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Regression analysis : a practical introduction
\"With the rise of \"big data\", there is an increasing demand to learn the skills needed to undertake sound quantitative analysis without requiring students to spend too much time on high-level math and proofs. This book provides an efficient alternative approach, with more time devoted to the practical aspects of regression analysis and how to recognise the most common pitfalls. By doing so, the book will better prepare readers for conducting, interpreting, and assessing regression analyses, while simultaneously making the material simpler and more enjoyable to learn. Logical and practical in approach, Regression Analysis teaches: (1) the tools for conducting regressions; (2) the concepts needed to design optimal regression models (based on avoiding the pitfalls); and (3) the proper interpretations of regressions. Furthermore, this book emphasizes honesty in research, with a prevalent lesson being that statistical significance is not the goal of research. This book is an ideal introduction to regression analysis for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand what regressions do, what their limitations are, and what they can tell us. This will be the most engaging book on regression analysis (or Econometrics) you will ever read!\"-- Provided by publisher.
Book
Digital Games, Design, and Learning: A Systematic Review and Meta-Analysis
In this meta-analysis, we systematically reviewed research on digital games and learning for K–16 students. We synthesized comparisons of game versus nongame conditions (i.e., media comparisons) and comparisons of augmented games versus standard game designs (i.e., value-added comparisons). We used random-effects meta-regression models with robust variance estimates to summarize overall effects and explore potential moderator effects. Results from media comparisons indicated that digital games significantly enhanced student learning relative to nongame conditions (ḡ = 0.33, 95% confidence interval [0.19, 0.48], k = 57, n = 209). Results from value-added comparisons indicated significant learning benefits associated with augmented game designs (ḡ = 0.34, 95% confidence interval [0.17, 0.51], k = 20, n = 40). Moderator analyses demonstrated that effects varied across various game mechanics characteristics, visual and narrative characteristics, and research quality characteristics. Taken together, the results highlight the affordances of games for learning as well as the key role of design beyond medium.
Journal Article
Handbook of regression analysis
\"Written by two established experts in the field, the purpose of this handbook is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or \"refresher\" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by hands-on examples. Software routines are available via an author-maintained web site\"-- Provided by publisher.
Book
Statistically Controlling for Confounding Constructs Is Harder than You Think
Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest--in some cases approaching 100%--when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity.
Journal Article
Mostly harmless econometrics : an empiricist's companion
In addition to econometric essentials, this book covers important new extensions as well as how to get standard errors right. The authors explain why fancier econometric techniques are typically unnecessary and even dangerous.
Book
Cognition and Memory after Covid-19 in a Large Community Sample
Investigators found that persons with resolved persistent symptoms after Covid-19 had objectively measured cognition similar to that in persons with short-duration symptoms, although small deficits remained.
Journal Article
Regression models for categorical, count, and related variables : an applied approach
\"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented under the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book\"--Provided by publisher.
Book
What motivates consumers to buy organic foods? Results of an empirical study in the United States
Consumers perceive organic foods as more nutritious, natural, and environmentally friendly than non-organic or conventional foods. Since organic foods developed, studies on consumer behavior and organic foods have contributed significantly to its development. The presesent study aims to identify the factors affecting consumer buying behaviour toward organic foods in the United States. Survey data are collected from 770 consumers in the Midwest, United States. ANOVA, multiple linear regression, factor analysis, independent t-tests, and hierarchical multiple regression analysis are used to analyze the collected primary data. This research confirms health consciousness, consumer knowledge, perceived or subjective norms, and perception of price influence consumers’ attitudes toward buying organic foods. Availability is another factor that affected the purchase intentions of consumers. Age, education, and income are demographic factors that also impact consumers’ buying behavior. The findings help marketers of organic foods design strategies to succeed in the US’s fast-growing organic foods market.
Journal Article
Total ecosystem carbon stocks of mangroves across broad global environmental and physical gradients
Mangroves sequester large quantities of carbon (C) that become significant sources of greenhouse gases when disturbed through land-use change. Thus, they are of great value to incorporate into climate change adaptation and mitigation strategies. In response, a global network of mangrove plots was established to provide policy-relevant ecological data relating to interactions of mangrove C stocks with climatic, tidal, plant community, and geomorphic factors. Mangroves from 190 sites were sampled across five continents encompassing large biological, physical, and climatic gradients using consistent methodologies for the quantification of total ecosystem C stocks (TECS). Carbon stock data were collected along with vegetation, physical, and climatic data to explore potential predictive relationships. There was a 28-fold range in TECS (79–2,208 Mg C/ha) with a mean of 856 ± 32 Mg C/ha. Belowground C comprised an average 85% of the TECS. Mean soil depth was 216 cm, ranging from 22 to >300 cm, with 68 sites (35%) exceeding a depth of 300 cm. TECS were weakly correlated with metrics of forest structure, suggesting that aboveground forest structure alone cannot accurately predict TECS. Similarly, precipitation was not a strong predictor of TECS. Reasonable estimates of TECS were derived via multiple regression analysis using precipitation, soil depth, tree mass, and latitude (𝑅² = 0.54) as variables. Soil carbon to a 1 m depth averaged 44% of the TECS. Limiting analyses of soil C stocks to the top 1 m of soils result in large underestimates of TECS as well as in the greenhouse gas emissions that would arise from their conversion to other land uses. The current IPCC Tier 1 default TECS value for mangroves is 511 Mg C/ha, which is only 60% of our calculated global mean. This study improves current assessments of mangrove C stocks providing a foundation necessary for C valuation related to climate change mitigation. We estimate mangroves globally store about 11.7 Pg C: an aboveground carbon stock of 1.6 Pg C and a belowground carbon stock of 10.2 Pg C). The differences in the estimates of total ecosystem carbon stocks based on climate, salinity, forest structure, geomorphology, or geopolitical boundaries are not as much of an influence as the choice of soil depth included in the estimate. Choosing to limit soils to a 1 m depth resulted in estimates of <5 Pg whereas those that included the soil profile >1 m depth resulted in global carbon stock estimates that exceeded 11.2 Pg C.
Journal Article