Causal Models

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Quantitative Research ◽  
Causal Model ◽  
A Priori ◽  
Case Analysis ◽  
Causal Models ◽  
Theoretic Model ◽  
Quantitative And Qualitative Research ◽  
Specific Outcome ◽  
Multiple Causation ◽  
Causal Complexity

This chapter compares two causal models used in qualitative and quantitative research: an additive-linear model and a set-theoretic model. The additive-linear causal model is common in the statistical culture, whereas the set-theoretic model is often used (implicitly) in the qualitative culture. After providing an overview of the two causal models, the chapter considers the main differences between them. It then gives an example to illustrate how a set-theoretic causal model is implicitly used in the within-case analysis of a specific outcome. It also explains how the form of causal complexity varies across the quantitative and qualitative paradigms. Finally, it examines another difference between the causal models used in quantitative and qualitative research, one that revolves around the concept of “equifinality” or “multiple causation.” The chapter suggests that while the two causal models are quite different, neither is a priori correct.

Scope

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Social Science ◽  
Quantitative Research ◽  
Causal Model ◽  
Social Science Research ◽  
Science Research ◽  
Model Fit ◽  
Empirical Testing ◽  
Qualitative And Quantitative ◽  
The Relationship ◽  
Causal Complexity

This chapter focuses on scope conditions in qualitative and quantitative research. It begins with a simple example, Hooke's law from physics, to illustrate the concept of “scope.” It then considers some of the most popular “within-model” responses to causal heterogeneity problems, showing that the option of changing the causal model to address causal heterogeneity issues is more attractive to quantitative researchers than to qualitative researchers. It also examines how the existence of causal complexity and concerns about fit with data can lead scholars to use scope conditions. Finally, it discusses the relationship between empirical testing and the proposed scope of theories and suggests that issues of scope raise Fundamental Tradeoffs in social science research, including tradeoffs concerning the tension between generality and parsimony, and between generality and issues of model fit.

Conclusion

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Quantitative Research ◽  
Causal Models ◽  
Methodological Pluralism ◽  
Mutual Respect ◽  
Design Data ◽  
Qualitative And Quantitative ◽  
Quantitative And Qualitative Research ◽  
The Social

This book concludes by reemphasizing important differences in the nature of qualitative and quantitative research—differences that extend across research design, data analysis, and causal inference. While their differences are considerable, the book argues that both research cultures can complement one another in terms of explaining the social and political world. However, a fruitful collaboration between quantitative and qualitative research—one built around mutual respect and appreciation—is possible only if scholars of both traditions understand and acknowledge their differences. These differences, summarized in tables, come in the areas of individual cases, causality and causal models, populations and data, concepts and measurement, and asymmetry. The book also contends that mixing the qualitative and quantitative cultures will contribute to methodological pluralism in the social sciences.

Within-Case versus Cross-Case Causal Analysis

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Qualitative Research ◽  
Causal Inference ◽  
Quantitative Research ◽  
Statistical Tests ◽  
Case Analysis ◽  
Process Tracing ◽  
Quantitative And Qualitative Research ◽  
Case Comparison ◽  
Primary Focus ◽  
Cross Case Analysis

This chapter examines how the qualitative and quantitative research traditions treat within-case analysis versus cross-case analysis for causal inference. In qualitative research, the primary focus is on specific events and processes taking place within each individual case. Leading qualitative methodologies of hypothesis testing, such as process tracing and counterfactual analysis, are fundamentally methods of within-case analysis. By contrast, quantitative research traditionally involves exclusively cross-case comparison. The chapter begins with a comparison of the typical roles (or nonroles) of within-case and cross-case analysis in case studies versus experiments. It then considers how causal inference in quantitative and qualitative research is linked to the use of “data-set observations” and “causal-process observations,” respectively. It also explains the differences between process-tracing tests and statistical tests and concludes by suggesting that cross-case analysis and within-case analysis can and often should be combined.

Causation in Legal and Moral Reasoning

2017 ◽  
Author(s):  
David A. Lagnado ◽  
Tobias Gerstenberg
Keyword(s):  
Moral Reasoning ◽  
Causal Reasoning ◽  
Causal Model ◽  
Physical World ◽  
Causal Models ◽  
Responsibility Attribution ◽  
Model Framework ◽  
The Social ◽  
Legal Concepts ◽  
Legal Judgments

Causation looms large in legal and moral reasoning. People construct causal models of the social and physical world to understand what has happened, how and why, and to allocate responsibility and blame. This chapter explores people’s common-sense notion of causation, and shows how it underpins moral and legal judgments. As a guiding framework it uses the causal model framework (Pearl, 2000) rooted in structural models and counterfactuals, and shows how it can resolve many of the problems that beset standard but-for analyses. It argues that legal concepts of causation are closely related to everyday causal reasoning, and both are tailored to the practical concerns of responsibility attribution. Causal models are also critical when people evaluate evidence, both in terms of the stories they tell to make sense of evidence, and the methods they use to assess its credibility and reliability.

Causal Models and Conditional Reasoning

2017 ◽  
Author(s):  
Mike Oaksford ◽  
Nick Chater
Keyword(s):  
Model Theory ◽  
Causal Model ◽  
Recent Model ◽  
Causal Models ◽  
Conditional Reasoning ◽  
Cognitive System ◽  
Markov Condition ◽  
Bayes Nets ◽  
Causal Bayes Nets ◽  
Conditional Statements

There are deep intuitions that the meaning of conditional statements relate to probabilistic law-like dependencies. In this chapter it is argued that these intuitions can be captured by representing conditionals in causal Bayes nets (CBNs) and that this conjecture is theoretically productive. This proposal is borne out in a variety of results. First, causal considerations can provide a unified account of abstract and causal conditional reasoning. Second, a recent model (Fernbach & Erb, 2013) can be extended to the explicit causal conditional reasoning paradigm (Byrne, 1989), making some novel predictions on the way. Third, when embedded in the broader cognitive system involved in reasoning, causal model theory can provide a novel explanation for apparent violations of the Markov condition in causal conditional reasoning (Ali et al, 2011). Alternative explanations are also considered (see, Rehder, 2014a) with respect to this evidence. While further work is required, the chapter concludes that the conjecture that conditional reasoning is underpinned by representations and processes similar to CBNs is indeed a productive line of research.

scholarly journals Overview of Nutritional Status of Pregnant Women with Preeclampsia: Case Study in Aru Islands Regency, Dobo City, Southeast Maluku

2021 ◽  
Vol 5 (3) ◽  
pp. 209-217
Author(s):  
Rifatolistia Tampubolon ◽  
Hapsari Probowati ◽  
Judith Devi Manutilaa
Keyword(s):  
Blood Pressure ◽  
Qualitative Research ◽  
Body Weight ◽  
Nutritional Status ◽  
Pregnant Women ◽  
Quantitative Research ◽  
Quantitative And Qualitative Research ◽  
Risk Increase ◽  
Energy Excess

Background: Preeclampsia is a syndrome in terms of hypertension after 20-week pregnancy referring to a pregnant woman that previously had normal blood pressure, followed by having hypertension, proteinuria, edema and generally occurs in the third trimester of pregnancy. Preeclampsia is one of five main causes of maternal mortality up to 12% in the world as well. Objective: This study was conducted to describe nutritional status of pregnant women with preeclampsia in Aru Islands Regency, Dobo City, Southeast Maluku. Methodology: This study used mix methods, namely, quantitative and qualitative research with Case Study design. Qualitative research was to determine nutritional status of pregnant women with preeclampsia and quantitative research was to record nutrition intake of pregnant women and measure nutritional status of pregnant women with preeclampsia. Results & Discussion: Characteristics of participants with preeclampsia were more than 27 years old, worked as housewife that could be one of stress triggers and had some risk to increase preeclampsia cases because of stress that caused blood pressure increase. Preeclampsia was detected in pregnancy term of participants about 20-30 weeks according to Maternal and Child Health data. Preeclampsia risk was doubly by every increase in body weight (5-7 kg). Participants had body weight increase ranging from 8-25 kg which caused preeclampsia risk increase. Parameters of recommended dietary allowances of pregnant women including energy excess, protein deficit, fat excess, calcium and zinc deficiency were secondary factor of preeclampsia risk increase in Aru Islands Regency, Dobo City, Southeast Maluku.

scholarly journals The Methodological Underdog: A Review of Quantitative Research in the Key Adult Education Journals

2017 ◽  
Vol 68 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Ellen Boeren
Keyword(s):  
Data Mining ◽  
Qualitative Research ◽  
Education Research ◽  
Adult Education ◽  
Continuing Education ◽  
Lifelong Learning ◽  
Quantitative Research ◽  
Critical Discussion ◽  
Journal Articles ◽  
Quantitative And Qualitative Research

An examination of articles published in leading adult education journals demonstrates that qualitative research dominates. To better understand this situation, a review of journal articles reporting on quantitative research has been undertaken by the author of this article. Differences in methodological strengths and weaknesses between quantitative and qualitative research are discussed, followed by a data mining exercise on 1,089 journal articles published in Adult Education Quarterly, Studies in Continuing Education, and International Journal of Lifelong Learning. A categorization of quantitative adult education research is presented, as well as a critical discussion on why quantitative adult education does not seem to be widespread in the key adult education journals.

Case Selection and Hypothesis Testing

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Qualitative Research ◽  
Causal Inference ◽  
Hypothesis Testing ◽  
Quantitative Research ◽  
Case Study Research ◽  
Causal Models ◽  
Study Research ◽  
Case Selection ◽  
Study Selection

This chapter discusses quantitative and qualitative practices of case-study selection when the goal of the analysis is to evaluate causal hypotheses. More specifically, it considers how the different causal models used in the qualitative and quantitative research cultures shape the kind of cases that provide the most leverage for hypothesis testing. The chapter examines whether one should select cases based on their value on the dependent variable. It also evaluates the kinds of cases that provide the most leverage for causal inference when conducting case-study research. It shows that differences in research goals between quantitative and qualitative scholars yield distinct ideas about best strategies of case selection. Qualitative research places emphasis on explaining particular cases; quantitative research does not.

scholarly journals Health system resilience: a literature review of empirical research

2020 ◽  
Vol 35 (8) ◽  
pp. 1084-1109
Author(s):  
Louise Biddle ◽  
Katharina Wahedi ◽  
Kayvan Bozorgmehr
Keyword(s):  
Health System ◽  
Mixed Methods Research ◽  
Quantitative Research ◽  
Empirical Studies ◽  
Disease Outbreaks ◽  
Building Blocks ◽  
Theoretical Work ◽  
Empirical Literature ◽  
Quantitative And Qualitative Research ◽  
System Resilience

Abstract The concept of health system resilience has gained popularity in the global health discourse, featuring in UN policies, academic articles and conferences. While substantial effort has gone into the conceptualization of health system resilience, there has been no review of how the concept has been operationalized in empirical studies. We conducted an empirical review in three databases using systematic methods. Findings were synthesized using descriptive quantitative analysis and by mapping aims, findings, underlying concepts and measurement approaches according to the resilience definition by Blanchet et al. We identified 71 empirical studies on health system resilience from 2008 to 2019, with an increase in literature in recent years (62% of studies published since 2017). Most studies addressed a specific crisis or challenge (82%), most notably infectious disease outbreaks (20%), natural disasters (15%) and climate change (11%). A large proportion of studies focused on service delivery (48%), while other health system building blocks were side-lined. The studies differed in terms of their disciplinary tradition and conceptual background, which was reflected in the variety of concepts and measurement approaches used. Despite extensive theoretical work on the domains which constitute health system resilience, we found that most of the empirical literature only addressed particular aspects related to absorptive and adaptive capacities, with legitimacy of institutions and transformative resilience seldom addressed. Qualitative and mixed methods research captured a broader range of resilience domains than quantitative research. The review shows that the way in which resilience is currently applied in the empirical literature does not match its theoretical foundations. In order to do justice to the complexities of the resilience concept, knowledge from both quantitative and qualitative research traditions should be integrated in a comprehensive assessment framework. Only then will the theoretical ‘resilience idea’ be able to prove its usefulness for the research community.

Leaning Causal Models with Conditional Causal Probabilities from Data

2002 ◽  
Vol 6 (1) ◽  
pp. 25-32
Author(s):  
Koichi Yamada ◽  
Keyword(s):  
Numerical Experiments ◽  
Causal Model ◽  
Causal Models ◽  
Conditional Probabilities ◽  
Prior Probabilities ◽  
Counting Data

We propose a way to lean probabilistic causal models using conditional causal probabilities (CCPs) to represent uncertainty of causalities. The CCP is a probability devised by Peng and Reggia representing the uncertainty that a cause actually causes an effect given the cause. The main advantage of using CCPs is that they represent exact probabilities of causalities that people recognize mentally, and that the number of probabilities used in the causal model is far smaller than that of conditional probabilities by all combinations of possible causes. Thus, Peng and Reggia assumed that CCPs are given by human experts as subjective ones, and did not discuss how to calculate them from data when a dataset was available. We address this problem, starting from a discussion about properties of data frequently given in practical problems, and shows that prior probabilities that should be learned may differ from those derived by counting data. We then discuss and propose how to learn prior probabilities and CCPs from data, and evaluate the proposed method through numerical experiments and analyze results to show that the precision of leaned models is satisfactory.

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