Causal Inferences, Closed Populations, and Measures of Association*

2017 ◽  
pp. 81-94
Author(s):  
H. M. Blalock
Keyword(s):  
Causal Inferences ◽  
Measures Of Association ◽  
Closed Populations

Causal Inferences, Closed Populations, and Measures of Association

1967 ◽  
Vol 61 (1) ◽  
pp. 130-136 ◽  
Author(s):  
Hubert M. Blalock
Keyword(s):  
Quantitative Research ◽  
Field Experiments ◽  
Experimental Situation ◽  
Clear Understanding ◽  
Causal Inferences ◽  
Measures Of Association ◽  
Specific Population ◽  
Experimental Conditions ◽  
First Case ◽  
Dependent Variables

Two of the most important traditions of quantitative research in sociology and social psychology are those of survey research and laboratory or field experiments. In the former, the explicit objective is usually that of generalizing to some specific population, whereas in the latter it is more often that of stating relationships among variables. These two objectives are not thought to be incompatible in any fundamental sense, but nevertheless we lack a clear understanding of their interrelationship.One of the most frequent objections to laboratory experiments turns on the question of generalizability, or what Campbell and Stanley refer to as “external validity.” In essence, this question seems to reduce to at least two related problems: (1) that of representativeness or typicality, and (2) the possibility of interaction effects that vary with experimental conditions. In the first case, the concern would seem to be with central tendency and dispersion of single variables, that is, whether the means and standard deviations of variables in the experimental situation are sufficiently close to those of some larger population. The second involves the question of possible disturbing influences introduced into the experimental setting that produce non-additive effects when combined with either the experimental variable or the premeasurement. These same variables may of course be operative in larger populations. But presumably they take on different numerical values, with the result that one would infer different relationships between major independent and dependent variables in the two kinds of research settings.

Causal inferences in comprehension: Does syntax play a role?

1991 ◽  
Author(s):  
Michael E. Young ◽  
Charles R. Fletcher
Keyword(s):  
Causal Inferences

From Measures of Association to Multilevel Models: Sociology Journals and the Quantitative Literacy Gap

2020 ◽  
pp. 0092055X2098042
Author(s):  
Thomas J. Linneman
Keyword(s):  
Multilevel Models ◽  
Ordinary Least Squares ◽  
Quantitative Literacy ◽  
Least Squares Regression ◽  
Journal Articles ◽  
Measures Of Association ◽  
Chi Square ◽  
Literacy Gap ◽  
Statistics Course ◽  
Sociology Journals

While most sociology majors must take a statistics course, the content of this course varies widely across departments. Starting from the assumption that sociology students should be able to engage effectively with the sociological literature, this article examines the statistical techniques used in 2,804 journal articles—from four generalist sociology journals from 1990 to 2019 and 11 additional sociology journals from 2019—in order to assess which techniques have risen or fallen in prevalence. Although stalwarts such as ordinary least squares regression, chi-square tests, and t tests maintain strong presences, the rise of logistic regression, interaction effects, and multilevel models has been dramatic. After assessing the proportion of articles students hypothetically could understand given various levels of statistical training, the article ends with suggestions for how to revamp the statistics course to help our students become more numerate citizens, both in their sociology courses and in the world at large.

scholarly journals Reactivity in social scientific experiments: what is it and how is it different (and worse) than a Placebo effect?

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
María Jiménez-Buedo
Keyword(s):  
Experimental Data ◽  
Conceptual Framework ◽  
Placebo Effect ◽  
Placebo Effects ◽  
Causal Inferences ◽  
Scientific Experiments ◽  
Social Scientific ◽  
The Many ◽  
Definition Of ◽  
In Virtue Of

AbstractReactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and distinguishes it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data.

scholarly journals Weighted mixed-effects dose–response models for tables of correlated contrasts

2021 ◽  
Vol 21 (2) ◽  
pp. 320-347
Author(s):  
Nicola Orsini
Keyword(s):  
Dose Response ◽  
Response Pattern ◽  
Mixed Effects ◽  
Data Generation ◽  
Measures Of Association ◽  
Response Models ◽  
Response Data ◽  
Hazard Ratios ◽  
Health Related ◽  
Generation Mechanisms

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.

scholarly journals Can Vaccination Trigger Autoimmune Disorders? A Meta-Analysis

Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 821
Author(s):  
Marek Petráš ◽  
Ivana Králová Lesná ◽  
Jana Dáňová ◽  
Alexander M. Čelko
Keyword(s):  
Meta Analysis ◽  
Autoimmune Disorders ◽  
Risk Of Bias ◽  
Extensive Literature ◽  
Primary Analysis ◽  
Measures Of Association ◽  
Assessment Development ◽  
Extensive Literature Search ◽  
The Stability

Vaccination as an important tool in the fight against infections has been suggested as a possible trigger of autoimmunity over the last decades. To confirm or refute this assumption, a Meta-analysis of Autoimmune Disorders Association With Immunization (MADAWI) was conducted. Included in the meta-analysis were a total of 144 studies published in 1968–2019 that were available in six databases and identified by an extensive literature search conducted on 30 November 2019. The risk of bias classification of the studies was performed using the Newcastle–Ottawa Quality Assessment Scale. The strength of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. While our primary analysis was conducted in terms of measures of association employed in studies with a low risk of bias, the robustness of the MADAWI outcome was tested using measures independent of each study risk of bias. Additionally, subgroup analyses were performed to determine the stability of the outcome. The pooled association of 0.99 (95% confidence interval, 0.97–1.02), based on a total of 364 published estimates, confirmed an equivalent occurrence of autoimmune disorders in vaccinated and unvaccinated persons. The same level of association reported by studies independently of the risk of bias was supported by a sufficient number of studies, and no serious limitation, inconsistency, indirectness, imprecision, and publication bias. A sensitivity analysis did not reveal any discrepancy in the primary result. Current common vaccination is not the cause of any of the examined autoimmune disorders in the medium and long terms.

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