Factor analysis with (mixed) observed and latent variables in the exponential family

Psychometrika ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 515-530 ◽  
Author(s):  
Michel Wedel ◽  
Wagner A. Kamakura
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Exponential Family

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

scholarly journals An evaluation of the factor structure of the Self-Assessed Wisdom Scale (SAWS) and the creation of the SAWS-15 as a short measure for personal wisdom

2021 ◽  
pp. 1-11
Author(s):  
Trilas M. Leeman ◽  
Bob G. Knight ◽  
Erich C. Fein ◽  
Sonya Winterbotham ◽  
Jeffrey Dean Webster
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Latent Variables ◽  
Emotional Regulation ◽  
Short Form ◽  
Age Groups ◽  
Total Sample ◽  
Future Research ◽  
Personal Wisdom ◽  
Age Range

ABSTRACT Objectives: Although wisdom is a desirable life span developmental goal, researchers have often lacked brief and reliable construct measures. We examined whether an abbreviated set of items could be empirically derived from the popular 40-item five-factor Self-Assessed Wisdom Scale (SAWS). Design: Survey data from 709 respondents were randomly split into two and analyzed using confirmatory factor analysis (CFA). Setting: The survey was conducted online in Australia. Participants: The total sample consisted of 709 participants (M age = 35.67 years; age range = 15–92 years) of whom 22% were male, and 78% female. Measurement: The study analyzed the 40-item SAWS. Results: Sample 1 showed the traditional five-factor structure for the 40-item SAWS did not fit the data. Exploratory factor analysis (EFA) on Sample 2 offered an alternative model based on a 15-item, five-factor solution with the latent variables Reminiscence/Reflection, Humor, Emotional Regulation, Experience, and Openness. This model, which replicates the factor structure of the original 40-item SAWS with a short form of 15 items, was then confirmed on Sample 1 using a CFA that produced acceptable fit and measurement invariance across age groups. Conclusions: We suggest the abbreviated SAWS-15 can be useful as a measure of individual differences in wisdom, and we highlight areas for future research.

scholarly journals Heterogeneous Beliefs and Risk-Neutral Skewness

2012 ◽  
Vol 47 (4) ◽  
pp. 851-872 ◽  
Author(s):  
Geoffrey C. Friesen ◽  
Yi Zhang ◽  
Thomas S. Zorn
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Stock Options ◽  
Systematic Risk ◽  
Heterogeneous Beliefs ◽  
Firm Level ◽  
Cross Sectional ◽  
Skewness Factor ◽  
Risk Neutral ◽  
Using Data

AbstractThis study tests whether belief differences affect the cross-sectional variation of risk-neutral skewness using data on firm-level stock options traded on the Chicago Board Options Exchange from 2003 to 2006. We find that stocks with greater belief differences have more negative skews, even after controlling for systematic risk and other firm-level variables known to affect skewness. Factor analysis identifies latent variables linked to risk and belief differences. The belief factor explains more variation in the risk-neutral skewness than the risk-based factor. Our results suggest that belief differences may be one of the unexplained firm-specific components affecting skewness.

A structural equation model for big data adoption in the healthcare supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dindayal Agrawal ◽  
Jitender Madaan
Keyword(s):  
Factor Analysis ◽  
Big Data ◽  
Supply Chain ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Model ◽  
Equation Modeling ◽  
Data Governance ◽  
Social Perspective ◽  
Content Type

PurposeThe purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).Design/methodology/approachFirst, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.FindingsThe segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”Research limitations/implicationsIn literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.Originality/valueThis paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

scholarly journals Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire

2019 ◽  
Vol 30 (2) ◽  
pp. 1033-1040 ◽  
Author(s):  
Yfke P. Ongena ◽  
Marieke Haan ◽  
Derya Yakar ◽  
Thomas C. Kwee
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Internal Consistency ◽  
Latent Variables ◽  
Procedural Knowledge ◽  
Patient Questionnaire ◽  
Successful Implementation ◽  
Personal Interaction ◽  
Five Factors

Abstract Objectives The patients’ view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of this article is to develop and validate a standardized patient questionnaire on the implementation of AI in radiology. Methods Six domains derived from a previous qualitative study were used to develop a questionnaire, and cognitive interviews were used as pretest method. One hundred fifty-five patients scheduled for CT, MRI, and/or conventional radiography filled out the questionnaire. To find underlying latent variables, we used exploratory factor analysis with principal axis factoring and oblique promax rotation. Internal consistency of the factors was measured with Cronbach’s alpha and composite reliability. Results The exploratory factor analysis revealed five factors on AI in radiology: (1) distrust and accountability (overall, patients were moderately negative on this subject), (2) procedural knowledge (patients generally indicated the need for their active engagement), (3) personal interaction (overall, patients preferred personal interaction), (4) efficiency (overall, patients were ambiguous on this subject), and (5) being informed (overall, scores on these items were not outspoken within this factor). Internal consistency was good for three factors (1, 2, and 3), and acceptable for two (4 and 5). Conclusions This study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection with confirmatory factor analysis may provide further refinement of the scale. Key Points • Although AI systems are increasingly developed, not much is known about patients’ views on AI in radiology. • Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients’ views on AI in radiology, revealing five factors. • Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology.

scholarly journals Variational Inference over Nonstationary Data Streams for Exponential Family Models

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1942
Author(s):  
Andrés R. Masegosa ◽  
Darío Ramos-López ◽  
Antonio Salmerón ◽  
Helge Langseth ◽  
Thomas D. Nielsen
Keyword(s):  
Bayesian Inference ◽  
Variational Methods ◽  
Latent Variables ◽  
Exponential Family ◽  
Concept Drift ◽  
Variational Inference ◽  
Model Parameters ◽  
Time Step ◽  
Data Set ◽  
Family Models

In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons. First, it requires continuous model updating and the ability to handle a posterior distribution conditioned on an unbounded data set. Secondly, the underlying data distribution may drift from one time step to another, and the classic i.i.d. (independent and identically distributed), or data exchangeability assumption does not hold anymore. In this paper, we present an approximate Bayesian inference approach using variational methods that addresses these issues for conjugate exponential family models with latent variables. Our proposal makes use of a novel scheme based on hierarchical priors to explicitly model temporal changes of the model parameters. We show how this approach induces an exponential forgetting mechanism with adaptive forgetting rates. The method is able to capture the smoothness of the concept drift, ranging from no drift to abrupt drift. The proposed variational inference scheme maintains the computational efficiency of variational methods over conjugate models, which is critical in streaming settings. The approach is validated on four different domains (energy, finance, geolocation, and text) using four real-world data sets.

scholarly journals Formation of Social Capital for Community College Students: A Second-Order Confirmatory Factor Analysis

2018 ◽  
Vol 47 (1) ◽  
pp. 3-30 ◽  
Author(s):  
Yu (April) Chen ◽  
Soko S. Starobin
Keyword(s):  
College Students ◽  
Community College ◽  
Social Capital ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Community College Students ◽  
Latent Variables ◽  
Institutional Agents ◽  
Family Social Capital ◽  
Confirmatory Factor

Objective: This quantitative study constructed a statistical model to measure family social capital and college social capital among community college students. The authors also examined influences of these two types of social capital constructs on degree aspiration. Method: This study utilized the STEM (Science, Technology, Engineering and Mathematics) Student Success Literacy Survey (SSSL) to collect data in all 15 community college districts in Iowa. With more than 5,000 responses, the authors conducted descriptive analysis, exploratory and confirmatory factor analysis, and structural equation modeling (SEM) analysis. Results: College social capital was measured by three latent variables such as interaction with advisors, interaction with faculty members, and transfer capital. The three latent variables were further measured by 14 survey items. Family social capital was measured by six survey items that described parent–child interaction in high school. The SEM results indicated that college social capital had stronger direct influences on degree aspiration compared with family social capital. The impact of family social capital was delivered through the mediation of college social capital. Contributions: Findings contributed to the literature by emphasizing the important role of institutional agents in promoting degree aspiration. Intervention programs should be implemented to encourage interactions between institutional agents and underrepresented and disadvantaged students.

scholarly journals DETERMINANTS OF ACADEMIC RESEARCH COMMERCIALIZATION IN IRAN GAS INDUSTRY

2013 ◽  
Vol 11 (1) ◽  
pp. 34-49
Author(s):  
Yaghoub Zahedi Anbardan
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Research Methodology ◽  
Latent Variables ◽  
Qualitative Data ◽  
Academic Research ◽  
Gas Industry ◽  
Research Commercialization ◽  
Confirmatory Factor ◽  
Quantitative Results

The aim of this research is to identify determinants of academic research commercialization in the Iranian gas industry. For this purpose, we have applied a mixed research methodology. After reviewing the literature we conducted interviews with academics that have experience in the gas industry commercialization in order to develop the research questionnaire. Qualitative data were analyzed by codifying the interviews. To analyze the quantitative results we applied the exploratory and confirmatory factor analysis (EFA, CFA). The results show that there are 6 latent variables and 28 observed variables including the gas industry academic research commercialization requirements and prerequisites in Iran.

scholarly journals Psychometric properties of a Serbian version of the State-Trait Anxiety Inventory X-2

2020 ◽  
Vol 12 (2) ◽  
pp. 13-22
Author(s):  
Damjan Jakšić ◽  
◽  
Jovana Trbojević Jocić ◽  
Stefan Maričić ◽  
• Bülent Okan Miçooğulları ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Positive Affect ◽  
Trait Anxiety ◽  
Latent Variables ◽  
The State ◽  
State Trait Anxiety Inventory ◽  
Internal Reliability ◽  
Male And Female ◽  
Latent Space ◽  
Significant Difference

Given that anxiety is a significant individual disposition that affects sports performance, this study aimed to verify the latent variables obtained by the State – Trait Anxiety Inventory X-2, and to compare the obtained factor solutions with the proposed model of latent variables. The second aim refers to the presentation of the results of the questionnaire State – Trait Anxiety Inventory X-2 answered by Serbian male and female handball players. The research was conducted on a sample of 170 respondents, 99 male and 71 female handball players, aged 14 to 39 years (average age = 21.9) with an average handball playing career of 9.39 years. Towards the end of 2019, the respondents filled in the above-mentioned questionnaire used to assess anxiety as a personality trait. Exploratory factor analysis with certain modifications of the algorithm was applied to determine accurately latent space. The overall analysis was performed using the R statistical package. Four factors were selected based on the conducted factor analysis of the latent space of the questionnaire. Ten, five, three and two items are projected on the first, second, third and fourth factor respectively. Since internal reliability of the fourth factor was 0.394 it was excluded from further consideration and interpretation. The achieved scores on four separate factors show that Serbian male and female handball players achieve average results on the first (Presence of Trait anxiety) and the third factor (Absence of proactivity), while they achieve scores above the average on the second factor (Positive affect). In order to examine the sex differences between the examined variables, a one-way analysis of variance was performed. The findings show that there is no statistically significant difference on all three factors between the scores achieved by male and female handball players. A statistically significant difference between handball players of different competitive ranks appeared only on the Positive affect factor between the top athletes and the athletes in the second league (p = .04).

scholarly journals Choosing the number of factors in independent factor analysis model

2005 ◽  
Vol 2 (2) ◽  
Author(s):  
Cinzia Viroli
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Latent Variable ◽  
Information Criteria ◽  
Ratio Test ◽  
Independent Factor ◽  
Analysis Model ◽  
Variable Model ◽  
Factor Analysis Model ◽  
Independent Factor Analysis

Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as a way to model a set of observed variables through linear combinations of hidden independent ones plus a noise term. Despite the peculiarity of its origin the method can be framed within the latent variable model domain and some parallels with the ordinary factor analysis can be drawn. If no prior information on the latent structure is available a relevant issue concerns the correct specification of the model. In this work some methods to detect the number of significant latent variables are investigated. Moreover, since the method defines a probability density function for the latent variables by mixtures of gaussians, the correct number of mixture components must also be determined. This issue will be treated according to two main approaches. The first one amounts to carry out a likelihood ratio test. The other one is based on a penalized form of the likelihood, that leads to the so called information criteria. Some simulations and empirical results on real data sets are finally presented.

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