A Note on Survey Research Methods Levels of Measurement: Foundational Basis for Quantitative Analysis of Survey Data (original) (raw)
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The fact that survey data are obtained from units selected with complex sample designs needs to be taken into account in the survey analysis: weights need to be used in analyzing survey data and variances of survey estimates need to be computed in a manner that reflects the complex sample design. This chapter outlines the development of weights and their use in computing survey estimates and provides a general discussion of variance estimation for survey data. It deals first with what are termed "descriptive" estimates, such as the totals, means, and proportions that are widely used in survey reports. It then discusses three forms of "analytic" uses of survey data that can be used to examine relationships between survey variables, namely multiple linear regression models, logistic regression models and multi-level models. These models form a set of valuable tools for analyzing the relationships between a key response variable and a number of other factors. In this chapter we give examples to illustrate the use of these modeling techniques and also provide guidance on the interpretation of the results.
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© 2003 Kluwer Academic Publishers. Printed in the Netherlands. 1 The Use of Indices in Surveys
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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.
The adequate measurement of abstract constructs is perhaps the greatest challenge to understanding the behavior of people in organizations. Problems with the reliability and validity of measures used on survey questionnaires continue to lead to difficulties in interpreting the results of field research. Price and Mueller suggest that measurement problems may be due to the lack of a well-established framework to guide researchers through the various stages of scale development. This article provides a conceptual framework and a straightforward guide for the development of scales in accordance with established psychometric principles for use in field studies.
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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 extensive simulation study. Finally, the values of the indices defined and their confidence intervals are calculated for an example with real data
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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.
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In this overview, we discuss the current state of survey methodology in a form that is useful and informative to a general social science audience. The article covers existing challenges, dilemmas, and opportunities for survey researchers and social scientists. We draw on the most current research to articulate our points; however, we also speculate on both technological and cultural changes that currently influence or may soon affect the efficacy of different methodological choices.
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In this chapter, we will first discuss the difference between concepts-by-intuition and the concepts-by-postulation. After that we will illustrate the different ways in which concepts-by-postulation can be defined by concepts-by-intuition. In doing so, we will make a distinction between concepts-by-postulation, namely between concepts with reflective and formative indicators. These illustrations make it clear that there are many different ways to define concepts-by-postulation. The effects that the wording of survey questions can have on their responses have been studied in depth by
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Survey research is a non-experimental research approach used to gather information about the incidence and distribution of, and the relationships that exist between, variables in a predetermined population. Its uses include the gathering of data related to attitudes, behaviours and the incidence of events. Survey research in one form or another has existed for over two millennia with the population census of Caesar Augustus (St. Luke's Gospel) being an early example. For most modern researchers sample surveys are more cost effective and easier to undertake than population surveys when gathering information; however, this increases the risk of both representation and measurement errors. There are a number of different forms of survey research; however they all share common steps and common limitations. The purpose of this article is to discuss these steps with a view to highlighting some of the common difficulties.
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In this chapter, we will first discuss the difference between concepts-by-intuition and the concepts-by-postulation. After that we will illustrate the different ways in which concepts-by-postulation can be defined by concepts-by-intuition. In doing so, we will make a distinction between concepts-by-postulation, namely between concepts with reflective and formative indicators. These illustrations make it clear that there are many different ways to define concepts-by-postulation. The effects that the wording of survey questions can have on their responses have been studied in depth by