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Research on how performance measurement systems affect employees’ perceptions of workplace fairness is important. As organizations often rely on their performance measurement systems to communicate information to their employees, it is useful to ascertain if and how the developments of performance measurement systems that are far more comprehensive than traditional financial systems affect employees’ perceptions of informational fairness through the information communicated to employees. Informational fairness refers to employees’ perceptions of workplace fairness that is based on the amount and the truthfulness of information that organizations provide to their employees. Based on a sample of managers from manufacturing organizations, the Partial Least Square results indicate that comprehensive performance measurement systems (comprehensive PMS) have a significant direct effect on job-relevant information. They also indicate that comprehensive PMS have an indirect effect on informational fairness via job-relevant information. In contrast, systems that are based on financial measures have no significant effects on job-relevant information and informational fairness. These results demonstrate how comprehensive PMS (through the communication of a greater amount of job-relevant information) can be used to engender employees’ perceptions of high workplace fairness.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

There is currently a great deal of attention in psychometric and statistical methods on ensuring measurement invariance when examining measures across time or populations. When measurement invariance is established, changes in scores over time or across groups can be attributed to changes in the construct rather than changes in reaction to or interpretation of the measurement instrument. When measurement in not invariant, it is possible that measured differences are due to the measurement instrument itself and not to the underlying phenomenon of interest. This chapter discusses the importance of establishing measurement invariance specifically in postsecondary settings, where it is anticipated that individuals' perspectives will change over time as a function of their higher education experiences. Using examples from several measures commonly used in higher education research, the concepts and processes underlying tests of measurement invariance are explained and analyses are interpreted using data from a US-based longitudinal study on bioscience PhD students. These measures include sense of belonging over time and across groups, mental well-being over time, and perceived mentorship quality over time. The chapter ends with a discussion about the implications of longitudinal and group measurement invariance as an important conceptual property for moving forward equitable, reproducible, and generalizable quantitative research in higher education. Invariance methods may further be relevant for addressing criticisms about quantitative analyses being biased toward majority populations that have been discussed by critical theorists engaging quantitative research strategies.

This article begins with a brief reading of the state of the practice of empirical social science research on measurement before proceeding to the discussion of an exemplary instance of this researcher's ethnographic effort to improve indicators of social capital formation. Given the central role measurement plays in social science research, it is appropriate, that a volume on methodological innovations in ethnography would contain a chapter about the relationship of ethnography to measure development. However, it is worth acknowledging that the line of argumentation advanced in this chapter is unconventional. The central tenant of this chapter – that ethnography has much to offer to the field of measurement and that ethnographers ought to take the contribution that they have the potential to make to the field of measurement seriously – at present might be thought to have little agreement either among those researchers whose primary focus is measurement or among ethnographers. This chapter contends that the features and strengths of ethnography specifically, and qualitative research more generally, makes it uniquely suited to contribute to the development of new indicators and the improvement of existing indicators. This chapter modestly hopes to encourage discussion of this contention and illustrate how this author sees his own ethnographic research into indicators of social capital formation as an attempt to address a pressing methodological dilemma within the field, more general of social scientific measure development.

A new nonparametric procedure is developed to evaluate the significance of violations of weak separability. The procedure correctly detects weak separability with high probability using simulated data that have violations of weak separability caused by adding measurement error. Results are not very sensitive when the amount of measurement error is miss-specified by the researcher. The methodology also correctly rejects weak separability for nonseparable simulated data. We fail to reject weak separability for a monetary and consumption data set that has violations of revealed preference, which suggests that measurement error may be the source of the observed violations.

Revealed preference axioms provide a simple way of testing data from consumers or firms for consistency with optimizing behavior. The resulting non-parametric tests are very attractive, since they do not require any ad hoc functional form assumptions. A weakness of such tests, however, is that they are non-stochastic. In this paper, we provide a detailed analysis of two non-parametric approaches that can be used to derive statistical tests for utility maximization, which account for random measurement errors in the observed data. These same approaches can also be used to derive tests for separability of the utility function.

The purpose is to assess current construct measurement in social entrepreneurship and provide recommendations for future construct measurement on the topic.

We use content analysis to assess the construct measurement practices in social entrepreneurship research. Prior studies were coded and analyzed to assess the way social entrepreneurship researchers have developed measures for key constructs in the social entrepreneurship literature. The content analysis allows for the examination of the number, type, and measures associated with social entrepreneurship research and for the comparison with the construct measurement practices in entrepreneurship research, in general.

We suggest that, while initial quantitative research has provided a useful start for empirical analysis of social entrepreneurship, future research can be improved by developing and applying stronger measures of key constructs, such as social value, mission consistency, and performance of social enterprises.

This chapter takes a content analytic approach to provide evidence regarding how a foundational element such as construct measurement has developed within social entrepreneurship research. We also propose directions for improving future research by validating and strengthening measurements of core constructs in social entrepreneurship.