Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

Azen, Razia; Traxel, Nicole M.

doi:10.3102/1076998609332754

Cited by 197 publications

(159 citation statements)

References 31 publications

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“…But, since this study was a multiple cue study where four extrinsic cues were presented to the respondents, following the suggestions by Azen and Traxel (2009) using a dominance analysis we have determined the relative importance of every cue individually and their impact on a dependent variable in the logistic regression model that we used. Based on the dominance analysis presented in Table 5, COO resulted as the most important cue when Kosovar consumers make their food purchasing decisions, followed by price, brand and domestic branded products.…”

Section: Research Resultsmentioning

confidence: 99%

Measuring the Impact of Extrinsic Cues on Consumers’ Purchasing Decision for Food Products

Fetai

Sadiku-Dushi

Ismaili

2017

Studia Universitatis Babes-Bolyai Oeconomica

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Abstract:The extrinsic product cues are becoming a very important aspect in product evaluation by consumers. Because of their importance many authors are exploring which extrinsic cues are considered more significant when evaluating the different kind of products. Therefore, the aim of this research is to investigate the impact of most researched extrinsic cues such as the country of origin (COO), brand, and price on a purchasing decision for food products among consumers in Kosova. To explore the domestic country bias, the impact of the additional cue labeled as "domestic product" is studied. Based on a quantitative survey, the main findings of this study revealed that the brand, the price, the country of origin, and domestic branded products have positive impact on a consumer's purchasing decision, since consumers rely on those extrinsic cues when making their purchasing decisions. When multiple cues are presented, the country of origin is considered as the most important cue, while the "domestic product" is least important to Kosovar consumers. The findings of this study are useful to food producers and marketers of food products, since it can provide them with useful information on what consumers consider most important when purchasing food products.JEL classification: M31, C35;

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Section: Research Resultsmentioning

confidence: 99%

Measuring the Impact of Extrinsic Cues on Consumers’ Purchasing Decision for Food Products

Fetai

Sadiku-Dushi

Ismaili

2017

Studia Universitatis Babes-Bolyai Oeconomica

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“…This includes relative weights (Azen & Traxel, 2009;Tonidandel & LeBreton, 2010), dominance analysis (Azen & Budescu, 2003), standardized logistic regression coefficients, and Pratt's index for logistic regression (Thomas, Zumbo, Zhu, & Dutta, 2008). The relative weights are derived from an orthogonal transformation of the predictor variables, whereas the dominance analysis method relies on regression model's pseudo coefficient of determination (i.e., model R 2 ) to evaluate variable importance.…”

Section: Resultsmentioning

confidence: 99%

Robust Measures of Variable Importance for Multivariate Group Designs

Sajobi

Lix

2017

J. Mod. App. Stat. Meth.

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Section: Resultsmentioning

confidence: 99%

Vol. 16, No. 2 (Full Issue)

2017

J. Mod. App. Stat. Meth.

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EDITORIAL ASSISTANCEJMASM (ISSN 1538−9472, http://digitalcommons.wayne.edu/jmasm) is an independent, open access electronic journal, published biannually in May and November by JMASM Inc. (PO Box 48023, Oak Park, MI, 48237) in collaboration with the Wayne State University Library System. JMASM seeks to publish (1) new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods, (2) the study of nonparametric, robust, permutation, exact, and approximate randomization methods, and (3) applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.Journal correspondence (other than manuscript submissions) and requests for advertising may be forwarded to ea@jmasm.com. See back matter for instructions for authors. IntroductionComputer simulation studies represent an important tool for investigating statistical procedures difficult or impossible to study using mathematical theory or real data. Descriptors of these studies vary (e.g., statistical experiment, Monte Carlo simulation, computer experiment), but the examples of Hoaglin and Andrews (1975) and Hauck and Anderson (1984) are followed here with use of the term simulation studies. Extensive descriptions of simulation studies can be found in Lewis and Orav (1989) and Santner, Williams, and Notz (2003). In the behavioral sciences simulation studies have been used to study a wide array of statistical methods (e.g., Cribbie, Fiksenbaum, & Wilcox, 2012; Depaoli, 2012; Enders, Baraldi, & Cham, 2014; Hu & Bentler, 1999; Tomarken & Serlin, 1986). The general goal of these studies is to provide evidence of the behavior of statistical methods under a variety of data conditions that improves statistical practice and informs future statistical research. The goal here is to encourage methodological researchers to treat these studies as statistical sampling EXPERIMENTAL DESIGN AND DATA ANALYSIS IN SIMULATION 4 experiments subject to established principles of experimental design and data analysis.An underappreciated facet of simulation studies in statistics is their role in enhancing the reproducibility of scientific findings. The importance of reproducibility has gained momentum in numerous scientific arenas because of growing evidence that many findings cannot be replicated (Stodden, 2015). Concerns over reproducibility and the role of statistics were captured in Statistics and science: A report of the London workshop on the future of the statistical sciences (2014) which noted: "The reproducibility problem goes far beyond statistics, of course, because it involves the entire reward structure of the scientific enterprise. Nevertheless, statistics is a very important ingredient in both the problem and the remedy." (p. 27) Simulati...

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Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

Cited by 197 publications

References 31 publications

Measuring the Impact of Extrinsic Cues on Consumers’ Purchasing Decision for Food Products

Measuring the Impact of Extrinsic Cues on Consumers’ Purchasing Decision for Food Products

Robust Measures of Variable Importance for Multivariate Group Designs

Vol. 16, No. 2 (Full Issue)

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