scholarly journals On unit free assessment of the extent of multilateral distributional variation

2021 ◽  
Author(s):  
Gordon Anderson ◽  
Oliver Linton ◽  
Maria Grazia Pittau ◽  
Yoon-Jae Whang ◽  
Roberto Zelli
Keyword(s):  
Social Sciences ◽  
Categorical Data ◽  
Respective Group ◽  
Scale Parameters ◽  
Income Distributions ◽  
Cardinal Measure ◽  
The Social ◽  
Comprehensive Examination ◽  
Data Environment ◽  
Multilateral Comparison

Summary Multilateral comparison of outcomes drawn from multiple groups pervade the social sciences and measurement of their variability, usually involving functions of respective group location and scale parameters, is of intrinsic interest. However, such approaches frequently mask more fundamental differences that more comprehensive examination of relative group distributional structures reveal. Indeed, in categorical data contexts, location- and scale-based techniques are no longer feasible without artificial and questionable cardinalisation of categories. Here, Gini’s transvariation measure is extended and employed in providing quantitative and visual multilateral comparison tools in discrete, continuous, categorical, univariate, or multivariate settings which are particularly useful in paradigms where cardinal measure is absent. Two applications, one analysing Eurozone cohesion in terms of the convergence or divergence of constituent nations income distributions, the other, drawn from a study of ageing, health, and income inequality in China, exemplify their use in a continuous and categorical data environment.

Evaluating Measurement Invariance in Categorical Data Latent Variable Models with the EPC-Interest

2015 ◽  
Vol 23 (4) ◽  
pp. 550-563 ◽  
Author(s):  
Daniel L. Oberski ◽  
Jeroen K. Vermunt ◽  
Guy B. D. Moors
Keyword(s):  
Social Sciences ◽  
Sensitivity Analysis ◽  
Measurement Invariance ◽  
Categorical Data ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Continuous Data ◽  
External Variable ◽  
The Social ◽  
The World

Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of “measurement invariance” may, for example, confound true differences across countries in postmaterialism with measurement differences. To deal with this problem, researchers commonly aim at “partial measurement invariance” that is, to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the “EPC-interest” was recently introduced for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-interest to latent variable models for categorical data and demonstrates its use in example analyses of U.S. Senate votes as well as respondent rankings of postmaterialism values in the World Values Study.

scholarly journals The Assignment of Scores Procedure for Ordinal Categorical Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Han-Ching Chen ◽  
Nae-Sheng Wang
Keyword(s):  
Social Sciences ◽  
Categorical Data ◽  
Ordinal Data ◽  
Interval Data ◽  
Score System ◽  
Ordered Categorical Data ◽  
The Social ◽  
Latent Distribution ◽  
Ordered Categorical

Ordinal data are the most frequently encountered type of data in the social sciences. Many statistical methods can be used to process such data. One common method is to assign scores to the data, convert them into interval data, and further perform statistical analysis. There are several authors who have recently developed assigning score methods to assign scores to ordered categorical data. This paper proposes an approach that defines an assigning score system for an ordinal categorical variable based on underlying continuous latent distribution with interpretation by using three case study examples. The results show that the proposed score system is well for skewed ordinal categorical data.

What Can We Learn From Factorial Surveys About Human Behavior?

Methodology ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Knut Petzold ◽  
Tobias Wolbring
Keyword(s):  
Social Sciences ◽  
Behavioral Intentions ◽  
Actual Behavior ◽  
Full Time ◽  
Factorial Survey ◽  
Survey Experiment ◽  
Intended Behavior ◽  
The Social ◽  
Survey Experiments ◽  
High Level

Abstract. Factorial survey experiments are increasingly used in the social sciences to investigate behavioral intentions. The measurement of self-reported behavioral intentions with factorial survey experiments frequently assumes that the determinants of intended behavior affect actual behavior in a similar way. We critically investigate this fundamental assumption using the misdirected email technique. Student participants of a survey were randomly assigned to a field experiment or a survey experiment. The email informs the recipient about the reception of a scholarship with varying stakes (full-time vs. book) and recipient’s names (German vs. Arabic). In the survey experiment, respondents saw an image of the same email. This validation design ensured a high level of correspondence between units, settings, and treatments across both studies. Results reveal that while the frequencies of self-reported intentions and actual behavior deviate, treatments show similar relative effects. Hence, although further research on this topic is needed, this study suggests that determinants of behavior might be inferred from behavioral intentions measured with survey experiments.

Sign in / Sign up

Export Citation Format

Share Document