Dimensional Reduction of Word-Frequency Data as a Substitute for Intersubjective Content Analysis

2004 ◽  
Vol 12 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Adam F. Simon ◽  
Michael Xenos
Keyword(s):  
Factor Analysis ◽  
Content Analysis ◽  
Dimensional Reduction ◽  
Semantic Analysis ◽  
Data Driven ◽  
Data Preparation ◽  
Factor Analyses ◽  
Exploratory Factor Analyses ◽  
Survey Responses ◽  
Word Frequencies

This paper presents a method for using dimensional reduction in the analysis of political content. We draw inspiration from latent semantic analysis (LSA) theory, which posits that factor analysis can successfully model human language. We suggest that the factor analysis of word frequencies generated from any political text—for example, open-ended survey responses—provides adequate content analysis categories and can substitute for more commonly practiced techniques. The method proceeds in three steps: data preparation, exploratory factor analyses, and hypothesis testing. This method may produce other benefits by allowing the data to speak more clearly in the development of coding dictionaries while avoiding the problems of inferential circularity common in other data-driven approaches. We demonstrate the method using responses collected in the execution of an experimental design dealing with the topic of partial-birth abortion and assess the demonstration by presenting a human coding of the same material.

Sources of Sport Confidence of Master Athletes

2004 ◽  
Vol 26 (3) ◽  
pp. 369-384 ◽  
Author(s):  
Rodney C. Wilson ◽  
Philip J. Sullivan ◽  
Nicholas D. Myers ◽  
Deborah L. Feltz
Keyword(s):  
Factor Analysis ◽  
Regression Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Structure ◽  
Track And Field ◽  
Factor Analyses ◽  
Master Athletes ◽  
Mental Preparation ◽  
Confirmatory Factor ◽  
Exploratory Factor Analyses

This study examined sources of sport confidence and their relationship to trait sport confidence with master athletes. The study employed 216 athletes from 50 to 96 years of age in track and field, tennis, and swimming, using the Sources of Sport Confidence Questionnaire (SSCQ; Vealey, Hayashi, Garner-Holman, & Giacobbi, 1998). Confirmatory factor analysis failed to replicate the proposed 9-factor structure of the SSCQ. Exploratory factor analyses revealed an 8-factor structure with similar factors to the SSCQ, but with fewer items and the elimination of the situational favorableness factor. Physical/mental preparation and mastery were the highest ranked sources among the athletes. A simultaneous multiple regression analysis indicated that physical/mental preparation and demonstration of ability were significant predictors of trait sport confidence for master athletes. Our findings suggest that the SSCQ needs more psychometric work if it is to be used with this type of population.

scholarly journals Cross-Cultural Adaptation and Lingual Validation of the Beliefs about Medicines Questionnaire (BMQ)-Specific for Cholesterol Lowering Drugs in the Visegrad Countries

2020 ◽  
Vol 17 (20) ◽  
pp. 7616
Author(s):  
Klára Boruzs ◽  
Viktor Dombrádi ◽  
János Sándor ◽  
Gábor Bányai ◽  
Robert Horne ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Cultural Adaptation ◽  
Factor Analyses ◽  
Cholesterol Lowering ◽  
Beliefs About Medicines ◽  
Polish Question ◽  
Confirmatory Factor ◽  
Exploratory Factor Analyses ◽  
Target Languages ◽  
Visegrád Countries

The goal of this study was to translate the Beliefs about Medicines Questionnaire—Specific (BMQ-Specific) for cholesterol-lowering drugs, into the Hungarian, Slovak, Czech and Polish languages and test their reliability with statistical methods. For this purpose, Cronbach’s alpha, confirmatory and exploratory factor analyses were conducted. The analyses included 235 Czech, 205 Hungarian, 200 Polish, and 200 Slovak respondents, all of whom were taking cholesterol-lowering drugs. The translations from English into the target languages were always done by two independent translators. As part of the validation process these translations were pilot tested and after the necessary alterations, they were translated back into English by a third translator. After the approval by the creator of the questionnaire, nationwide surveys were conducted in all four countries. The results of the confirmatory factor analysis were exceptionally good for the Czech and Slovak translations, while the Polish and Hungarian translations marginally crossed the predetermined thresholds. With the exception of a single Polish question, the results of the exploratory factor analysis were deemed acceptable. The translated versions of BMQ-Specific are reliable and valid tools to assess patients’ beliefs about medication, especially medication adherence among patients taking cholesterol-lowering medication. A comparison between the four countries with this questionnaire is now possible.

Exploratory Factor Analysis of the French Version of the Big Five Questionnaire for Children (BFQ-C)

2017 ◽  
Vol 76 (3) ◽  
pp. 125-130 ◽  
Author(s):  
Martine Bouvard ◽  
Jean-Luc Roulin
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Big Five ◽  
Goodness Of Fit ◽  
Internal Validity ◽  
Factor Analyses ◽  
Confirmatory Factor Analyses ◽  
French Version ◽  
Exploratory Factor Analyses

Abstract. This article examines the internal validity of the French version of the Big Five Questionnaire for Children (BFQ-C). We first performed confirmatory factor analyses (CFA) to determine the fit of the factor structure identified in previous research on the BFQ-C and then used exploratory factor analyses. A sample of 399 children (192 boys and 207 girls) recruited from elementary schools completed the BFQ-C in their classrooms. Participants were 8 to 12 years old with a mean age of 9.58 years (SD = 0.98). The results indicated poor goodness-of-fit statistics for the CFA solution. Exploratory factor analysis improved the model fit markedly over conventional CFA. The factor structure of the French version of the BFQ-C suggested reasonable fit for the five intercorrelated factors corresponding to the Big Five with the exploratory factor analyses, even if the fifth factor appeared to be more problematic than the other 4 factors.

Exploratory and Confirmatory Factor Analysis

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Music Education ◽  
Latent Variables ◽  
Data Driven ◽  
Factor Analyses ◽  
Statistical Tool ◽  
Analysis Techniques ◽  
Confirmatory Factor ◽  
Research Questions

This chapter consists of data-driven examples of how factor analysis as a statistical tool can be applied in music education research. The chapter presents examples of how factor analysis methods can be used to identify latent variables, which in turn can be used to represent a broad set of measured variables. Exploratory and confirmatory factor analysis techniques are compared and illustrated with data-driven examples. The examples highlight some of the major considerations and basic steps for performing factor analyses so that the reader can begin to imagine how to apply this technique to their own research questions.

scholarly journals The Hateful Eight (H8): An Efficient Multifaceted Approach to the Short Dark Tetrad (SD4)

2020 ◽  
Author(s):  
Gregory D. Webster ◽  
Val Wongsomboon
Keyword(s):  
Factor Analysis ◽  
Hierarchical Models ◽  
Factor Model ◽  
Verbal Abuse ◽  
Sex Partners ◽  
Factor Analyses ◽  
Multifaceted Approach ◽  
Confirmatory Factor ◽  
Exploratory Factor Analyses ◽  
Better Than

Using the 28 items of the Short Dark Tetrad (SD4; Paulhus et al., 2020), we developed an alternative 16-item version with eight facets called the Hateful Eight (H8). Over 450 undergraduate participants completed the SD4 and two sexual behavior items. We split the sample into exploratory and confirmatory halves. Exploratory factor analyses showed that each SD4 trait—Machiavellianism, narcissism, psychopathy, and sadism—had a two-factor model that fit significantly better than a one-factor model. Taking the two highest-loading items from each factor for each trait yielded eight facets, which we labeled deviousness and scheming (Machiavellianism); leadership and exceptionalism (narcissism); defiance and recklessness (psychopathy); and violent voyeurism and verbal abuse (sadism). Confirmatory factor analysis using both split-half and whole samples supported both an eight-factor/facet solution and hierarchical models in which the eight facets loaded onto the four Dark Tetrad traits, which in turn loaded onto a global Dark Tetrad factor. Participant sex interacted with the H8 composite score in predicting number of sex partners; men showed stronger H8–sex-partners slopes than women. Showing the utility of the H8’s hierarchical, multifaceted structure, this H8-by-sex interaction effect was driven by its narcissism trait, which was in turn driven by its exceptionalism facet.

scholarly journals Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yumeng Sun
Keyword(s):  
Factor Analysis ◽  
Network Model ◽  
Semantic Analysis ◽  
Technical Problem ◽  
Machine Learning Algorithms ◽  
Telecommunication Networks ◽  
Data Driven ◽  
Traffic Data ◽  
Nonlinear Simulation ◽  
Over The Top

The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.

Assessing professionals' adoption readiness for eMental Health: Development and Validation of the eMental Health Adoption Readiness (eMHAR) Scale (Preprint)

2021 ◽  
Author(s):  
Milou Feijt ◽  
Yvonne de Kort ◽  
Joyce Westerink ◽  
Joyce Bierbooms ◽  
Inge Bongers ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Structure ◽  
Internal Consistency ◽  
Healthcare Professionals ◽  
Mental Healthcare ◽  
Factor Analyses ◽  
Confirmatory Factor ◽  
Exploratory Factor Analyses ◽  
Three Factor

BACKGROUND Over the last decades, significant advances have been made in the development of digital tools and applications for mental healthcare. Yet, despite growing evidence for their effectiveness, their acceptance and use in clinical practice remain low. To gain further insights in the process of eMental Health adoption and to facilitate future research on this topic, a validated and easy-to-use instrument to assess professionals' readiness to adopt eMental Health is necessary. OBJECTIVE The aim of this study was to develop and validate an instrument for assessing mental healthcare professionals' adoption readiness for eMental Health. METHODS Item generation was guided by literature and input from mental healthcare professionals and experts in survey development. Exploratory factor analyses were conducted on an initial set of 29 items completed by a sample of mental healthcare professionals (N = 432), after which the scale was reduced to 15 items in an iterative process. The factor structure thus obtained was subsequently tested with a confirmatory factor analysis with a second sample of mental healthcare professionals (N = 363). Internal consistency, convergent validity and predictive validity of the eMHAR Scale were assessed. RESULTS Exploratory factor analyses resulted in a three-factor solution with 15 items. The factors were analyzed and labeled as ‘perceived benefits and applicability of EMH’, ‘EMH proactive innovation’, and ‘EMH self-efficacy’. These factors were confirmed through a confirmatory factor analysis. The total scale and subscales showed good internal consistency (Cronbach’s alpha = 0.73-0.88) and acceptable convergent and predictive relations to related constructs. CONCLUSIONS The constructed eMHAR Scale showed a conceptually interpretable three-factor structure with satisfactory characteristics and relationships with relevant concepts. Its ease of use allows for a quick acquisition of data that can contribute to understanding and facilitating the process of adoption of eMental Health by clinical professionals.

Multidimensional Properties of the Access to Categories of Experience Scale1

2003 ◽  
Vol 19 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Peter A. Creed ◽  
M. Anthony Machin
Keyword(s):  
Factor Analysis ◽  
Construct Validity ◽  
General Health Questionnaire ◽  
Original Sample ◽  
Health Questionnaire ◽  
Factor Analyses ◽  
Confirmatory Factor ◽  
Exploratory Factor Analyses ◽  
Deprivation Model ◽  
Made In

Summary: This paper tests the factor structure and construct validity of the Access to Categories of Experience scale (ACE; Evans, 1986 ), which has been widely used to measure the five latent benefits of employment proposed by Jahoda (1981) . The ACE and the General Health Questionnaire (GHQ; Goldberg, 1972 ) were administered to 425 unemployed individuals. This original sample was randomly split into two subsamples. Exploratory factor analyses conducted on the first subsample demonstrated four rather than the five separate factors for the ACE scale, which has been asserted by its development and its previous use. The scale was dominated by one major factor of Activity. Confirmatory factor analysis conducted on the second subsample demonstrated a better fit for the four factors rather than the five. Construct validity was established for the ACE scale. When the identified ACE factors were correlated with the GHQ, higher levels of access to the latent benefits was associated with lower levels of psychological distress, although this result was not identified for all factors. The implications of this finding for the latent deprivation model ( Jahoda, 1981 ) are discussed, and recommendations are made in relation to the use of the ACE scale in this area of research.

Factor Analysis of the Model Constructs Suggested by Kolb's Learning Skills Profile

1995 ◽  
Vol 80 (2) ◽  
pp. 479-486
Author(s):  
Gary Kaskowitz
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Factor Model ◽  
Middle Level ◽  
Experiential Learning Theory ◽  
Factor Analyses ◽  
Learning Skills ◽  
Exploratory Factor Analyses ◽  
Confirmatory Analysis ◽  
Loading Scheme

Data from 236 MBA and middle-level managers who completed Kolb's Learning Skills Profile were analyzed using confirmatory and exploratory factor analyses to assess whether Kolb's model of Experiential Learning Theory could be validated at the performance level. An analysis of the Learning Skills Profile correlation matrix showed that Kolb's proposed four factors could be found although not without significant intercorrelation. The two-factor model suggested by the theory, i.e., Grasping of experience and Transformation of experience, did not appear in a confirmatory analysis. Although some support could be found for the factor loadings suggested by Kolb, an alternative loading scheme was found through exploratory factor analysis, wherein results suggested that other explanations might be found for the underlying latent variables proposed by Kolb.

scholarly journals Exploratory Factor Analyses of the CAHPS® Hospital Pilot Survey Responses across and within Medical, Surgical, and Obstetric Services

2005 ◽  
Vol 40 (6p2) ◽  
pp. 2078-2095 ◽  
Author(s):  
A. James O'Malley ◽  
Alan M. Zaslavsky ◽  
Ron D. Hays ◽  
Kimberly A. Hepner ◽  
San Keller ◽  
...  
Keyword(s):  
Factor Analyses ◽  
Pilot Survey ◽  
Exploratory Factor Analyses ◽  
Obstetric Services ◽  
Survey Responses
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