-square difference test (Δx2) between two groups, and Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI). Factorial invariance results revealed that the stability of the models was varying over increasing levels of measurement as a function of Variable-To-Factor (VTF) ratio, Subject-To-Variable (STV) ratio, and their interactions. There were invariant factor loadings and invariant intercepts among the groups indicating that measurement invariance was achieved. For VTF ratios 4:1, 7:1, and 10:1, the models started to show stability over the levels of measurement when the STV ratio was 4:1. Yet, the frequency of stability models over 1000 replications increased (from 77% to 91%) as the STV ratio increased. The models showed more stability at or above 32:1 STV.

Appetitive Traits in a Population-Based Study of Polish Adolescents within the PLACE-19 Study: Validation of the Adult Eating Behavior Questionnaire

Appetitive traits of food approach or food avoidance are commonly measured using the Adult Eating Behavior Questionnaire (AEBQ). However, there is no Polish version of the AEBQ validated for adolescents, and to the best of our knowledge, no study completed with the Polish version of the AEBQ has been published thus far. The present study aimed to validate the AEBQ in a population-based sample of Polish secondary school students and to assess differences in appetitive traits between boys and girls within the Polish Adolescents’ COVID-19 Experience (PLACE-19) Study. The PLACE-19 Study was conducted in a group of 2448 adolescents recruited in May 2020 through the random quota sampling of secondary schools. The AEBQ was used to assess food approach subscales (Food Responsiveness, Emotional Over-Eating, and Enjoyment of Food) and food avoidance subscales (Satiety Responsiveness, Emotional Under-Eating, Food Fussiness, and Slowness in Eating). To validate the questionnaire, the standardized factor loadings within confirmatory factor analysis (CFA) with weighted least squares (WLS) were analyzed, and invariance was verified. The CFA presented good model fit, with χ2 = 4826.105 (degrees of freedom (df) = 384), root mean square error of approximation (RMSEA) = 0.069 (90% confidence interval (CI): 0.067, 0.070), comparative fit index (CFI) = 0.90, and standardized root mean square residual (SRMR) = 0.08. The results revealed that, compared to the configural invariance model, the metric invariance model did not result in significantly decreased model fit, with ΔCFI = −0.002 and ΔRMSEA = −0.001, which were lower than the recommended cutoffs of 0.010 and 0.015, respectively. The scalar invariance model also did not result in significantly decreased fit of the model over the metric invariance model, with ΔCFI = −0.005 and ΔRMSEA = 0.000. Girls reported higher levels of Food Responsiveness (p < 0.0001), Emotional Over-Eating (p < 0.0001), Satiety Responsiveness (p < 0.0001), Emotional Under-Eating (p < 0.0001), and Slowness in Eating than boys (p < 0.0001), and the total AEBQ scores of girls were also higher (p < 0.0001). Positive inter-correlations were observed between all food approach subscales, as well as between Emotional Under-Eating and all food approach subscales for girls, boys, and the total sample; positive inter-correlations were also observed between the majority of food avoidance subscales. The present study confirmed the validity of the AEBQ in the studied population, and supported the associations between appetitive traits assessed using the AEBQ; it also indicated higher scores of both food approach and food avoidance subscales in girls than in boys in a population-based sample of Polish secondary school students.

Failure to Detect Metric Measurement Non-Invariance: How Manifest Residual Variances, Indicator Communalities, and Sample Size affect the χ2-Test Statistic

We investigate the effects of manifest residual variances, indicator communalities, and sample size on the χ2-test statistic of the metric measurement invariance model when the model is misspecified, i.e., there is at least one population loading that violates metric measurement invariance. First, we demonstrate the choice of the scaling method does not affect the model’s χ2-test statistic. Afterward, we demonstrate that the χ2-test statistic relates inversely to manifest residual variances, whereas sample size and χ2-test statistic show a positive relation. Moreover, we consider indicator communality as a key factor for the size of the χ2-test statistic. In this context, we introduce the concept of signal-to-noise ratio as a tool for studying the effects of manifest residual variance and indicator communality and demonstrate its use with the example. Finally, we discuss the limitations and the practical implications for the analysis of metric measurement invariance models.

Detecting latent mean differences between non-invariant groups using ordered categorical variables

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Though more and more applied researchers have begun to treat response options as ordered-categorical variables when conducting measurement invariance (MI) testing, little is known about the role of ordered-categorical variables when comparing latent means between groups. Therefore, this study simulated ordered-categorical data to specifically examine the detection of latent mean differences between non-invariant groups across a variety of conditions, including the number of items, population latent mean differences, etc. The purpose of this study was to investigate the relative parameter bias, power rates, and Type I error rates that may arise when ignoring various types of MI in both the configural invariance and metric invariance models. In summary, the most important contributors to relative bias of the true latent mean difference estimates were a) the number of items and the size of the factor loadings in the configural invariance model, b) the size of the factor loading and threshold differences in the metric invariance model that ignored group parameter differences, and c) the number of items in the metric invariance model that addressed the group parameter differences. Thus, in order to reduce the bias in estimating the true latent mean difference between groups, practitioners should identify and address the non-invariance and use a test instrument with more items. The dominant effect on the power to identify whether the latent mean difference was different from 0, in both the configural invariance model and the metric invariance model that ignored true group differences, was the population latent mean difference. In the metric invariance model that addressed the group differences, the most important effects were a) population latent mean differences, and b) loading and threshold differences. When the latent mean difference was at least moderate or the large threshold difference was ignored, the power rate was inflated to be above .90. Applied researchers should know that it will be easier to detect relatively large latent mean differences if both the loading and threshold differences are free to differ between groups. The dominant effect on Type I error rate in the configural invariance model was the number of items. In the metric invariance model that ignored the group parameter differences, the most important effects were a) the size of threshold differences, b) the loading and threshold differences, and c) the number of items. In the metric invariance model that addressed the group parameter differences, the most important effect on Type I error was the number of noninvariant items, which also significantly interacted with the number of items. Often, applied researchers assume their groups are equal, and may not concern themselves with detecting the true latent mean differences. Of course, true population differences cannot be known, so it is recommended that researchers should still conduct a MI analysis. It is especially important to note that in the metric invariance model that addressed group parameter differences, the Type I error rate was below .05. This result suggests that conducting MI testing will help applied researchers detect the true latent mean difference regardless of the magnitude of that difference (i.e., 0, .2 and .5 in this study).

The Effects of Manifest Residual Variances, Indicator Communality, and Sample Size on the χ2-Test Statistic of the Metric Invariance Model

In this contribution, we investigate the effects of manifest residual variance, indicator communality and sample size on the χ2-test statistic of the metric measurement invariance model, i.e. the model with equality constraints on all loadings. We demonstrate by means of Monte Carlo studies that the χ2-test statistic relates inversely to manifest residual variance, whereas sample size and χ2-test statistic show the well-known pro- portional relation. Moreover, we consider indicator communality as a key factor for the size of the χ2-test statistic. In this context, we introduce the concept of signal-to-noise ratio as a tool for studying the effects of manifest residual error and indicator commu- nality and demonstrate its use with some examples. Finally, we discuss the limitations of this contribution and its practical implication for the analysis of metric measurement invariance models.

Measurement invariance of the Center for Epidemiologic Studies Depression Scale in parents of individuals with disabilities

We aimed to verify the factor model and measurement invariance of the abbreviated Center for Epidemiologic Studies Depression Scale by conducting a confirmatory factor analysis using data from 761 parents of individuals with intellectual disabilities who completed the scale as part of the 2011 Survey on the Actual Conditions of Individuals with Developmental Disabilities, South Korea, and 7,301 participants from the general population who completed the scale as part of the 2011 Welfare Panel Study and Survey by the Ministry of Health and Welfare, South Korea. We used fit indices to assess data reliability and Amos 22.0 for data analysis. According to the results, the 4-factor model had an appropriate fit to the data and the regression coefficients were significant. However, the chi-square difference test result was nonsignificant; therefore, the metric invariance model was the most appropriate measurement invariance model for the data. Implications of the findings are discussed.