Using the Statistical Program R Instead of SPSS To Analyze Data

Tang, Hui; Ji, Pengsheng

doi:10.1021/bk-2014-1166.ch008

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…Engaging consumers (Arslan et al, 2020). R is as accurate as AMOS (Tang and Ji, 2014). A two-step process was carried out to analyse the data as follows (Reyes-Menendez et al, 2018):…”

Section: Resultsmentioning

confidence: 99%

Engaging consumers through firm-generated content on Instagram

Ballester

Ruiz

2021

SJME

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Purpose The purpose of this paper is to analyse the impact of consumers’ perceptions of the enjoyment and originality of firm-generated content (FGC) posted on Instagram on affective customer engagement (CE). In addition, an examination is undertaken of affective CE as a driver of customer behaviour. Design/methodology/approach The paper takes a quantitative approach using a sample of 334 women followers of an eco-friendly restaurant Instagram account. After validation of the measurement scales, the hypotheses were tested through structural equation modelling. Drawing on the stimuli-organism-response framework the authors posit that consumers’ perceptions of the enjoyment and originality of Instagram posts generate affective CE, which, in turn, influences customer behaviour. Findings The results showed that the perceived enjoyment and perceived originality of Instagram posts generated by an eco-friendly restaurant have a positive influence on affective CE, which, in turn, affects consumers’ recommendation behaviours, intention to follow the restaurant’s advice on Instagram and intention to revisit the restaurant. Originality/value This research provides novel insights into how the perceived enjoyment and originality of FGC posted on Instagram increases women’s affective engagement and expands knowledge of how affective CE might increase positive electronic word-of-mouth, intention to follow the restaurant’s advice and repurchase intentions.

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“…Engaging consumers (Arslan et al, 2020). R is as accurate as AMOS (Tang and Ji, 2014). A two-step process was carried out to analyse the data as follows (Reyes-Menendez et al, 2018):…”

Section: Resultsmentioning

confidence: 99%

Engaging consumers through firm-generated content on Instagram

Ballester

Ruiz

2021

SJME

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“…While there are many benefits of using a factor analysis approach to psychometric data analysis, such as obtaining validity evidence in addition to reliability evidence, there are also difficulties associated with moving to this approach. First, it is likely that some of the popularity of alpha in CER has arisen due to the ease of obtaining it in software, like SSPS, frequently used for data analysis. , Even with the increasing user-friendliness of R and the availability of functions for computing additional reliability coefficients beyond alpha, moving away from alpha still represents an additional layer of difficulty for most researchers.…”

Section: A Factor Analysis Approach To Reliablitymentioning

confidence: 99%

“…First, it is likely that some of the popularity of alpha in CER has arisen due to the ease of obtaining it in software, like SSPS, frequently used for data analysis. 64,85 Even with the increasing user-friendliness of R and the availability of functions for computing additional reliability coefficients beyond alpha, moving away from alpha still represents an additional layer of difficulty for most researchers. Paradoxically, in some ways reducing the activation energy required to compute additional reliability coefficients from raw data has the potential to create new issues with reporting of reliability coefficients, such as omega and coefficient H, in contexts where they are not applicable due to not meeting underlying statistical assumptions.…”

Section: Limitations Of Reliability In a Factor Analysis Frameworkmentioning

confidence: 99%

Moving beyond Alpha: A Primer on Alternative Sources of Single-Administration Reliability Evidence for Quantitative Chemistry Education Research

2018

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This methodological paper examines current conceptions of reliability in chemistry education research (CER) and provides recommendations for moving beyond the current reliance on reporting coefficient alpha (α) as reliability evidence without regard to its appropriateness for the research context. To help foster a better understanding of reliability and the assumptions that underlie reliability coefficients, reliability is first described from a conceptual framework, drawing on examples from measurement in the physical sciences; then classical test theory is used to frame a discussion of how reliability evidence for psychometric measurements is commonly examined in CER, primarily in the form of single-administration reliability coefficients. Following this more conceptual introduction to reliability, the paper transitions to a more mathematical treatment of reliability using a factor analysis framework with emphasis on the assumptions underlying coefficient alpha and other single-administration reliability coefficients, such as omega (ω) and coefficient H, which are recommended as successors to alpha in CER due to their more broad applicability to a variety of factor models. The factor analysis-based reliability discussion is accompanied by R code that demonstrates the mathematical relations underlying single-administration reliability coefficients and provides interested readers the opportunity to compute coefficients beyond alpha for their own data.

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