Latent Variable Scores Research Articles

The impact of customer value types on customer outcomes for different retail formats

Purpose – The purpose of this paper is to profile grocery retailers in terms of seven value types based on Holbrook’s value typology; to link these value types to three key outcomes (i.e. satisfaction, repurchase intention, and word-of-mouth); and to evaluate the impact of the retail format on performance and importance of the seven value types. Design/methodology/approach – For each retail format, the authors administered a consumer survey, resulting in an aggregate sample of 392 respondents. The authors used partial least squares structural equations modeling to test the relationships between the value types and key outcomes (i.e. importance) and ANOVAs to examine cross-format differences between latent variable scores of the value types (i.e. performance). Findings – The three retail formats included in the study perform differently on Holbrook’s value types (e.g. non-discounters excel in terms of aesthetic value and play, compared to hard and soft discounters). Furthermore, this study reveals that the strategic importance of each value type depends on the key outcome (e.g. whereas efficiency is the main source of satisfaction, play mainly drives the other two outcomes). Research limitations/implications – The authors randomly assigned respondents to one of the three retail formats irrespective of their personal preference or patronage. To conduct value-based segmentation, respondents should evaluate either their preferred format or all supermarkets. Practical implications – This study offers positioning advice to retail managers, according to their format and strategic objectives. Originality/value – Unlike previous research, this paper provides a cross-format comparison of retailers based on a three-dimensional value typology and its key outcomes.

The sampling design effect on partial least squares algorithm

The objective of this article is to analyze the effect of the probability sampling’s selection on the estimated results in Structural Equation Modeling (SEM) using the Partial Least Squares (PLS) algorithm.The idea leading this work is to estimate the satisfaction level of government service users in a large and dispersed population, for which a sample design with an equal selection probability is not a feasible option. This study is based on the analysis of the sampling distributions of estimators under different sampling designs.It is shown that the probability of selection of the units behind the sampling design affect the results of the PLS algorithm, both the scores of latent variables and the impacts between them.To the author's knowledge, this issue has not been addressed before in the literature.

The Accuracy of Computerized Adaptive Testing in Heterogeneous Populations: A Mixture Item-Response Theory Analysis.

BackgroundComputerized adaptive testing (CAT) utilizes latent variable measurement model parameters that are typically assumed to be equivalently applicable to all people. Biased latent variable scores may be obtained in samples that are heterogeneous with respect to a specified measurement model. We examined the implications of sample heterogeneity with respect to CAT-predicted patient-reported outcomes (PRO) scores for the measurement of pain.MethodsA latent variable mixture modeling (LVMM) analysis was conducted using data collected from a heterogeneous sample of people in British Columbia, Canada, who were administered the 36 pain domain items of the CAT-5D-QOL. The fitted LVMM was then used to produce data for a simulation analysis. We evaluated bias by comparing the referent PRO scores of the LVMM with PRO scores predicted by a “conventional” CAT (ignoring heterogeneity) and a LVMM-based “mixture” CAT (accommodating heterogeneity).ResultsThe LVMM analysis indicated support for three latent classes with class proportions of 0.25, 0.30 and 0.45, which suggests that the sample was heterogeneous. The simulation analyses revealed differences between the referent PRO scores and the PRO scores produced by the “conventional” CAT. The “mixture” CAT produced PRO scores that were nearly equivalent to the referent scores.ConclusionBias in PRO scores based on latent variable models may result when population heterogeneity is ignored. Improved accuracy could be obtained by using CATs that are parameterized using LVMM.

'Alzheimer's Progression Score': Development of a Biomarker Summary Outcome for AD Prevention Trials.

Alzheimer's disease (AD) prevention research requires methods for measurement of disease progression not yet revealed by symptoms. Preferably, such measurement should encompass multiple disease markers. Evaluate an item response theory (IRT) model-based latent variable Alzheimer Progression Score (APS) that uses multi-modal disease markers to estimate pre-clinical disease progression. Estimate APS scores in the BIOCARD observational study, and in the parallel PREVENT-AD Cohort and its sister INTREPAD placebo-controlled prevention trial. Use BIOCARD data to evaluate whether baseline and early APS trajectory predict later progression to MCI/dementia. Similarly, use longitudinal PREVENT-AD data to assess test measurement invariance over time. Further, assess portability of the PREVENT-AD IRT model to baseline INTREPAD data, and explore model changes when CSF markers are added or withdrawn. BIOCARD was established in 1995 and participants were followed up to 20 years in Baltimore, USA. The PREVENT-AD and INTREPAD trial cohorts were established between 2011-2015 in Montreal, Canada, using nearly identical entry criteria to enroll high-risk cognitively normal persons aged 60+ then followed for several years. 349 cognitively normal, primarily middle-aged participants in BIOCARD, 125 high-risk participants aged 60+ in PREVENT-AD, and 217 similar subjects in INTREPAD. 106 INTREPAD participants donated up to four serial CSF samples. Global cognitive assessment and multiple structural, functional, and diffusion MRI metrics, sensori-neural tests, and CSF concentrations of tau, Aβ42 and their ratio. Both baseline values and early slope of APS scores in BIOCARD predicted later progression to MCI or AD. Presence of CSF variables strongly improved such prediction. A similarly derived APS in PREVENT-AD showed measurement invariance over time and portability to the parallel INTREPAD sample. An IRT-based APS can summarize multimodal information to provide a longitudinal measure of pre-clinical AD progression, and holds promise as an outcome for AD prevention trials.

Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least-squares Discriminant Analysis.

Background:Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province.Objective:To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS).Materials and Methods:Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed.Results:The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified.Conclusion:NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials.SUMMARY After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.

Alcohol and drug use among patients of HIV services in Hanoi, Vietnam

Aims: The Mystical Experiences Questionnaire (MEQ) was previously developed to evaluate discrete mystical-type experiences occasioned by the classic hallucinogen psilocybin. The 30-item revised MEQ (MEQ30) was previously psychometrically validated through factor analysis of retrospective accounts of profound experiences with psilocybin-containing mushrooms. We psychometrically validated the MEQ30 using data from experimental laboratory studies with controlled doses of psilocybin. Methods: We applied confirmatory factor analysis to MEQ30 responses provided immediately after moderate/high oral psilocybin dose (≥20mg/70kg) sessions (n=184) from five laboratory experiments. We used structural equation modeling to regress MEQ30 factor scores on the rated intensity of drug effects and ratings provided 3–8 weeks post-session of persisting effects attributed to the moderate to high dose psilocybin session. Results: Confirmatory factor analyses support the reliability and internal validity of the MEQ30. Structural equation models demonstrate the external and convergent validity of the MEQ30 by showing that latent variable scores on the MEQ30 positively predict persisting change in attitudes, behavior, and well-being attributed to experiences with psilocybin while controlling for the contribution of the participant-rated intensity of drug effects. Conclusions: Validation of the MEQ30 in experimental data constitutes an important step in developing this questionnaire instrument for use in pharmacological studies of mysticaltype experiences. Further experimental work should validate the MEQ30 in studies of mystical-type experiences encountered through other means. On the basis of our findings, we recommend use of the MEQ30 for future investigations of individual episodes of mystical-type experiences. Financial support: NIH T32DA007209, R01DA003889, the Heffter Research Institute, and the Council on Spiritual Practices.

Validation of the revised Mystical Experience Questionnaire in experimental sessions with psilocybin.

The 30-item revised Mystical Experience Questionnaire (MEQ30) was previously developed within an online survey of mystical-type experiences occasioned by psilocybin-containing mushrooms. The rated experiences occurred on average eight years before completion of the questionnaire. The current paper validates the MEQ30 using data from experimental studies with controlled doses of psilocybin. Data were pooled and analyzed from five laboratory experiments in which participants (n=184) received a moderate to high oral dose of psilocybin (at least 20 mg/70 kg). Results of confirmatory factor analysis demonstrate the reliability and internal validity of the MEQ30. Structural equation models demonstrate the external and convergent validity of the MEQ30 by showing that latent variable scores on the MEQ30 positively predict persisting change in attitudes, behavior, and well-being attributed to experiences with psilocybin while controlling for the contribution of the participant-rated intensity of drug effects. These findings support the use of the MEQ30 as an efficient measure of individual mystical experiences. A method to score a "complete mystical experience" that was used in previous versions of the mystical experience questionnaire is validated in the MEQ30, and a stand-alone version of the MEQ30 is provided for use in future research.

Are They Listening? Parental Social Coaching and Parenting Emotional Climate Predict Adolescent Receptivity.

Associations linking parenting emotional climate and quality of parental social coaching with young adolescents' receptivity to parental social coaching were examined (N=80). Parenting emotional climate was assessed with adolescent-reported parental warmth and hostility. Quality of parental social coaching (i.e., prosocial advice, benign framing) was assessed via parent-report and behavioral observations during a parent-adolescent discussion about negative peer evaluation. An adolescent receptivity latent variable score was derived from observations of adolescents' behavior during the discussion, change in adolescents' peer response plan following the discussion, and adolescent-reported tendency to seek social advice from the parent. Parenting climate moderated associations between coaching and receptivity: Higher quality coaching was associated with greater receptivity in the context of a more positive climate. Analyses suggested a stronger association between coaching and receptivity among younger compared to older adolescents.

Acute treatment optimization in episodic and chronic migraine: results of the American Migraine Prevalence and Prevention (AMPP) Study.

The migraine Treatment Optimization Questionnaire (mTOQ) was developed to assess response to acute treatment in persons with migraine. The original validated form used yes or no response options. This study aims to (1) assess the psychometric properties of a 6-item version of the mTOQ (mTOQ-6) using ordinal response options; (2) compare treatment optimization using the revised mTOQ-6 for both episodic and chronic migraine (EM and CM, respectively); (3) identify demographic, headache, and treatment features associated with treatment optimization. The American Migraine Prevalence and Prevention (AMPP) Study is a longitudinal, US population-based study. Annual questionnaires were mailed to a sample of 24,000 severe headache sufferers identified by screening a panel constructed to be representative of the US population. The current study included respondents to the 2006 AMPP Study survey who met modified International Classification of Headache Disorders-3 beta criteria for migraine; persons with CM (≥15 HA days/month) or EM (<15 HA days/month) were included. Acute treatment optimization was measured with the mTOQ-6. A single factor latent variable model was used to assess item characteristics. This model was expanded through structural equation models (SEM) to incorporate a contrast between persons with CM and EM on the scaled treatment optimization scores. We estimated both an unadjusted SEM and a SEM adjusted for demographic features, headache characteristics, and acute treatment. Migraine criteria were met by 8612 persons (539 for CM and 8073 for EM) who completed the mTOQ-6 as part of the 2006 AMPP Study survey. When compared, those with CM exhibited worse treatment optimization across all domains of the mTOQ-6. For example, 35.1% of CM and 44.6% of EM respondents reported being pain free at 2 hours "half the time or more" with their usual migraine medication. Latent variable model parameters indicated excellent psychometric properties of the mTOQ-6. Scaled treatment optimization scores obtained from the unadjusted SEM were significantly lower (indicating worse treatment optimization) for persons with CM (3.25) compared to persons with EM (4.01), b = -0.76, P < .0001; scores remained significantly lower for CM after adjustment with a wide array of demographic and disease severity covariates. Poor treatment optimization was associated with cutaneous allodynia, major depression, and the use of nonsteroidal anti-inflammatory drugs. Better treatment optimization was associated with the use of triptans and preventive medications. Estimates of the latent variable scores for the mTOQ-6 revealed persistent low levels of treatment optimization for both EM and CM, though treatment optimization is worse for CM.

구조식 모형에 대한 단계적 접근

Structural equation modeling (SEM) is a widely used in social sciences such as education, business administration, and psychology. In SEM, the latent variable score is the estimate of the latent variable which cannot be observed directly. This study uses stagewise structural equation modeling(stagewise SEM; SSEM) by partitioning the whole model into several stages. The traditional estimation method minimizes the discrepancy function using the variance-covariance of all observed variables. This method can lead to inappropriate situations where exogenous latent variables may be affected by endogenous latent variables. The SSEM approach can avoid such situations and reduce the complexity of the whole SEM in estimating parameters.

If at First You Don’t Succeed, Try, Try Again

Previous research has considered sequential item response theory (SIRT) models for circumstances where examinees are allowed at least one opportunity to correctly answer questions. Research suggests that employing answer-until-correct assessment frameworks with partial feedback can promote student learning and improve score precision. This article describes SIRT models for cases when test takers are allowed a finite number of repeated attempts on items. An overview of SIRT models is provided and the Rasch SIRT is discussed as a special case. Three applications are presented using assessment data from a calculus-based probability theory course. The first application estimates a Rasch SIRT model using marginal maximum likelihood and Markov chain Monte Carlo procedures and students with higher latent variable scores tend to have more knowledge and are better able to retrieve that knowledge in fewer attempts. The second application uses R to estimate growth-curve SIRT models that account for individual differences in content knowledge and recovery/retrieval rates. The third application is a multidimensional SIRT model that estimates an attempt-specific latent proficiency variable. The implications of SIRT models and answer-until-correct assessment frameworks are discussed for researchers, psychometricians, and test developers.

Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits.

Cognitive impairment is a common non-motor feature of Parkinson's disease (PD). Understanding the neural mechanisms of this deficit is crucial for the development of efficient methods for treatment monitoring and augmentation of cognitive functions in PD patients. The current study aimed to investigate resting state fMRI correlates of cognitive impairment in PD from a large-scale network perspective, and to assess the impact of dopamine deficiency on these networks. Thirty PD patients with resting state fMRI were included from the Parkinson's Progression Marker Initiative (PPMI) database. Eighteen patients from this sample were also scanned with 123I-FP-CIT SPECT. A standardized neuropsychological battery was administered, evaluating verbal memory, visuospatial, and executive cognitive domains. Image preprocessing was performed using an SPM8-based workflow, obtaining time-series from 90 regions-of-interest (ROIs) defined from the AAL brain atlas. The Brain Connectivity Toolbox (BCT) was used to extract nodal strength from all ROIs, and modularity of the cognitive circuitry determined using the meta-analytical software Neurosynth. Brain-behavior covariance patterns between cognitive functions and nodal strength were estimated using Partial Least Squares. Extracted latent variable (LV) scores were matched with the performances in the three cognitive domains (memory, visuospatial, and executive) and striatal dopamine transporter binding ratios (SBR) using linear modeling. Finally, influence of nigrostriatal dopaminergic deficiency on the modularity of the “cognitive network” was analyzed. For the range of deficits studied, better executive performance was associated with increased dorsal fronto-parietal cortical processing and inhibited subcortical and primary sensory involvement. This profile was also characterized by a relative preservation of nigrostriatal dopaminergic function. The profile associated with better memory performance correlated with increased prefronto-limbic processing, and was not associated with presynaptic striatal dopamine uptake. SBR ratios were negatively correlated with modularity of the “cognitive network,” suggesting integrative effects of the preserved nigrostriatal dopamine system on this circuitry.

The Effects of Chance Correlations on Partial Least Squares Path Modeling

Partial least squares path modeling (PLS) has been increasing in popularity as a form of or an alternative to structural equation modeling (SEM) and has currently considerable momentum in some management disciplines. Despite recent criticism toward the method, most existing studies analyzing the performance of PLS have reached positive conclusions. This article shows that most of the evidence for the usefulness of the method has been a misinterpretation. The analysis presented shows that PLS amplifies the effects of chance correlations in a unique way and this effect explains prior simulations results better than the previous interpretations. It is unlikely that a researcher would willingly amplify error, and therefore the results show that the usefulness of the PLS method is a fallacy. There are much better ways to compensate for the attenuation effect caused by using latent variable scores to estimate SEM models than creating a bias into the opposite direction.

Structural equation modeling for analyzing erythrocyte fatty acids in Framingham.

Research has shown that several types of erythrocyte fatty acids (i.e., omega-3, omega-6, and trans) are associated with risk for cardiovascular diseases. However, there are complex metabolic and dietary relations among fatty acids, which induce correlations that are typically ignored when using them as risk predictors. A latent variable approach could summarize these complex relations into a few latent variable scores for use in statistical models. Twenty-two red blood cell (RBC) fatty acids were measured in Framingham (N = 3196). The correlation matrix of the fatty acids was modeled using structural equation modeling; the model was tested for goodness-of-fit and gender invariance. Thirteen fatty acids were summarized by three latent variables, and gender invariance was rejected so separate models were developed for men and women. A score was developed for the polyunsaturated fatty acid (PUFA) latent variable, which explained about 30% of the variance in the data. The PUFA score included loadings in opposing directions among three omega-3 and three omega-6 fatty acids, and incorporated the biosynthetic and dietary relations among them. Whether the PUFA factor score can improve the performance of risk prediction in cardiovascular diseases remains to be tested.

Statistics Related Self-Efficacy A Confirmatory Factor Analysis Demonstrating a Significant Link to Prior Mathematics Experiences for Graduate Level Students

The present study examined students' statistics-related self-efficacy, as measured with the current statistics self-efficacy (CSSE) inventory developed by Finney and Schraw (2003). Structural equation modeling was used to check the confirmatory factor analysis of the one-dimensional factor of CSSE. Once confirmed, this factor was used to test whether a significant link to prior mathematics experiences exists. Additionally a new post-structural equation modeling (SEM) application was employed to compute error-free latent variable score for CSSE in an effort to examine the ancillary effects of gender, age, ethnicity, department, degree level, hours completed, expected course grade, number of college-level math classes, current GPA on students' CSSE scores. Results support the one-dimensional construct and as expected, the model demonstrated a significant link between CSSE scores and prior mathematics experiences to CSSE. Additionally the students' department, expected grade, and number of prior math classes were found to have a significant effect on student's CSSE scores.

Symptom changes in five dimensions of the Positive and Negative Syndrome Scale in refractory psychosis

Refractory psychosis units currently have little information regarding which symptoms profiles should be expected to respond to treatment. In the current study, we provide this information using structural equation modeling of Positive and Negative Syndrome Scale (PANSS) ratings at admission and discharge on a sample of 610 patients admitted to a treatment refractory psychosis program at a Canadian tertiary care unit between 1990 and 2011. The hypothesized five-dimensional structure of the PANSS fit the data well at both admission and discharge, and the latent variable scores are reported as a function of symptom dimension and diagnostic category. The results suggest that, overall, positive symptoms (POS) responded to treatment better than all other symptoms dimensions, but for the schizoaffective and bipolar groups, greater response on POS was observed relative to the schizophrenia and major depression groups. The major depression group showed the most improvement on negative symptoms and emotional distress, and the bipolar group showed the most improvement on disorganization. Schizophrenia was distinct from schizoaffective disorder in showing reduced treatment response on all symptom dimensions. These results can assist refractory psychosis units by providing information on how PANSS symptom dimensions respond to treatment and how this depends on diagnostic category.

Quantitative and qualitative symptomatic differences in individuals at Ultra-High Risk for psychosis and healthy controls

Patients at Ultra-High Risk (UHR) for developing a first psychosis vary widely in their symptom presentation and illness course. An important aim in UHR research concerns the characterization of the clinical heterogeneity in this population. We aimed to identify qualitatively and quantitatively different clinical symptom profiles at baseline and at 2-year follow-up in a group of UHR subjects and healthy controls. We employed a Latent Class Factor Analysis (LCFA) to the 19 items of the Structured Interview for Prodromal Syndromes (SIPS) ratings at baseline and at 2-year follow-up in a sample of 147 UHR subjects and 141 controls from the Dutch Prediction of Psychosis Study (DUPS) in the Netherlands. Additionally, a stepwise logistic regression analysis was performed with transition to psychosis as a dependent variable and baseline latent variable scores as predictors. Variation in symptomatology at baseline was explained by both quantitative and qualitative differences; at 2-year follow-up qualitative differences between individuals were no longer observed. Quantitative differences showed moderate stability over time (range=0.109–0.42). Within the UHR sample, transition to psychosis was significantly associated with quantitative differences in baseline SIPS scores. The results of our study suggest a ‘quasi’-continuous extended psychosis phenotype, a finding that merits replication in other samples.

Gene variants associated with antisocial behaviour: a latent variable approach

The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a 15-year follow-up of a randomized trial of a prenatal and infancy nurse-home visitation programme in Elmira, New York. We then investigated, via a novel latent variable approach, 450 informative genetic polymorphisms in 71 genes previously associated with antisocial behaviour, drug use, affiliative behaviours and stress response in 241 consenting individuals for whom DNA was available. Haplotype and Pathway analyses were also performed. Eight single-nucleotide polymorphisms (SNPs) from eight genes contributed to the latent genetic variable that in turn accounted for 16.0% of the variance within the latent antisocial phenotype. The number of risk alleles was linearly related to the latent antisocial variable scores. Haplotypes that included the putative risk alleles for all eight genes were also associated with higher latent antisocial variable scores. In addition, 33 SNPs from 63 of the remaining genes were also significant when added to the final model. Many of these genes interact on a molecular level, forming molecular networks. The results support a role for genes related to dopamine, norepinephrine, serotonin, glutamate, opioid and cholinergic signalling as well as stress response pathways in mediating susceptibility to antisocial behaviour. This preliminary study supports use of relevant behavioural indicators and latent variable approaches to study the potential 'co-action' of gene variants associated with antisocial behaviour. It also underscores the cumulative relevance of common genetic variants for understanding the aetiology of complex behaviour. If replicated in future studies, this approach may allow the identification of a 'shared' variance across genetic risk alleles associated with complex neuropsychiatric dimensional phenotypes using relatively small numbers of well-characterized research participants.

An item-level examination of the Flynn effect on the National Intelligence Test in Estonia

This study examined the Flynn effect (FE; i.e., the rise in IQ scores over time) in Estonia using the Estonian version of the National Intelligence Tests (NIT; Haggerty, Terman, Thorndike, Whipple & Yerkes, 1919; National Research Council, 1920). Using secondary data from two cohorts (1934, n=890 and 2006, n=913) of students, we analyzed the NIT's subtests using item response theory (IRT). For each subtest, we first examined invariance in all the items and then linked the latent variable (θ) scores between the two cohorts using the invariant items. The results showed that there was a FE in θ for all subtests except one, although there was much variability in the FE magnitude, ranging from an effect size of 0.24 (3.60 IQ points) to 1.05 (15.75 IQ points). In addition, this study showed there was a decrease in the variability of θ for all the subtests, although only two of the subtests showed large decreases (approximately .50 standard deviations). Last, the subtests' precision of measuring θ was very similar at both time points.

Transition from motivation to behaviour: examining the moderating role of Identification (ID) on the relationship between motives and attendance

In sports consumer behaviour literature only a small amount of variance in attendance is explained by motives. One possible explanation for this is the existence of a third factor which moderates this relationship between the motives and attendance. Individuals who strongly identify with a sports team demonstrate distinctly different behavioural patterns from weakly identified individuals. Identification may, therefore, serve as a moderator. Accordingly, two hypotheses are generated: (a) the relationship between motives and attendance intention ranges from weak to moderate; and (b) the overarching construct of Identification (Team Identification) moderates the influence of motives on attendance intention. Participants were 207 United States of America National Collegiate Athletic Association (NCAA) Division IA student-subjects. Instrumentation includes measures of motivation, points of attachment and attendance intention. Through hierarchical Confirmatory Factor Analysis, regression analyses and latent variable scores approach, the results largely support both hypotheses.