scholarly journals The Taboo Against Explicit Causal Inference in Nonexperimental Psychology

2020 ◽  
Vol 15 (5) ◽  
pp. 1243-1255 ◽  
Author(s):  
Michael P. Grosz ◽  
Julia M. Rohrer ◽  
Felix Thoemmes
Keyword(s):  
Data Analysis ◽  
Causal Inference ◽  
Study Design ◽  
Causal Reasoning ◽  
Causal Effects ◽  
Future Research ◽  
Subsequent Work ◽  
Causal Mechanisms ◽  
Causal Assumptions ◽  
Research Questions

Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policymaking. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects—the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policymakers.

scholarly journals The Taboo Against Explicit Causal Inference in Nonexperimental Psychology

2020 ◽  
Author(s):  
Michael P. Grosz ◽  
Julia Marie Rohrer ◽  
Felix Thoemmes
Keyword(s):  
Data Analysis ◽  
Causal Inference ◽  
Causal Reasoning ◽  
Policy Making ◽  
Causal Effects ◽  
Future Research ◽  
Subsequent Work ◽  
Policy Makers ◽  
Causal Assumptions ◽  
Research Questions

Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policy making. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects—the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policy makers.

Causal Explanation

2017 ◽  
Author(s):  
Tania Lombrozo ◽  
Nadya Vasilyeva
Keyword(s):  
Causal Inference ◽  
Causal Reasoning ◽  
Causal Explanation ◽  
Future Research ◽  
Causal Mechanisms ◽  
The Relationship

Explanation and causation are intimately related. Explanations often appeal to causes, and causal claims are often answers to implicit or explicit questions about why or how something occurred. This chapter considers what we can learn about causal reasoning from research on explanation. In particular, it reviews an emerging body of work suggesting that explanatory considerations—such as the simplicity or scope of a causal hypothesis—can systematically influence causal inference and learning. It also discusses proposed distinctions among types of explanations and reviews the effects of each explanation type on causal reasoning and representation. Finally, it considers the relationship between explanations and causal mechanisms and raises important questions for future research.

Data Documentation and Informed Consent in Research

2018 ◽  
pp. 127-154
Author(s):  
Dzever Linus Terry ◽  
Esan Adepeju Amudalat
Keyword(s):  
Data Analysis ◽  
Informed Consent ◽  
Information Sharing ◽  
Study Design ◽  
Current Work ◽  
Research Purpose ◽  
Future Research ◽  
Culturally Appropriate ◽  
User Friendliness ◽  
Data Documentation

This chapter examines data documentation and informed consent in research. The exercise of documenting data provides an opportunity for researchers to reflect on a study to guide current work and inform and enhance future research. The chapter explains data documentation, how data is created or digitized, what data mean, what their content and structure are and any data manipulations that may have taken place, specific steps for documenting data-based research beginning with the study design and ending with data analysis and guidelines for obtaining informed consent, developing culturally appropriate methods of sharing information for research purpose and discusses prevailing views on current controversies, ambiguities and problems with these guidelines and suggests potential solutions. The literature also includes many references to the need for data documentation and informed consent. Further research is needed to examine the validity and user friendliness of innovations in information sharing procedures for obtaining consent in different cultural settings.

Compositional Data Analysis in Tourism: Review and Future Directions

2020 ◽  
Vol 25 (1) ◽  
pp. 153-168 ◽  
Author(s):  
Germà Coenders ◽  
Berta Ferrer-Rosell
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Future Research ◽  
Compositional Data Analysis ◽  
Relative Importance ◽  
Future Directions ◽  
Tourist Satisfaction ◽  
Tourist Flows ◽  
Research Questions ◽  
Tourism And Hospitality

Compositional data analysis (CoDa) is the standard statistical methodology when data contain information about the relative importance of parts of a whole. Many research questions in tourism are either related to distribution of a whole (e.g., distribution, share, allocation, etc.) or relative importance (e.g., dominance, concentration, profile, etc.). Example research questions might be: How does time allocated to different types of activities relate to tourist satisfaction? or Which origins and destinations concentrate the most tourist flows per tourist segment? The first aim of this article is to present the manner in which CoDa solves statistical problems that arise when treating compositional data with classical statistical methods (e.g., spurious correlations, meaningless distances, assumption violation). The second aim is to review all CoDa applications in tourism and hospitality to date. The third is to present CoDa applications in related fields (e.g., finance, sociology, geography, economics, management, ecology, education), which can be translated into future research in tourism. In order to show how to apply the most common CoDa tools (exploratory analysis of compositions, and use of compositions as variables in a model) an example of restaurant menu styles is used.

scholarly journals Identification of causal effects in case-control studies

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Bas B. L. Penning de Vries ◽  
Rolf H. H. Groenwold
Keyword(s):  
Causal Inference ◽  
Case Control ◽  
Causal Effects ◽  
Future Research ◽  
Causal Interpretation ◽  
Case Control Studies ◽  
Sampling Schemes ◽  
Intention To Treat ◽  
Study Results ◽  
Control Designs

Abstract Background Case-control designs are an important yet commonly misunderstood tool in the epidemiologist’s arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Results We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Conclusion The modern epidemiologist’s arsenal for causal inference is well-suited to make transparent for case-control designs what assumptions are necessary or sufficient to endow the respective study results with a causal interpretation and, in turn, help resolve or prevent misunderstanding. Our approach may inform future research on different estimands, other variations of the case-control design or settings with additional complexities.

OUTDOOR CAMP EFFECTS ON ATHLETES: ORIENTEERING EXAMPLE

2017 ◽  
Vol 1 (104) ◽  
pp. 55-64 ◽  
Author(s):  
Kadir Yıldız ◽  
Pınar Güzel ◽  
Fırat Çetinöz ◽  
Tolga Beşikçi
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Outdoor Education ◽  
Study Group ◽  
Analysis Method ◽  
Data Collection Tool ◽  
Main Research ◽  
Interview Technique ◽  
Content Analysis Method ◽  
Research Questions

Background. In this research, we aimed to investigate the effects of outdoor camps on orienteering athletes. Methods. The study group consisted of 74 athletes (44 males and 30 females, aged 11.94 ± 1.32 years) who participated in Bolu outdoor camp on the 3 rd –13 th of August, 2015. Interview technique, which is one of the qualitative research methods, was used as data collection tool and content analysis method was used for data analysis. Results. Demographic factors were interpreted after the analysis of the obtained data and three main research questions were discussed under the topics of the views of athletes about the concept of Orienteering which is an outdoor sport, themes and codes regarding the purpose of Orienteering by the students who participated in the outdoor camp, and themes and codes about the outcomes of Orienteering for the students who participated in outdoor camps. Conclusion. It is suggested that a policy must be developed within the Ministry of Youth and Sport and Sport Federations in order to disseminate more deliberate and more comprehensive outdoor education among young people and measures should be taken to provide extensive participation.

Autobiographical Memory Impairments as a Transdiagnostic Feature of Mental Illness: A meta-analysis of autobiographical memory specificity and overgenerality amongst people with psychiatric diagnoses

2020 ◽  
Author(s):  
Tom Joseph Barry ◽  
David John Hallford ◽  
Keisuke Takano
Keyword(s):  
Autobiographical Memory ◽  
Memory Retrieval ◽  
Keyword Search ◽  
Meta Analysis ◽  
Future Research ◽  
Psychiatric Diagnoses ◽  
Schizophrenia Spectrum Disorders ◽  
Memory Impairments ◽  
Memory Specificity ◽  
Causal Mechanisms

Decades of research has examined the difficulty that people with psychiatric diagnoses, such as Major Depressive Disorder, Schizophrenia Spectrum Disorders, and Posttraumatic Stress Disorder, have in recalling specific autobiographical memories from events that lasted less than a day. Instead, they seem to retrieve general events that have occurred many times or which occurred over longer periods of time, termed overgeneral memory. We present the first transdiagnostic meta-analysis of memory specificity/overgenerality, and the first meta-regression of proposed causal mechanisms. A keyword search of Embase, PsycARTICLES and PsycINFO databases yielded 74 studies that compared people with and without psychiatric diagnoses on the retrieval of specific (k = 85) or general memories (k = 56). Multi-level meta-analysis confirmed that people with psychiatric diagnoses typically recall fewer specific (g = -0.864, 95% CI[-1.030, -0.698]) and more general (g = .712, 95% CI[0.524, 0.900]) memories than diagnoses-free people. The size of these effects did not differ between diagnostic groups. There were no consistent moderators; effect sizes were not explained by methodological factors such as cue valence, or demographic variables such as participants’ age. There was also no support for the contribution of underlying processes that are thought to be involved in specific/general memory retrieval (e.g., rumination). Our findings confirm that deficits in autobiographical memory retrieval are a transdiagnostic factor associated with a broad range of psychiatric problems, but future research should explore novel causal mechanisms such as encoding deficits and the social processes involved in memory sharing and rehearsal.

Dysfunctional Behavior in Organizations: Insights from the Management Control Literature

2017 ◽  
Vol 37 (4) ◽  
pp. 117-141 ◽  
Author(s):  
Krista Fiolleau ◽  
Theresa Libby ◽  
Linda Thorne
Keyword(s):  
Risk Factors ◽  
Internal Control ◽  
Management Control ◽  
Future Research ◽  
Fraud Triangle ◽  
Audit Risk ◽  
Research Opportunities ◽  
Dysfunctional Behavior ◽  
Research Questions ◽  
Published Research

SUMMARY As the scope of the audit continues to broaden (Cohen, Krishnamoorthy, and Wright 2017), research questions in management control and internal control are beginning to overlap. Even so, there is little overlap between these fields in terms of published research to date. The purpose of this paper is to take a step in bridging the gap between the management control and the internal control literatures. We survey relevant findings from the extant management control literature published between 2003 and 2016 on dysfunctional behavior and the ways in which it might be mitigated. We then use the fraud triangle as an organizing framework to consider how the management control literature might help to address audit risk factors identified in SAS 99/AU SEC 316 (AICPA 2002). The outcome of our analysis is meant to identify and classify the extant management control literature of relevance to research on internal control in a manner that researchers new to the management control literature will find accessible. We conclude with a set of future research opportunities that can help to broaden the scope of current research in internal control.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

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