Confirmatory Factor Analysis (CFA) as an Analytical Technique to Assess Measurement Error in Survey Research

Mishra, Manit

doi:10.1177/0971890716672933

Cited by 35 publications

(25 citation statements)

References 57 publications

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“…In order to conduct a confirmatory factor analysis, we checked the Bartlett's sphericity test to ensure inter-item co ela ion [ 2 = 1493.61, df = 120, p < .001] and the Kaiser-Meyer-Olkin Indice [KMO= .79] for the sample adequacy (Cerny & Kaiser, 1977;Dziuban & Shirkey, 1974;IBM, 2011). To test the reliability of the proposed structure we conducted a confirmatory factor analysis (CFA) with a structural model using AMOS plugin in SPSS (figure 3) using a variance-covariance matrix with maximum likelihood (ML) estimation (Mishra, 2016). ML estimation is more reliable in many cases than others and is widely used (Bollen, 1989).…”

Section: Resultsmentioning

confidence: 99%

Perception and Evaluation in Human–Robot Interaction: The Human–Robot Interaction Evaluation Scale (HRIES)—A Multicomponent Approach of Anthropomorphism

Spatola

Kühnlenz

Cheng

2021

Int J of Soc Robotics

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The evaluation of how (human) individuals perceive robots is a central issue to better understand Human-Robot Interaction (HRI). On this topic, promising proposals have emerged. However, present tools are not able to assess a sufficient part of the composite psychological dimensions involved in the evaluation of human-robot interaction. Indeed, the percentage of variance explained is often under the recommended threshold for a construct to be valid. In this article, we consolidate the lessons learned from three different studies and propose a further developed questionnaire based on a multicomponent approach of anthropomorphism by adding traits from psychosocial theory about the perception of others and the attribution and deprivation of human characteristics: the de-humanization theory. Among these characteristics, the attribution of agency is of main interest in the field of social robotics as it has been argued that robots could be considered as intentional agents. Factor analyses reveal a four sub-dimensions scale including Sociability, Agency, Animacy, and the Disturbance. We discuss the implication(s) of these dimensions on future perception of and attitudes towards robots.

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Section: Resultsmentioning

confidence: 99%

Perception and Evaluation in Human–Robot Interaction: The Human–Robot Interaction Evaluation Scale (HRIES)—A Multicomponent Approach of Anthropomorphism

Spatola

Kühnlenz

Cheng

2021

Int J of Soc Robotics

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“…The chi‐square test is highly sensitive to sample size, a high correlation between the dimensions in the questionnaire and error variance in the model (Kline, 2011). Thus, other fit indexes often receive more attention (Mishra, 2016; Polit & Yang, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…According to Mishra (2016), some plausible explanations of error variances might be that respondents have limited experience with the construct, the respondents might not have understood the meaning of the items, or they respond according to social desirability. Cote and Buckley (1987) claim that abstract constructs may be more challenging to measure than concrete constructs are and measurement error in social science research within the education discipline accounts for 30.5% of the variance.…”

Section: Discussionmentioning

confidence: 99%

Reliability and structural validity of the Norwegian version of the TeamSTEPPS Teamwork Attitudes Questionnaire: A cross‐sectional study among Bachelor of Nursing students

et al. 2020

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Aim To test the reliability and structural validity of the Norwegian version of the TeamSTEPPS ® Teamwork Attitudes Questionnaire (T‐TAQ) among Bachelor of Nursing students. Design Cross‐sectional study. Methods Bachelor of Nursing students ( N = 1,624) at three campuses in different regions of Norway were invited to complete the survey. The data were analysed with descriptive statistics, Cronbach's alpha and confirmatory factor analysis (CFA). Three models were tested. Model 3 was a post hoc modification with a correlation between four negatively worded items. The data was collected in September 2018 and May‐June 2019. Results A total of 509 students were included in the study. Cronbach's alpha ranged from 0.44–0.70 for the dimensions and was 0.79 for the total questionnaire. The fit indexes of model 3 were as follows: RMSEA = 0.043, chi‐square = 724.3 ( p < .000), normed chi‐square = 1.862, TLI = 0.812 and CFI = 0.832. The questionnaire shows some potential to display attitudes towards teamwork in health care among Bachelor of Nursing students. Low Cronbach's alpha in the dimensions might indicate that the questionnaire should be considered used as a unidimensional questionnaire.

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“…To mitigate the problem, Podsakoff et al (2012) recommend the following steps: (a) detect one or more likely sources of method bias, (b) manipulate them in the design of the study, and (c) test if the hypothesised estimates of the relationships among the constructs generalise across conditions. Sources of method bias are detected by observing the most extreme responses (MRS), which are items with the highest loading factor in confirmatory factor analysis (Mishra, 2016). Those items are excluded, and the model is recalculated.…”

Section: Instrument Developmentmentioning

confidence: 99%

Nascent entrepreneurs of millennial generations in the emerging market of Indonesia

Basuki¹,

Widyanti²,

Rajiani³

2021

EBER

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The objective of the article is to investigate the empirical linkages between personality, cultural values, and entrepreneurial characteristics and entrepreneurial behaviour in the context of SMEs' run by millennial generations in Indonesia. Research Design & Methods:This study used survey methods to reveal the relationship between variables through hypotheses testing on 551 respondents from among SMEs' owners who just started business (nascent entrepreneur) in Banjarmasin, Indonesia, with the use of structural equation modelling. Findings: Although the results prove that personality, cultural values, and entrepreneurial characteristics significantly affect entrepreneurial behaviour, the entrepreneurial characteristics that distinguish entrepreneur from non-entrepreneurs are rated the lowest. The impact of low self-efficacy, which does not support the implementation of entrepreneurial activities, makes it difficult to justify the millennial entrepreneur respondents are genuine to become the businessman or they are naively pursuing an unfeasible or inoperable opportunity. Implications & Recommendations: Since the extant literature is very scarce in fully addressing the new generation of entrepreneurs, our model can be used to identify unique characteristics of millennial entrepreneurs from emerging market countries. Contribution & Value Added: Up to this point, the majority of research in the field originated from Englishspeaking countries. The current study provides additional evidence on the entrepreneurial tendency of millennial Indonesians, which contributes to the growing international research on this generation. Article type:research article

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Confirmatory Factor Analysis (CFA) as an Analytical Technique to Assess Measurement Error in Survey Research

Cited by 35 publications

References 57 publications

Perception and Evaluation in Human–Robot Interaction: The Human–Robot Interaction Evaluation Scale (HRIES)—A Multicomponent Approach of Anthropomorphism

Perception and Evaluation in Human–Robot Interaction: The Human–Robot Interaction Evaluation Scale (HRIES)—A Multicomponent Approach of Anthropomorphism

Reliability and structural validity of the Norwegian version of the TeamSTEPPS Teamwork Attitudes Questionnaire: A cross‐sectional study among Bachelor of Nursing students

Nascent entrepreneurs of millennial generations in the emerging market of Indonesia

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