scholarly journals Levels of measurement and statistical analyses

2021 ◽  
Vol 5 ◽  
Author(s):  
Matt N Williams
Keyword(s):  
Statistical Analyses ◽  
Data Analyses ◽  
Statistical Assumptions

Most researchers and students in psychology learn of S. S. Stevens’ scales or “levels” of measurement (nominal, ordinal, interval, and ratio), and of his rules setting out which statistical analyses are admissible with each measurement level. Many are nevertheless left confused about the basis of these rules, and whether they should be rigidly followed. In this article, I attempt to provide an accessible explanation of the measurement-theoretic concerns that led Stevens to argue that certain types of analyses are inappropriate with data of particular levels of measurement. I explain how these measurement-theoretic concerns are distinct from the statistical assumptions underlying data analyses, which rarely include assumptions about levels of measurement. The level of measurement of observations can nevertheless have important implications for statistical assumptions. I conclude that researchers may find it more useful to critically investigate the plausibility of the statistical assumptions underlying analyses than to limit themselves to the set of analyses that Stevens believed to be admissible with data of a given level of measurement.

scholarly journals Levels of measurement and statistical analyses

2019 ◽  
Author(s):  
Matt Williams
Keyword(s):  
Statistical Analyses ◽  
Data Analyses ◽  
Statistical Assumptions

Most researchers and students in psychology learn of S. S. Stevens’ scales or “levels” of measurement (nominal, ordinal, interval, and ratio), and of his rules setting out which statistical analyses are inadmissible with each measurement level. Many are nevertheless left confused about the basis of these rules, and whether they should be rigidly followed. In this article, I attempt to provide an accessible explanation of the measurement-theoretic concerns that led Stevens to argue that certain types of analyses are inappropriate with particular levels of measurement. I explain how these measurement-theoretic concerns are distinct from the statistical assumptions underlying data analyses, which rarely include assumptions about levels of measurement. The level of measurement of observations can nevertheless have important implications for statistical assumptions. I conclude that researchers may find it more useful to critically investigate the plausibility of the statistical assumptions underlying analyses rather than limiting themselves to the set of analyses that Stevens believed to be admissible with data of a given level of measurement.

Assessing assumptions for statistical analyses in randomised clinical trials

2019 ◽  
Vol 24 (5) ◽  
pp. 185-189 ◽  
Author(s):  
Emil Eik Nielsen ◽  
Anders Kehlet Nørskov ◽  
Theis Lange ◽  
Lehana Thabane ◽  
Jørn Wetterslev ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Statistical Methods ◽  
Statistical Power ◽  
Current Practice ◽  
Statistical Analyses ◽  
Randomised Clinical Trials ◽  
Validity Of Results ◽  
Medical Journals ◽  
Statistical Assumptions

In order to ensure the validity of results of randomised clinical trials and under some circumstances to optimise statistical power, most statistical methods require validation of underlying statistical assumptions. The present paper describes how trialists in major medical journals report tests of underlying statistical assumptions when analysing results of randomised clinical trials. We also consider possible solutions how to improve current practice by adequate reporting of tests of underlying statistical assumptions. We conclude that there is a need to reach consensus on which underlying assumptions should be assessed, how these underlying assumptions should be assessed and what should be done if the underlying assumptions are violated.

DEPENDENCE AND INTERACTION IN FREQUENCY DATA ANALYSIS IN SLA RESEARCH

1999 ◽  
Vol 21 (3) ◽  
pp. 453-475 ◽  
Author(s):  
Hidetoshi Saito
Keyword(s):  
Data Analysis ◽  
Statistical Analyses ◽  
Published Data ◽  
Data Sets ◽  
Frequency Data ◽  
Independent Observation ◽  
Chi Square ◽  
Interaction Terms ◽  
Data Analyses ◽  
Chi Square Analysis

In SLA research, dependence in frequency data is a prevalent problem (Hatch & Lazaraton, 1991). Researchers usually regard the data as being independent and subject them to statistical analyses. Another problem in frequency data analysis in SLA research is the exclusion of interaction terms. This is the case because of the use of chi-square analysis or the unpopularity of multifactorial frequency data analyses. This study investigates the violation of the “independent observation” assumption as well as the effect of including interaction terms in frequency data analysis. Reanalyzing a couple of published data sets, the paper argues in favor of using multifactorial frequency data analyses over multiple chi-squares in order to take into account dependence and interaction of the frequency data. A set of recommendations for SLA researchers will be provided when statistically analyzing frequency data in SLA research.

Not just nuisance: spatialising social statistics

2019 ◽  
pp. 149-168 ◽  
Author(s):  
Richard Harris
Keyword(s):  
Social Sciences ◽  
Social Policy ◽  
Policy Analysis ◽  
Statistical Analyses ◽  
Units Of Analysis ◽  
The Social ◽  
Statistical Assumptions ◽  
Social Statistics ◽  
Dominant Treatment

Outside of the specialist community of quantitative spatial researchers’ statistical analyses in the social sciences see geography merely as simple units of analysis or else as nuisance risks to the satisfaction of underlying statistical assumptions, if indeed it sees geography at all. In step-by-step discussion and visualisations this chapter upends that dominant treatment by illustrating the range of rich and frequently untapped spatial insights that a clearer understanding and grasp of specialist but (relatively) straightforward spatial methodologies can bring substantively to social policy analysis and practice.

Compensatory Coping Through the Extension of Working Hours

2019 ◽  
Vol 63 (3) ◽  
pp. 115-128 ◽  
Author(s):  
Maie Stein ◽  
Sylvie Vincent-Höper ◽  
Nicole Deci ◽  
Sabine Gregersen ◽  
Albert Nienhaus
Keyword(s):  
Job Demands ◽  
Working Hours ◽  
Well Being ◽  
Leader Member Exchange ◽  
Workplace Health ◽  
High Quality ◽  
Data Analyses ◽  
Member Exchange ◽  
The Relationship ◽  
Advance Knowledge

Abstract. To advance knowledge of the mechanisms underlying the relationship between leadership and employees’ well-being, this study examines leaders’ effects on their employees’ compensatory coping efforts. Using an extension of the job demands–resources model, we propose that high-quality leader–member exchange (LMX) allows employees to cope with high job demands without increasing their effort expenditure through the extension of working hours. Data analyses ( N = 356) revealed that LMX buffers the effect of quantitative demands on the extension of working hours such that the indirect effect of quantitative demands on emotional exhaustion is only significant at low and average levels of LMX. This study indicates that integrating leadership with employees’ coping efforts into a unifying model contributes to understanding how leadership is related to employees’ well-being. The notion that leaders can affect their employees’ use of compensatory coping efforts that detract from well-being offers promising approaches to the promotion of workplace health.

Structuring Situational Information

2015 ◽  
Vol 20 (3) ◽  
pp. 176-189 ◽  
Author(s):  
John F. Rauthmann
Keyword(s):  
Statistical Analyses ◽  
Current Work ◽  
Common Language ◽  
Road Map ◽  
Taxonomic Approach ◽  
Situational Information

Abstract. There is as yet no consensually agreed-upon situational taxonomy. The current work addresses this issue and reviews extant taxonomic approaches by highlighting a “road map” of six research stations that lead to the observed diversity in taxonomies: (1) theoretical and conceptual guidelines, (2) the “type” of situational information studied, (3) the general taxonomic approach taken, (4) the generation of situation pools, (5) the assessment and rating of situational information, and (6) the statistical analyses of situation data. Current situational taxonomies are difficult to integrate because they follow different paths along these six stations. Some suggestions are given on how to spur integrated taxonomies toward a unified psychology of situations that speaks a common language.

Sign in / Sign up

Export Citation Format

Share Document