计量经济分析 = Econometric analysis

行銷硏究 : Marketing research

To what extent does the number of response categories in a Likert-type scale influence the resultant data? Surprisingly little attention has been paid to the issue of whether the response category format has any influence on data characteristics such as the mean, coefficient of variation, skewness and kurtosis. This issue is important for several reasons. The first is that decisions are made based on outcomes such as the mean score. For example, marketing organizations and research providers use Likert type scales to measure constructs such as customer satisfaction. In this situation a higher score is better. Could the score have been comparatively better if a different scale format had been used? There is an absence of evidence on this issue. The second reason is that scale formats that are used in on-going market research projects such as tracking studies occasionally change. Can the old results be re-scaled or transformed to be comparable to data from a new scale format? Again, little is known about this. The third reason concerns data characteristics such as variation about the mean, skewness and kurtosis. Analysis tools such as regression are often used on data of this type to explain the variation in certain variables. If there is little variance in the data, this is harder to do. How does scale format affect these characteristics? The answers would be useful to both market researchers as well as academics. A literature review found that little work has been done on this issue. Therefore, this study set out to investigate the impact of scale format on data characteristics. It examined how using Likert-type scales with varying numbers of response categories affects the resultant data in terms of mean scores, and measures of dispersion and shape. Three groups of respondents were administered a series of eight questions (group n’s = 300, 250,185). Respondents were randomly selected members of the general public. A different scale format was administered to each group – either a five-point, seven-point or ten-point scale. The surveys were conducted by a professional market research organization via telephone interview. Data characteristics of mean score, standard deviation, skewness and kurtosis were analyzed according to scale format. The five and seven-point scales were re-scaled to a comparable mean score out of ten. The study found that the five and seven-point scales produced the same mean score as each other, once they were re-scaled. However the ten-point format tended to produce slightly lower relative means than either the 5 or 7-point scales (after the latter were re-scaled). The overall mean score of the eight questions was 0.3 scale points lower for the 10-point format compared to the 5 and 7-point format. This difference was statistically significant at p=0.04. In terms of the other data characteristics, there was very little difference among the scale formats in terms of variation about the mean, skewness or kurtosis. Therefore each of the three formats appears comparable for the type of research project in which multiple-item scales are analyzed with multivariate statistical methods. This study is also ‘good news’ for research departments or agencies who ponder whether changing scale format will destroy the comparability of historical data. Five and seven-point scales can easily be re-scaled with the resultant data being quite comparable. In the case of comparing five or seven-point data to 10-point data, a straightforward re-scaling and arithmetic adjustment easily facilitates the comparison. Finally, it appears that indicators of customer sentiment – such as satisfaction surveys – may be partially dependent on the choice of scale format. A five or seven-point scale is likely to produce slightly higher mean scores relative to the highest possible attainable score, compared to that produced from a ten-point scale.

Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5 Point, 7 Point and 10 Point Scales

The Theory of Monopolistic Competition.

Likert scale is applied as one of the most fundamental and frequently used psychometric tools in educational and social sciences research. Simultaneously, it is also subjected to a lot of debates and controversies in regards with the analysis and inclusion of points on the scale. With this context, through reviewing the available literature and then clubbing the received information with coherent scientific thinking, this paper attempts to gradually build a construct around Likert scale. This analytical review begins with the necessity of psychometric tools like Likert scale andits variants and focuses on some convoluted issues like validity, reliability and analysis of the scale.

Likert Scale: Explored and Explained

This study examined how using Likert-type scales with either 5-point, 7-point or 10-point format affects the resultant data in terms of mean scores, and measures of dispersion and shape. Three groups of respondents were administered a series of eight questions (group n’s = 300, 250, 185). Respondents were randomly selected members of the general public. A different scale format was administered to each group. The 5and 7-point scales were rescaled to a comparable mean score out of ten. The study found that the 5and 7-point scales produced the same mean score as each other, once they were rescaled. However, the 10-point format tended to produce slightly lower relative means than either the 5or 7point scales (after the latter were rescaled). The overall mean score of the eight questions was 0.3 scale points lower for the 10-point format compared to the rescaled 5and 7-point formats. This difference was statistically significant at p = 0.04. In terms of the other data characteristics, there was very little difference among the scale formats in terms of variation about the mean, skewness or kurtosis. This study is ‘good news’ for research departments or agencies who ponder whether changing scale format will destroy the comparability of historical data. 5and 7-point scales can easily be rescaled with the resultant data being quite comparable. In the case of comparing 5or 7-point data to 10-point data, a straightforward rescaling and arithmetic adjustment easily facilitates the comparison. The study suggests that indicators of customer sentiment – such as satisfaction surveys – may be partially dependent on the choice of scale format. A 5or 7-point scale is likely to produce slightly higher mean scores relative to the highest possible attainable score, compared to that produced from a 10-point scale. International Journal of Market Research Vol. 50 Issue 1

https://www.researchgate.net/file.PostFileLoader.html?id=55cc52f65e9d9711338b458d&assetKey=AS%3A273831153864713%401442297842941

Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales:

Subjective performance measures have been widely used in research on market orientation and its presumed link to company performance. However, only a small number of studies have examined the link between subjective performance measures and objective ones. This study replicates earlier research and extends previous findings using a broader sample of firms than in most previous studies, and uses slightly different measurement scales. It finds that there is a strong correlation between objective and subjective performance measures. However, this correlation is far from perfect and the article concludes that researchers should attempt to validate their results by using both types of measures.

/pdf/the-relationship-between-subjective-and-objective-company-1rgfiqsopl.pdf

The Relationship between Subjective and Objective Company Performance Measures in Market Orientation Research: Further Empirical Evidence

This study examines the association between market orientation and company profitability. It incorporates two methodological approaches that have generally not been used in previous research. First, it uses lagged company and environmental control variables in the data analysis, to better discern their effects on profitability and hence, clarify any relationship between market orientation and performance. Second, it analyses the individual components of market orientation and their relationships with business profitability separately. The study finds that of the components of a market orientation, a competitor orientation emerges as the variable with the strongest association with performance. This association is robust in two models, one with solely cross sectional data, and the other with lagged control variables. In simple terms, the strongest distinguishing feature of high-profit firms in this study is that they are very attuned to the activities and characteristics of competitors. For managers, this reinforces the view that while a customer orientation is vital, competitor intelligence activities may also be a key factor in ensuring high performance. The implication for researchers is that each component of market orientation should not necessarily be assumed to have equally strong associations with profitability.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2323983_code1797307.pdf?abstractid=2323983&mirid=5

Market Orientation and Company Profitability: Further Evidence Incorporating Longitudinal Data

This study examines the association between market orientation and company profitability. It incorporates two methodological approaches that have generally not been used in previous research. First, it uses lagged company and environmental control variables in the data analysis, to better discern their effects on profitability and, hence, clarify any relationship between market orientation and perfor Mance. Second, it analyses the individual components of market orientation and their relationships with business profitability separately. The study finds that, of the components of a market orientation, a competitor orientation emerges as the variable with the strongest association with perfor Mance. This association is robust in two models, one with solely cross‐sectional data, and the other with lagged control variables. In simple terms, the strongest distinguishing feature of high‐profit firms in this study is that they are very attuned to the activities and characteristics of competitors. For managers, t...

John Dawes

Papers

Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5 Point, 7 Point and 10 Point Scales

Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales:

The Relationship between Subjective and Objective Company Performance Measures in Market Orientation Research: Further Empirical Evidence

Market Orientation and Company Profitability: Further Evidence Incorporating Longitudinal Data

Market Orientation and Company Profitability: Further Evidence Incorporating Longitudinal Data