The Use of Indices in Surveys (original) (raw)
Related papers
© 2003 Kluwer Academic Publishers. Printed in the Netherlands. 1 The Use of Indices in Surveys
2014
Abstract. The paper deals with some new indices for ordinal data that arise from sample surveys. Their aim is to measure the degree of concentration to the “positive ” or “negative ” answers in a given question. The properties of these indices are examined. Moreover, methods for constructing confidence limits for the indices are discussed and their performance is evaluated through an ex-tensive simulation study. Finally, the values of the indices defined and their confidence intervals are calculated for an example with real data.
On Certain Indices for Ordinal Data with Unequally Weighted Classes’, Quality and Quantity
2005
Abstract. In this paper, some new indices for ordinal data are introduced. These indices have been developed so as to measure the degree of concentration on the “small ” or the “large” values of a variable whose level of measurement is ordinal. Their advantage in relation to other approaches is that they ascribe unequal weights to each class of values. Although, they constitute a useful tool in various fields of applications, the focus here is on their use in sample surveys and specifically in situations where one is interested in taking into account the “distance ” of the responses from the “neutral ” category in a given question. The prop-erties of these indices are examined and methods for constructing confidence intervals for their actual values are discussed. The performance of these methods is evaluated through an extensive simulation study. 1.
Quantitative Methods Inquires 55 A NEW PARADIGM FOR MODELLING ORDINAL RESPONSES IN SAMPLE SURVEYS 1
A growing interest in the current surveys is focused on human and relational issues collected as ordinal variables. Standard approaches interpret them as manifest expressions of a continuous latent variable and the current methodology is based on the relationship between the cumulative function of the ratings and the subjects' variables. A different class of models, called CUB, is based on the statement that ordinal responses are a weighted combination of a personal feeling and an inherent uncertainty surrounding the decisional process. In this paper, the novel paradigm is presented and applied to real data sets to show the advantages of this method for analyzing big data in the context of official statistics.
Dhaulagiri Journal of Sociology and Anthropology
This research note briefly describes the levels of measurement of variables and their applications in the quantitative analysis of survey data. It first presents the concept of the measurement of variables. Second, the four levels of measurements, namely, nominal, ordinal, interval, and ratio, with examples are offered. Then, the application of these measurement levels to the statistical analysis of data at the univariate (descriptive statistics), bivariate, and multivariate (e.g., binary logistic and multiple linear regression) levels are discussed. This note is expected to be useful to the beginning (naïve) scholars for real-world application of statistical tools to analyze survey data.
The Application of the Nominal Scale of Measurement in Research Data Analysis
Prestige Journal of Education, 2023
Appropriate measurement scales are fundamental in data analysis, allowing researchers to categorise, select appropriate statistical methods, and analyse and interpret their data accurately. The nominal scale is one such measurement scale in behavioural sciences, which is crucial in organising data into distinct categories. This paper provides an overview of the nominal measurement scale in research data analysis. It explains the characteristics and role of the nominal scale in organising data into distinct categories. The paper discusses methods of collecting nominal scale data, including surveys and observations. It explores the use of the nominal scale in descriptive (such as frequency counts, measures of dispersion and central tendencies), and inferential statistics (such as point biserial correlation, independent t-test, analysis of variance, logistic regression, discriminant analysis, differential item functioning, chi-square test of independence, Kruskal-Walli's test, and Mann-Whitney U Test). Each technique is explained with assumptions and application areas. In conclusion, the paper emphasises the significance of the nominal scale in data analysis and its contribution to various statistical techniques. It serves as a comprehensive guide for researchers and practitioners looking to understand and utilise the nominal measurement scale in their data analysis.
SURVEY DESIGN USING INDIVIDUAL NUMERICAL SCALES IN THE FRAMEWORK OF ANALYTIC HIERARCHY PROCESSES
2011
This paper discusses the adequacy of a generalization of Saaty's 1-9 scale proposed by Liang at all (2008) in the attempt to identify individual scales. Several surveys in completely different areas were conducted on different topics. Comparisons among the consistency index-as a measure of a "good answer" and the previously mentioned scale reveal a non monotonic correspondence among those two criterions. Also, the individual scale considered -which is in itself a generalization of other similar scales for measuring individual responses -is not uniquely determined for a single respondent and is very often contradictory. Yet, the potential benefits in determining individual scales of measurement are enormous and maybe the most important one is getting rid of the myth of the good appliance of the "law of large numbers" in social sciences.
Measuring agreement in ordered rating scales
Quality and Quantity, 2001
Ordered rating scales are one of the most frequently used question formats in large-scale surveys. Analysts of the responses to such questions often find themselves in need of describing the degree of agreement (concentration, consensus) of the answers to such questions. For that purpose they commonly use standard deviations of the response distributions, or measures based on these (such as the coefficient of consensus defined by , or the coefficient of variability, etc. This paper demonstrates that such measures are inappropriate for this purpose because they misrepresent what they are supposed to measure: the 'peakedness' of a distribution. As an alternative a measure of agreement A is proposed. This measure is a weighted average of the degree of agreement that exists in the simple component parts -layers -into which any frequency distribution can be disaggregated, and for which agreement can be expressed in a straightforward and unequivocal way.