Boredom in practical English language classes: a longitudinal confirmatory factor analysis-curve of factors model

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Majid Elahi Shirvan ◽  
Elham Yazdanmehr ◽  
Tahereh Taherian ◽  
Mariusz Kruk ◽  
Mirosław Pawlak
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Language Learning ◽  
Latent Variables ◽  
English Language ◽  
Rate Of Change ◽  
English Classes ◽  
Confirmatory Factor ◽  
Longitudinal Validity ◽  
Over Time

Abstract The study aimed to examine temporal change of boredom in English classes (BPELC) and test the longitudinal validity of the boredom in practical English classes-revised (BPELC-R) scale via longitudinal confirmatory factor analysis-curve of factors model (LCFA-CFM) approach. This approach ensures measurement invariance of BPELC over time, deals with its second-order latent variables, and considers the assessment of inter-individual differences while experiencing the emotion. Data were collected from 412 EFL adult learners on four measurements using BPELC-R and were analyzed by Mplus with LCFA-CFM. The model fit was accepted, which indicates invariance of BPELC-R as well as the factor structure of the instrument including the factor loadings of its subscales over time. Without the consideration of LCFA of BPELC-R, as addressed in this study, any observed change of the construct in the course of language learning could be misinterpreted. Also, though the rate of change in boredom differed across individual L2 learners, they all experienced a decreasing trend over time. Furthermore, the negative association between the intercept and slope suggested that learners with higher initial levels of boredom experienced a steeper decrease over time. The decreasing pattern of boredom is discussed in light of the main theories of this construct.

Measurement Invariance of the Serbian Version of the Satisfaction With Life Scale Across Age, Gender, and Time

2019 ◽  
Vol 35 (4) ◽  
pp. 555-563 ◽  
Author(s):  
Veljko Jovanović
Keyword(s):  
Life Satisfaction ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Satisfaction With Life ◽  
Satisfaction With Life Scale ◽  
Confirmatory Factor ◽  
Life Scale ◽  
Serbian Version ◽  
Age Range ◽  
Over Time

Abstract. The present research aimed at examining measurement invariance of the Serbian version of the Satisfaction with Life Scale (SWLS) across age, gender, and time. A total sample in Study 1 consisted of 2,595 participants from Serbia, with a mean age of 23.79 years (age range: 14–55 years). The final sample in Study 2 included 333 Serbian undergraduate students ( Mage = 20.81; age range: 20–27 years), who completed the SWLS over periods of 6 and 18 months after the initial assessment. Confirmatory factor analysis (CFA) supported the modified unidimensional model of the SWLS, with correlated residuals of items 4 and 5 tapping past satisfaction. The results of the multigroup confirmatory factor analysis supported the full scalar invariance across gender and over time and partial scalar invariance across age. Latent mean comparisons revealed that women reported higher life satisfaction than men. Additionally, adolescents reported higher life satisfaction than students and adults, with adults showing the lowest life satisfaction. Our findings indicate that the SWLS allows meaningful comparisons in life satisfaction across age, gender, and over time.

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 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 Evaluation of the Social Benefits of Financial Innovation Towns Based on SEM Model——Taking Yuhuang Shannan Fund Town as an Example

2021 ◽  
Vol 251 ◽  
pp. 01075
Author(s):  
Guimei Wu ◽  
Yuting Ye ◽  
Ting Li ◽  
Xueqin Chen ◽  
Shasha Zhu
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Evaluation System ◽  
Structural Equation ◽  
Financial Innovation ◽  
Social Benefits ◽  
The Social ◽  
Confirmatory Factor ◽  
The Impact

Taking Yuhuang Shannan Fund Town as a typical example, this paper established a financial innovation characteristic town social benefit evaluation system through on-site investigation and quantitative analysis. It can be summarized into five major aspects: social and livelihood development, socioeconomic development, ecological environment, infrastructure construction and related system construction. Then we constructed a structural equation model (SEM) for the evaluation of social benefits of towns, and made the assumption that the impact of the five latent variables on the total variable of social benefits is positive. Through the first-order confirmatory factor analysis and the second-order confirmatory factor analysis of the structural equation, it is concluded that the five latent variables have a positively significant impact on the social benefits and have strong internal consistency. According to the degree of influence, effective suggestions are given from private equity and industrial foundation, which provide reference and practical guidance of the construction of financial innovative towns in the future.

Testing Factorial Invariance across Groups: A Reconceptualization and Proposed New Method

1999 ◽  
Vol 25 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Gordon W. Cheung ◽  
Roger B. Rensvold
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Cross Cultural ◽  
Factorial Invariance ◽  
Published Data ◽  
Data Set ◽  
Test Items ◽  
Confirmatory Factor ◽  
Different Cultures

Many cross-cultural researchers are concerned with factorial invariance; that is, with whether or not members of different cultures associate survey items, or similar measures, with similar constructs. Researchers usually test items for factorial invariance using confirmatory factor analysis (CFA). CFA, however, poses certain problems that must be dealt with. Primary among them is standardization, the process that assigns units of measurement to the constructs (latent variables). Two standardization procedures and several minor variants have been reported in the literature, but using these procedures when testing for factorial invariance can lead to inaccurate results. In this paper we review basic theory, and propose an extension of Byrne, Shavelson, and Muthgn’s (1989) procedure for identifying non-invariant items. The extended procedure solves the standardization problem by performing a systematic comparison of all pairs of factor loadings across groups. A numerical example based upon a large published data set is presented to illustrate the utility of the new procedure, particularly with regard to partial factorial invariance.

L2 GRIT

2021 ◽  
pp. 1-28
Author(s):  
Majid Elahi Shirvan ◽  
Tahereh Taherian ◽  
Elham Yazdanmehr
Keyword(s):  
Latent Variables ◽  
Measurement Errors ◽  
Second Order ◽  
Rate Of Change ◽  
Cross Sectional ◽  
Domain Specific ◽  
Confirmatory Factor ◽  
L2 Learners ◽  
Measure Change ◽  
Over Time

Abstract Given the longitudinal nature of L2 grit, the use of conventional research methodologies with cross-sectional data to examine the validity of L2 grit scale seems inadequate. The present research was an attempt to extend the domain-specific phase of research on L2 grit, with the pursuit of long-term goals at its core, into a dynamic one. Thus, we adopted a longitudinal confirmatory factor analysis-curve of factors model (LCFA-CFM) approach to trace changes in L2 learners’ grit at different points of time in an EFL course. LCFA-CFM ensures measurement invariance over time, deals with second-order latent variables, takes into account measurement errors, and is capable of assessing interindividual differences. With this in mind, we, first, employed LCFA to test the factor invariance of L2 grit based on a bifactor CFA model over time and, second, used CFM to measure change of L2 grit during an L2 course. To do so, we collected data from 437 adult EFL learners in Iran in four time phases using the L2 grit scale and analyzed them using Mplus 7.4. The model fit was accepted and invariance of the latent factor of L2 grit was confirmed over time. Also, the negative covariance between initial level of L2 grit and its rate of change over time (second-order latent variables) suggested a steeper increase in the construct over time for learners with lower initial scores of the construct. That is, L2 learners who started at a higher level of L2 grit experienced less change in L2 grit over time. The LCFA-CFM ensured that the factor structure of L2 grit is invariant over time and provided insights into how L2 grit changes over an L2 course.

The confirmatory factor analysis of teacher’s research motivation scale

2018 ◽  
Vol 8 (3) ◽  
pp. 101-106
Author(s):  
Ujsara Prasertsin
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Intrinsic Motivation ◽  
Latent Variables ◽  
Latent Variable ◽  
Extrinsic Motivation ◽  
Class Action ◽  
P Value ◽  
Confirmatory Factor ◽  
Motivation Scale

The purpose of the research is to develop the measurement of motivation scale of in class action research conducted by school teachers. The sampling is 403 teachers, subordinated to Office of The Basic Education Commission. Data collection was conducted through questionnaires of 20 questions. The questions were designed into 5 levels following to the motivation scale in research measurement of Deemer, Mahoney, & Ball (2010). This 20 questions questionnaire is consisting of 3 latent variables that are 9 questions of intrinsic motivation, 6 questions of failure avoidance and 5 questions extrinsic motivation. The purpose of confirmatory factor analysis (CFA) is to test the construct validity of research latent variables that found the harmony correlation of empirical data contained in this research model, the value of Chi-Square ( )=89.224 at the degree of freedom=71, P value=0.071, GFI=0.978, AGFI=0.936, RMSEA=0.062, RMR=0.018, Model AIC=367.224, Saturated AIC=420.000, Model CAIC= 1062.076, Saturated CAIC = 1469.777. The weight factors of latent variable are 0.692, -0.066 and 0.894 retrospectively. The value of reliability according to cronbach’s alpha coefficient of correlation is 0.479, 0.004 and 0.800 retrospectively. Moreover correlation matrix of 20 observed variables shows the correlation among latent variables of intrinsic motivation and extrinsic motivation with the significant level of statistic correlation at 0.05, the correlation value ranged between 0.196-0.604 and 0.196-0.696 retrospectively. The highest value of correlation scored 0.696 is founded in observed variables of intrinsic motivation latent variable. Keywords: Confirmatory, factor analysis, teacher, research motivation

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.

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