Confirmatory Model Research Articles

The analysis of TIMSS 2015 data with confirmatory mixture item response theory: A multidimensional approach

In this study, we illustrated an application of the confirmatory mixture IRT model for multidimensional tests. We aimed to examine the differences in student performance by domains with a confirmatory mixture IRT modeling approach. A three-dimensional and three-class model was analyzed by assuming content domains as dimensions and cognitive domains as item groups. We estimated the item performance differences among the students through structural parameters. There were 463 students from Turkey and 880 students from Canada who participated in the TIMSS 2015 4th-grade mathematics assessment. Results for Turkey indicated, students in Class 2 had better performance in knowing and reasoning compared to those in Classes 1 and 3. Students in Class 2 and Class 3 were similar in applying math concepts compared to students in Class 1. For the Canadian sample, students in Class 2 had better performance in knowing, applying, and reasoning compared to those in Class 1 and 3. Also, Class 3 students were better at applying domain than Class 1. Also, mean values were obtained for all content domains in the two countries. Confirmatory mixture IRT modeling approaches appear to differentiate students’ mathematics competencies.

Confirmatory measurement modeling and longitudinal invariance of the CompACT-15: A short-form assessment of psychological flexibility.

The Comprehensive Assessment of Acceptance and Commitment Therapy Processes (CompACT; Francis et al., 2016) is a recently developed measure of psychological flexibility (PF) possessing several advantages over other measures of PF, including multidimensional assessment and relative brevity. Unfortunately, previous psychometric evaluations of the CompACT have been limited by their use of exploratory factor analysis to assess dimensionality, coefficient α to assess reliability, and a lack of attention to measurement equivalence in assessing change over time. The present study used confirmatory factor analysis (CFA) and item factor analysis (IFA) to examine the dimensionality, factor-specific reliability, longitudinal measurement invariance, and construct validity of the CompACT items in a longitudinal online sample of U.S. adults (N = 523). Converging evidence across CFA and IFA confirmatory latent variable measurement models provides support for the reduction of the 23-item CompACT to a 15-item short form with a more stable factor structure, acceptable reliability over large ranges of its three latent factors, and measurement equivalence of its items in assessing latent change over time. Results also support the construct validity of the CompACT-15 items based on its relations with theoretically relevant measures. Overall, the CompACT-15 appears to be a psychometrically sound instrument with the potential to contribute to research and intervention efforts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Development and psychometric evaluation of the Man-Made Disaster-Related Distress Scale (MMDS)

Man-made disasters (MMD2MMD = man-made disasters2) are a widespread cause for psychological burden, both in directly and indirectly affected regions, as currently highlighted by the Russian invasion of Ukraine. Yet, measurement instruments that assess psychological distress associated with MMD are lacking. The study aimed to develop and validate such an instrument in an indirectly affected region. The resulting “man-made disaster-related distress scale” (MMDS) was tested within a German-speaking sample (N = 327). Exploratory and confirmatory factor analysis revealed a two-factorial structure (“Psychological distress” and “Change of existing belief systems”) of the MMDS. The confirmatory model had an acceptable to good fit with CFI/TLI higher than 0.90. Internal consistency of the MMDS and its subscales was high with α and ω higher than 0.80. Correlational analyses suggested convergent, discriminant, and criterion validity. Measurement invariance was confirmed for gender and age with a change in CFI smaller than 0.01 but was rejected for educational level. The MMDS resulted in a 14-item assessment instrument based on a 7-point Likert-scale with good psychometric properties. Its administration is readily and economical, yielding the option to be used as a standardized measurement tool.

An extended TPB model of waste-sorting intention: a case study of Indonesia

PurposeThis research investigates factors affecting the intention to sort waste. Specifically, this research aims to develop a waste-sorting intention model by extending the theory of planned behavior (TPB) model and to test the model to ensure the model's goodness-of-fit, validity and reliability.Design/methodology/approachThis research used a quantitative research methodology. Data were collected from 460 respondents using an online questionnaire. Some statistical analyses were performed to analyze the data: descriptive statistics, factor analysis, confirmatory factor analysis-structural equation modeling (CFA-SEM), SEM and Cronbach's alpha analysis.FindingsThe result shows that the intention to sort waste was directly affected by attitude, subjective norms, perceived behavioral control (PBC), moral obligation and facility support. Environmental concerns, waste-sorting knowledge and time availability indirectly influenced the intention to sort waste. The testing indicated that the proposed model was fit, valid and reliable.Practical implicationsThe model provides a more comprehensive understanding of waste-sorting intention. The central and local governments can use the results to encourage waste-sorting intention in the community.Originality/valueThis research is believed to be the first study to develop and test the waste-sorting intention model that extends the TPB model by incorporating moral obligation, facility support, policy and regulation support, environmental concerns, waste-sorting knowledge and time availability into the traditional TPB model.

Confirmatory Factorial Model of Searching for Information on the Internet During COVID-19

The pandemic directly impacted the educational system by moving from the traditional classroom to the electronic blackboard. In this transition process, an educational habitus emerged that consisted of facing the health, economic, social and educational crisis in a self-management of opportunities. In this sense, the search for information was a fundamental tool to promote self-knowledge. The objective of the present was to establish the reliability and validity of an instrument that measures the intention to search for information in digital networks with a non-probabilistic selection of 253 students from a public university in the State of Mexico. The results show the establishment of eight factors among which the intention to select information explained 21% of the total variance. Lines of research are noted regarding the construction of an agenda with central information processing axes and issues to anticipate discussion scenarios and shared agreements. In relation to the state of knowledge in which stigma prevails towards the proximity of people and distancing as well as confinement as central strategies of anti-COVID-19 policies, the establishment of an agenda focused on the self-management of knowledge is recommended. The management, production and transfer of knowledge is recommended from the self-management of resources and opportunities, as well as from the interrupted collaboration between the parties involved in the public education system.

Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization.

Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a psychological measurement instrument. The algorithm assigns items to an unknown number of nested factors in a confirmatory bifactor model, while simultaneously selecting items for the final scale. To achieve this, the algorithm follows the biological template of bees' foraging behavior: Scout bees explore new food sources, whereas onlooker bees search in the vicinity of previously explored, promising food sources. Analogously, scout bees in BSO introduce major changes to a model specification (e.g., adding or removing a specific factor), whereas onlooker bees only make minor changes (e.g., adding an item to a factor or swapping items between specific factors). Through this division of labor in an artificial bee colony, the algorithm aims to strike a balance between two opposing strategies diversification (or exploration) versus intensification (or exploitation). We demonstrate the usefulness of the algorithm to find the underlying structure in two empirical data sets (Holzinger-Swineford and short dark triad questionnaire, SDQ3). Furthermore, we illustrate the influence of relevant hyperparameters such as the number of bees in the hive, the percentage of scouts to onlookers, and the number of top solutions to be followed. Finally, useful applications of the new algorithm are discussed, as well as limitations and possible future research opportunities.

Psychometric properties of the multidimensional inventory of infidelity in Jordanian society

The concept of infidelity is one of the basics of marital life, and on it depends the satisfaction, compatibility and happiness of the spouses. Therefore, there is an urgent need in Arab countries to develop tools that contribute to dealing with this problem and its painful consequences. Infidelity is a problem that affects a person psychologically and physically, and it is important to develop a measure to identify this problem. This study aimed to verify the validity and reliability of the Multidimensional Infidelity Inventory (IMIN) on a Jordanian sample. For this purpose, the Spanish version of the questionnaire was translated and applied to a Jordanian sample of 732 persons (291 men, 441 women). The results showed that three factors explained 70.28% of the total variance of the infidelity trend subscale, and seven factors explained 68.43% of the total variance of the reasons for infidelity subscale. Five factors explained 61.21% of the total variance of the infidelity beliefs subscale, and two factors explained 57.45% of the total variance for the infidelity consequences subscale. All of them had confirmatory models with adequate levels of goodness of fit, adequate Cronbach alpha indicators, item-item and item-test correlations, in addition to concordance with the original proposal of the instrument. This instrument can assess, diagnose, and overcome infidelity.

How Functional and Psychosocial Benefits Impact Customer Value in Internet Shopping using Means-End Chain Approach

Marketing and strategic management revolve around customer value analysis and management. However, only some know how cognitive capability affects customer value from a functional and psychosocial perspective. Internet apparel services are transforming retail increasingly after Covid. Thus, marketing managers in the rapidly growing apparel industry must be flexible to predict other customer-valued services. This study examines customer value and functional and psychosocial benefits using cognitive capabilities. Exploratory, confirmatory, and structural equation modelling are used to better understand the relationship between functional and psychosocial benefits and customer value. These complicated benefit combinations promote customer value and help develop value generation and its causes. Using the conceptual model, managers can improve cognitive capabilities by understanding how perceived functional and psychosocial benefits affect value generation for various types of customers.

Agri-Food Enterprises and Relationship Marketing During COVID-19: A Primary Data Research through Commitment-Trust Theory

This paper examines relationship marketing through the views of agri-food enterprises during COVID-19 using the commitment-trust theory as a framework. For this purpose, a primary research was conducted in 266 Greek agri-food enterprises at all stages of the agri-food sector and its activities to investigate relationship marketing in times of crisis. Through a confirmatory structural equation model (SEM), compatible with the commitment-trust theory, the results show that relationship termination costs explain the relationship commitment while communication explains trust. Moreover, there is an indirect effect of shared values on cooperation through trust. It is important to note that despite the COVID-19 crisis, there is no effect of uncertainty on trust. The above outcomes shed light on agri-food enterprises and relationship marketing in periods of crisis. With this knowledge, this paper provides recommendations to agri-food enterprises to focus on cooperation and shared values rather than being dominated by uncertainty.

Temporal relationships between latent symptoms in psychosis: a longitudinal experience sampling methodology study

IntroductionA variety of dimensions of psychopathology are observed in psychosis. However, the validation of clinical assessment scales, and their latent variable structure, is often derived from cross-sectional rather than longitudinal data, limiting our understanding of how variables interact and reinforce one another.ObjectivesUsing experience sampling methodology (ESM) and analytic approaches optimised for longitudinal data, we assess potential latent variables of commonly-reported symptoms in psychosis, and explore the temporal relationship between them.MethodsN=36 participants with a diagnosis of schizophrenia or schizoaffective disorder provided data for up to one year, as part of the Sleepsight study. Using a smartphone app, participants self-reported clinical symptoms once daily for a mean duration of 323 days (SD: 88), with a response rate of 69%. Symptoms were rated using seven-point Likert scale items. Items included symptoms traditionally implicated in psychosis (feeling “cheerful”, “anxious”, “relaxed”, “irritable”, “sad”, “in control”, “stressed”, “suspicious”, “trouble concentrating”, “preoccupied by thoughts”, “others dislike me”, “confused”, “others influence my thoughts” and “unusual sights and sounds”). We used a sparse PCA (SPCA) model to identify latent variables in the longitudinal data. SPCA has previously been applied to longitudinal ESM data, and was developed to achieve a compromise between the explained variance and the interpretability of the principal components. We then used a multistage exploratory and confirmatory differential time-varying effect model (DTVEM) to explore the temporal relationship between the latent variables. DTVEM generates a standardised β coefficient reflecting the strength of relationship between variables across multiple time lags. Only significant lags (p<0.05) are reported here.ResultsThe SPCA analysis identified five latent variables, explaining 61.4% of the total variance. Tentative interpretation of the SPCA loadings suggested these latent variables corresponded to i) cognitive symptoms, ii) feeling in-control, iii) thought interference and perceptual disturbance, iv) irritability and stress and v) paranoia. Time lag analysis revealed an effect of feeling in-control on subsequent cognitive symptoms (β=-0.19), and of cognitive symptoms on subsequent thought interference and perceptual disturbance (β=0.14). Irritability and stress was also associated with subsequent cognitive symptoms (β=0.09).ConclusionsUsing longitudinal data, we employ novel methodology to identify potential latent symptoms among commonly reported symptoms in psychosis. We identify five latent symptoms, and elucidate important temporal relationships between them. These findings may inform our understanding of the psychopathology of psychosis, potentially offering data-driven simplification of clinical assessment and novel insights for future research.Disclosure of InterestNone Declared

A hyperaldosteronism subtypes predictive model using ensemble learning

This study aimed to develop a machine-learning algorithm to diagnose aldosterone-producing adenoma (APA) for predicting APA probabilities. A retrospective cross-sectional analysis of the Japan Rare/Intractable Adrenal Diseases Study dataset was performed using the nationwide PA registry in Japan comprised of 41 centers. Patients treated between January 2006 and December 2019 were included. Forty-six features at screening and 13 features at confirmatory test were used for model development to calculate APA probability. Seven machine-learning programs were combined to develop the ensemble-learning model (ELM), which was externally validated. The strongest predictive factors for APA were serum potassium (s-K) at first visit, s-K after medication, plasma aldosterone concentration, aldosterone-to-renin ratio, and potassium supplementation dose. The average performance of the screening model had an AUC of 0.899; the confirmatory test model had an AUC of 0.913. In the external validation, the AUC was 0.964 in the screening model using an APA probability of 0.17. The clinical findings at screening predicted the diagnosis of APA with high accuracy. This novel algorithm can support the PA practice in primary care settings and prevent potentially curable APA patients from falling outside the PA diagnostic flowchart.

Emerging ideas. Filial piety, substance use, adverse childhood experiences, and parental support: An intergenerational perspective

AbstractObjectiveThe goal was to test the validity of the dual filial piety model in Austria and examine how the reciprocal and authoritarian filial piety interplay with familial factors and one's tobacco and alcohol use.BackgroundFamilial influences on substance use have been identified. Being conceptualized as a Chinese construct, research on filial piety and its interplay with substance use is insufficient in Western populations.MethodUsing a cross‐sectional survey including filial piety, familial variables, psychopathy traits, and alcohol use, we collected data from 201 Austrian community members (63.0% female; Mage = 35.15 years). Another age‐stratified random sample with 202 Austrians was also subsequently recruited to replicate the original findings. Multiple correlational analyses and confirmatory structural equation modeling were utilized on both samples, separately.ResultsThe dual filial piety model was found as a robust construct in both samples, with an acceptable scalability (Cronbach's α > .70). Although filial piety was unassociated with substance use, earlier experiences and psychopathy traits both showed consistent correlations with substance use. Un‐directional network analysis among variables further revealed an association between ever smoking experience and adverse childhood experiences, which was replicated in the second sample.ConclusionsThis study highlights the utility of filial piety in non‐Eastern populations. Filial piety was associated with substance use in one sample, but more research from family and developmental psychology is needed given its close relationships with childhood aversive experiences and remembered parental support.

A bifactor approach to subscore assessment.

Bifactor confirmatory factor analysis models and statistical indices computed from them have previously been used to provide evidence for the appropriateness of utilizing a unidimensional interpretation of multidimensional data. However, the ability of bifactor indices to aid in the assessment of subscore strength has not been investigated. A simulation study was conducted to relate bifactor indices to the strength of subscores corresponding to specific factors. The bifactor indices OmegaHS and ECVSS were found to be strongly predictive of subscore strength conditional upon OmegaS. The number of factors was also found to play a minor role in this relationship. Cutoffs for assessing the appropriateness of interpreting subscores were constructed based on OmegaHS or ECVSS conditional upon OmegaS and the number of factors. For low subscore reliability (OmegaS = .60), OmegaHS = .25 or ECVSS = .45 is sufficient that the subscore has a good chance of having added value (VAR > 1.1) above and beyond the total score. For moderate reliability (OmegaS = .80), OmegaHS = .20 or ECVSS = .30 is sufficient, and the role of OmegaHS or ECVSS diminishes as OmegaS increases further. A subscore having added value does not necessitate its interpretation. Instead, when subscores are desired to be interpreted, high OmegaHS or ECVSS can be considered as evidence that such an interpretation is statistically appropriate. Finally, we illustrate the use of these cutoffs with an empirical data set. When combined with prior bifactor research, this work extends a framework of using confirmatory bifactor models for dimensionality assessment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Factor structure of post-operative quality of recovery questionnaire (QoR-15): An Italian adaptation and validation.

The Quality of Recovery questionnaire (QoR-15) is an English instrument for measuring quality of recovery in surgical patients, not yet translated and validated in Italian when the Enhanced Recovery After Surgery (ERAS) Piemonte studies were planned. To produce the Italian version of the QoR-15 questionnaire, to evaluate its factorial structure and to assess the invariance between two types of surgery. The Italian version (QoR-15I) was obtained translating and adapting the original version to the Italian context. The validation was performed suppling the QoR-15I to 3,784 patients enrolled in two parallel stepped wedge cluster randomised trials (ERAS Colon-rectum Piemonte; ERAS Gyneco Piemonte). The factor structure and its invariance between types of surgery was tested using confirmatory bifactor model and multi-group analysis. Comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) fit indices and their changes between nested models were used to assess the factor structure and the invariance. The bifactor model showed good fit (RMSEA = 0.049, CFI =0.957, SRMR = 0.036) and provided a general recovery factor and two specific factors for physical and mental recovery. Eighty-four percent of the common variance is attributable to the general factor, and thus the QoR-15I is sufficiently 'one-dimensional' with an adequate reliability (ωh = 0.70). The ωs values for the physical and mental recovery factors were 0.01 and 0.13, respectively. Multigroup analysis supported configural (RMSEA = 0.053, CFI = 0.950, SRMR = 0.035) and metric invariance (ΔRMSEA = -0.004; ΔCFI = -0.002; ΔSRMR = 0.014), whereas the intercept constraint was removed from item 15 to obtain partial scalar invariance (ΔRMSEA = 0.002; ΔCFI = 0.007; ΔSRMR = 0.004). Construct validity was supported by a negative association of QoR-15I scores with all variables related to worse patient condition and more complex surgery. Our results support the use of the QoR-15I as a valid, reliable, and clinically feasible tool for measuring the quality of recovery after surgery. The results of the confirmatory factor analyses suggest that a unique recovery score can be calculated and support measurement invariance of the QOR-15I across the two type of surgery, suggesting that the questionnaire has the same meaning and the same measurement parameters in colorectal and gynaecologic patients.

Initial Validation of the Counseling Center Assessment of Psychological Symptoms-62 for the Autistic College Population

Internal validity and measurement invariance of the Counseling Center Assessment of Psychological Symptoms-62 item (CCAPS-62) was examined via confirmatory factor analytic models among autistic (n = 1,268) and non-autistic (n = 3,776) college students. Findings reviewed the CCAPS-62 had a strong model fit and was invariant across groups.

Do the innovation and digital transformation strategies induce sme performances In new normal era? Structrual & confirmatory analysis models

One of major challenge in a sustainable growth, which organizations face is a slow adoption of the digital transformation. This research work presents the reasons that lead to the slow digitization process in medical device SMEs in southern Germany. In addition, by developing the conceptual model, this work highlights the effect of these improper implementations on SME's business performances and financial situation. The researchers applied correlational research design methodology, with simple random sampling techniques along with empirical and statistical study with primary data collection. The main study variables are SME’s financial situation, SMEs organizational performance, and medical digitization rules. The study demonstrated the negative impact of delayed digital mechanisms in terms of businesses and financial performances.The extra transparency restrictions that add burdens for SMEs, and the lack of training for the employees, which in overall add more difficulties for adopting innovation and digital transformation are other factors negatively affecting the studied process.

Scale development and validation study on the using YouTube as a learning environment in secondary education according to the technology acceptance model

In today's technologies, in line with the needs of the age, social platforms have taken their place in education with increasing importance day by day. Especially in the pandemic process, which requires distance education, the tendency to social media has increased. Therefore, the attitudes of secondary school students towards the use of YouTube for learning purposes have gained importance. For this reason, it is aimed to develop the YouTube Usage Scale (YUS), which can be used to determine secondary school students' attitudes toward YouTube use. Thus, in the study, it was foreseen to determine the behavioral intentions of secondary school students to use YouTube as a Learning Resource and to reveal the factors affecting this behavioral intention. The research population consists of all secondary school students studying in the Malazgirt district of Muş province, located in the eastern part of Turkey, in the 2018-2019 academic year. The sample of the study is represented by 644 students selected by the random sampling method. The content validity of the YUS was provided by expert opinion. The construct validity was validated by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). For content validity, a 47-item draft YUS was submitted to the opinion of a group of 14 experts in the field. The content validity of the 42-item YUS was ensured. In the construct validity studies of YUS, EFA was performed with a total of 644 secondary school students studying in the 5th, 6th, 7th and 8th grades. CFA was carried out with the participation of 311 secondary school students excluding the EFA sample group. EFA showed that YUS consisted of 4 sub-dimensions and 25 items. The Cronbach's alpha coefficient was found to be 0.91 in the reliability analysis of YUS. Also, in reliability studies, it was determined that the subscales had summability and no response bias. The fit-order between the four-factor structure determined as a result of EFA and the sample data was examined using the AMOS 24.0 program within the framework of the Technology Acceptance Model (TAM). First- and second-order confirmatory analysis models were used in the data-model fit calculations. As a result of the study, the validity and reliability of the 24-items YUS scale were produced. Thus, the structures explaining the behavioral factors of secondary school students were defined, and the relationship of these structures with other factors was determined.

Confirmatory Factorial Model of Entrepreneurship against COVID-19

The purpose of the work was to specify a model for the study of social understanding. A documentary, cross-sectional and exploratory study was carried out with a non-probabilistic selection of sources indexed to international repositories, considering the indexation, year of publication and impact factor. Discussion axes were established to address the problem, although the research design limited the findings to the sample analyzed, suggesting the extension of other repositories, years and quality indicators.

Extending exploratory diagnostic classification models: Inferring the effect of covariates.

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.

The Positive Aspects of Caregiving Scale can be shortened to six items: application of item response theory

Objective For nearly two decades, with one exception, researchers have used the Positive Aspects of Caregiving Scale (PACS) without a close examination of how the items behave. This study examined PACS’ measurement model and item response characteristics, including differential item functioning (DIF) by race. Methods Item response theory was used to analyze the baseline data of the Resources for Enhancing Alzheimer’s Caregiver Health (REACH) I trial (N = 1229). Bifactor confirmatory models were estimated to assess dimensionality, and whether the items behaved differently in African Americans and Hispanics, with Whites serving as the comparison, were examined using the multiple-indicators multiple-causes model. Results For both 9- and 11-item versions, 1-factor and bifactor solutions supported a unidimensional structure. However, the items performed generally poorly in differentiating levels of the latent construct, especially between moderate and high true scores. Additionally, five items were found to have DIF, which were unrelated to relationship differences among racial groups. While African Americans and Hispanics reported higher positive gains than Whites, their differences were accentuated by the DIF items as a whole. A 6-item version called PACS-II was created by removing the DIF items. Besides reducing estimated racial differences, PACS-II removed race-based heterogeneity of variance that was evidenced in the original, longer versions. Conclusions Being free of racial bias, PACS-II is preferred over the original versions, though the ceiling effect of measurement (prevalence of extreme scores on the high end) remains to be an issue for improvement.