Data Feminism D'Ignazio, Catherine; Klein, Lauren F
The MIT Press eBooks,
03/2020
eBook
Odprti dostop
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
The open access edition of this book was made possible by generous funding from the ...MIT Libraries.
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.
Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”
Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
This book explains in a practical and dynamic way the relevant aspects of quantitative research methodology in health sciences. It explores research paradigms, the foundations of evidence-based ...clinical practice, the formulation, through different models, of the research question and objectives, definition of the scope of study, population and sample, and the classification of research variables. The book also considers different quantitative research designs, distinguishing between observational studies and experimental studies, the main quantitative data collection techniques, including some guidelines for the validation of questionnaires, descriptive statistical analysis, and inferential analysis. It will allow the reader to understand, in a simple way, the phases of the scientific method applied to quantitative research in health, acquire basic and advanced skills for the development of a health research project from a quantitative perspective, and develop the conceptual phase with the definition of the research problem, its objectives, and working hypotheses.
Seven Rules for Social Research Firebaugh, Glenn
Princeton University Press,
2018, 2018., 20180626, 2008, 2008-00-00, 20080101
eBook, Book
'Seven Rules for Social Research' teaches social scientists how to get the most out of their technical skills and tools, providing a resource that fully describes the strategies and concepts no ...researcher or student of human behaviour can do without.
In this article, we review applications of covariance-based structural equation modeling (SEM) in the Journal of Advertising (JA) starting with the first issue in 1972. We identify 111 articles from ...the earliest application of SEM in 1983 through 2015, and discuss important methodological issues related to the following aspects: confirmatory factor analysis (CFA), causal modeling, multiple group analysis, reporting, and guidelines for interpretation of results. Moreover, we summarize some issues related to varying terminology associated with different SEM methods. Findings indicate that the use of SEM in the JA contributes greatly to conceptual, empirical, and methodological advances in advertising research. The assessment contributes to the literature by offering advertising researchers a summary guide to best practices and a reminder of the basics that distinguish the powerful and unique approach involving structural analysis of covariances.
In Canada, non-traumatic dental conditions (NTDCs) presenting in emergency departments (EDs) are dealt with by non-dental professionals who are generally not equipped to deal with such emergencies, ...resulting in an inefficient usage of heath care resources. This study aimed to assess the burden of ED visits for NTDCs in Ontario by observing trends from 2006 to 2014.
Aggregate data for Ontario were obtained from the Canadian Institute for Health Information's National Ambulatory Care Reporting System. Data were examined for the whole of Ontario and stratified by 14 Local Health Integration Networks. Descriptive analysis was conducted for both number of people and number of visits, stratified by sex and age groups (0-5, 6-18, 19-64, and 65+ years). Numbers were also examined by neighbourhood stratifications, including urban/rural, income quintile and immigrant tercile.
Over the study period, an upward trend of visiting EDs for NTDCs was observed. Approximately 403 628 people in Ontario made 482 565 visits over the period of nine years. On average, 341 per 100 000 people, per year, visited. Young children, people living in neighbourhoods with lower income and higher immigrant concentration, and people living in the rural regions, visited EDs more for NTDCs during 2006-2014.
The upward and inequitable trends of utilization of EDs for NTDCs reinforce recognition of the important need for both universal and targeted approaches for primary prevention of dental conditions. To enhance equitable access to dental care, policy advocacy is required for publicly funding essential and emergency dental services for all.
The variable-centered approach is favored in management and applied psychology, but the person-centered approach is quickly growing in popularity. A partial cause for this rise is the finer-grained ...detail that it allows. Many researchers may be unaware, however, that another approach may provide even finer-grained detail: the person-specific approach. In the current article, we (a) detail the purpose of each approach, (b) describe how to determine when each approach is most appropriate, and (c) delineate when the approaches diverge to give differing results. Through achieving these goals, we suggest that no single approach is the “best.” Instead, the choice of approach should be guided by the research question. To further emphasize this point, we provide illustrative examples using real data to answer three distinct research questions. The results show that each research question can be fully addressed only by the appropriate approach. To conclude, we directly suggest certain research areas that may benefit from the application of person-centered and person-specific approaches. Together, we believe that discussing variable-centered, person-centered, and person-specific approaches together may provide a more thorough understanding of each.
Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers ...remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.
Advertising research is a scientific discipline that studies artifacts (e.g., various forms of marketing communication) as well as natural phenomena (e.g., consumer behavior). Empirical advertising ...research therefore requires methods that can model design constructs as well as behavioral constructs, which typically require different measurement models. This article presents variance-based structural equation modeling (SEM) as a family of techniques that can handle different types of measurement models: composites, common factors, and causal-formative measurement. It explains the differences between these types of measurement models and clears up possible ambiguity regarding formative endogenous constructs. The article proposes confirmatory composite analysis to assess the nomological validity of composites, confirmatory factor analysis (CFA) and the heterotrait-monotrait ratio of correlations (HTMT) to assess the construct validity of common factors, and the multiple indicator, multiple causes (MIMIC) model to assess the external validity of causal-formative measurement.
OBJECTIVES
: Filipino Americans form the second-largest Asian American and Pacific Islanders subgroup. Growing evidence suggests that Filipino Americans have higher rates of diabetes than ...non-Hispanic whites. The key objectives of this study are 1) to determine the prevalence of diabetes in non-obese Filipino Americans compared to non-obese non-Hispanic whites, and 2) to identify risk factors for diabetes in non-obese Filipino men and women.
METHODS
: Secondary analysis of population-based data from combined waves (2007, 2009 and 2011) of the adult California Health Interview Survey (CHIS). The study sample was restricted to non-obese Filipino Americans (
n
= 1629) and non-Hispanic whites (
n
= 72 072).
RESULTS
: Non-obese Filipino Americans had more than twice the odds of diabetes compared to non-Hispanic whites, even after correcting for several known risk factors (OR = 2.80,
p
< 0.001). For non-obese Filipino men, older age, poverty, cigarette smoking, and being overweight are associated with increased odds for diabetes, while older age was the only factor associated with diabetes among Filipina women.
DISCUSSION
: Diabetes prevention approaches need to be targeted towards non-obese Filipino Americans, due to their high risk of diabetes.
PDF
