On the use of ridit analysis

Beder, Jay H.; Heim, Robert C.

doi:10.1007/bf02294610

Cited by 29 publications

(8 citation statements)

References 12 publications

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“…It is essentially a weight allotted to a response group that reflects the probability of its appearance in the standard distribution. This technique is considered to facilitate the data analysis of variables that exceed dichotomous classifications and are well-organized (Panda, 2012), which is predominantly helpful in the statistical analysis of items with a three-point scale rating or more based on universal standards and several item indices (Beder and Heim, 1990). The RIDIT value is a number allotted to a particular response category of a variable as an assigned weight, which imitates the chance of that category appearing in the standard distribution.…”

Section: Methodsmentioning

confidence: 99%

Antecedents of mobile advertising value: a precedence analysis using the hybrid RIDIT-GRA approach

Sharma

Pathak

Siddiqui

2021

JIBR

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Purpose Massive transformations in mobile communication technologies have forced marketers to recognize and emphasize the factors that influence consumers’ perception of advertising value. This paper aims to explore and rank the various antecedents of advertising value as perceived by consumers to offer meaningful conclusions to marketers on mobile platforms. Design/methodology/approach Responses were collected from 483 consumers using a shopping mall intercept survey and analyzed using SPSS to confirm reliability, validity and data reduction. The Relative to an Identified Distribution (RIDIT) analysis and Grey Relational Analysis (GRA) methods were then applied to prioritize the scale items of the antecedents of mobile advertising value. Findings Five antecedents of advertising value were found: credibility, entertainment, informativeness, irritation and message relevance. A priority ranking was allotted to the antecedents’ scale items using the RIDIT analysis and was verified via GRA results with a correlation of 98% between the rankings of the two independent methodologies. Practical implications The findings provide a roadmap to determine which antecedents of mobile advertising value have a higher or lower impact on consumers’ overall perceptions of the advertisements they are exposed to on mobile platforms. Originality/value This study aims to use first-hand data to prioritize the underlying antecedents of mobile advertising value, which has rarely been done to the best of the authors’ knowledge. It also used two different approaches in a single study to rank the dimensions, thus producing more valid results.

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Section: Methodsmentioning

confidence: 99%

Antecedents of mobile advertising value: a precedence analysis using the hybrid RIDIT-GRA approach

Sharma

Pathak

Siddiqui

2021

JIBR

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“…RIDIT analysis is an effective method to study the data, following the Likert scale (Bross, 1958). There are no assumptions in RIDIT analysis for the population distribution of the study, so it is a “distribution-free” analysis (Beder & Heim, 1990; Bhattacharya & Kumar, 2017; Bross, 1958). Kruskal–Wallis (W) is a statistical test in RIDIT analysis, which confirms the results for “goodness of fit” (Bhattacharya & Kumar, 2017; Sadhukhan et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bross first introduced RIDIT analysis in 1958, and later different researchers improved it (Beder & Heim, 1990; Bhattacharya & Kumar, 2017). RIDIT analysis is an effective method to study the data, following the Likert scale (Bross, 1958).…”

Section: Literature Reviewmentioning

confidence: 99%

Which Service Attributes Sway Internet Service Providers? Analysis Through Triangulation Approach

Mahmood

Manzoor

2021

Sage Open

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Internet service providing industry is considered a mainstay for all types of organizations all over the world. In order to be competitive, internet service providers (ISPs) need to realize that all service quality attributes do not carry the same value in customers’ eyes. Therefore, the present study proposes a rationally appealing methodological framework for prioritizing attributes of the SERVQUAL model without restricting them to their dimensions. The responses on 22 attributes of service quality collected from 401 internet users were processed through a triangulation approach comprising three ranking methods: Relative to an Identified Distribution Integral Transformation (RIDIT), Grey Relational Analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A correlational study of these results was also computed using Spearman’s rank-order correlation coefficient, which proved positive. The consistent outcomes reveal a definite set of users’ priorities for the quality attributes of internet services, and it denies the general thought that the rate of internet service providers is the primary concern of customers. The findings uniquely contribute by highlighting important facets of service quality from customers’ viewpoint. The study would benefit ISPs to empathize customers’ needs corresponding to essential attributes. In this way, they would be able to rationalize the budget for top-ranked features, and redesign operational strategies accordingly.

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“…This is the mean squared ridit for B, yzs/A. An approximate 100( 1 -CX)% confidence interval of the mean ridit comparison is then given by: FA/B (or rm> f 7212 r ~ &rf%+&i N'4 NB (Beder and Heim, 1990). For the data in Table 1, an approximate 95% confidence interval (i.e., using 20,025 = 1.96) for the probability that a typical low-dose patient will have more pain-related sleep interference than a typical high dose patient is 0.695 f 1.96 d 0.553-0.6952+0.134-0.3052 22 18 = [0.550,0.840] The ridit comparison indicates a clear advantage for the high-dose group in controlling painful sleep disturbances.…”

Section: Calculation Of Ridit Scores and Associated Confidence Intervalsmentioning

confidence: 99%

Ridit scores for analysis and interpretation of ordinal pain data

Donaldson

1998

European Journal of Pain

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Pain studies frequently generate outcome data in the form of ordered categorical responses. Measurement properties of such ordinal variables do not permit straightforward numeric treatment, yet purely categorical (nominal) procedures fail to incorporate the ordered nature of the pain response. This paper describes analysis of ordered categorical data by means of ridit analysis, a simple, yet statistically valid and efficient, technique that yields a highly intuitive interpretation of a treatment effect. The mean ridit comparison of two treatments estimates the probability that a randomly-selected patient from one will have higher pain than a randomly-selected patient from the other. Ridit analysis is illustrated with two examples from a clinical trial involving patients with painful bony metastases subsequent to hormone-refractory prostate cancer.

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On the use of ridit analysis

Abstract: ordinal scale, categorical scale, asymptotic normality, U-statistic, ranks,

Cited by 29 publications

References 12 publications

Antecedents of mobile advertising value: a precedence analysis using the hybrid RIDIT-GRA approach

Antecedents of mobile advertising value: a precedence analysis using the hybrid RIDIT-GRA approach

Which Service Attributes Sway Internet Service Providers? Analysis Through Triangulation Approach

Ridit scores for analysis and interpretation of ordinal pain data

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