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This chapter focuses on experimental designs, in which one or more factors are randomly assigned and manipulated. The first topic is statistical power or the likelihood of obtaining a significant result, which depends on several aspects of design. Second, the chapter examines the factors (independent variables) in a design, including the selection of levels of a factor and their treatment as fixed or random, and then dependent variables, including the selection of items, stimuli, or other aspects of a measure. Finally, artifacts and confounds that can affect the validity of results are addressed, as well as special designs for studying mediation. A concluding section raises the possibility that traditional conceptualizations of design – generally focusing on a single study and on the question of whether a manipulation has an effect – may be inadequate in the current world where multiple-study research programs are the more meaningful unit of evidence, and mediational questions are often of primary interest.
The accumulation of empirical evidence that has been collected in multiple contexts, places, and times requires a more comprehensive understanding of empirical research than is typically required for interpreting the findings from individual studies. We advance a novel conceptual framework where causal mechanisms are central to characterizing social phenomena that transcend context, place, or time. We distinguish various concepts of external validity, all of which characterize the relationship between the effects produced by mechanisms in different settings. Approaches to evidence accumulation require careful consideration of cross-study features, including theoretical considerations that link constituent studies and measurement considerations about how phenomena are quantifed. Our main theoretical contribution is developing uniting principles that constitute the qualitative and quantitative assumptions that form the basis for a quantitative relationship between constituent studies. We then apply our framework to three approaches to studying general social phenomena: meta-analysis, replication, and extrapolation.
from
Part I
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The Philosophy and Methodology of Experimentation in Sociology
Davide Barrera, Università degli Studi di Torino, Italy,Klarita Gërxhani, Vrije Universiteit, Amsterdam,Bernhard Kittel, Universität Wien, Austria,Luis Miller, Institute of Public Goods and Policies, Spanish National Research Council,Tobias Wolbring, School of Business, Economics and Society at the Friedrich-Alexander-University Erlangen-Nürnberg
Sociology is a science concerning itself with the interpretive understanding of social action and thereby with a causal explanation of its course and consequences. Empirically, a key goal is to find relations between variables. This is often done using naturally occurring data, survey data, or in-depth interviews. With such data, the challenge is to establish whether a relation between variables is causal or merely a correlation. One approach is to address the causality issue by applying proper statistical or econometric techniques, which is possible under certain conditions for some research questions. Alternatively, one can generate new data with experimental control in a laboratory or the field. It is precisely through this control via randomization and the manipulation of the causal factors of interest that the experimental method ensures – with a high degree of confidence – tests of causal explanations. In this chapter, the canonical approach to causality in randomized experiments (the Neyman–Rubin causal model) is first introduced. This model formalizes the idea of causality using the "potential outcomes" or "counterfactual" approach. The chapter then discusses the limits of the counterfactual approach and the key role of theory in establishing causal explanations in experimental sociology.
from
Part II
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The Practice of Experimentation in Sociology
Davide Barrera, Università degli Studi di Torino, Italy,Klarita Gërxhani, Vrije Universiteit, Amsterdam,Bernhard Kittel, Universität Wien, Austria,Luis Miller, Institute of Public Goods and Policies, Spanish National Research Council,Tobias Wolbring, School of Business, Economics and Society at the Friedrich-Alexander-University Erlangen-Nürnberg
Field experiments have a long tradition in some areas of the social and behavioral sciences and have become increasingly popular in sociology. Field experiments are staged in "natural" research settings where individuals usually interact in everyday life and regularly complete the task under investigation. The implementation in the field is the core feature distinguishing the approach from laboratory experiments. It is also one of the major reasons why researchers use field experiments; they allow incorporating social context, investigating subjects under "natural" conditions, and collecting unobtrusive measures of behavior. However, these advantages of field experiments come at the price of reduced control. In contrast to the controlled setting of the laboratory, many factors can influence the outcome but are not under the experimenter’s control and are often hard to measure in the field. Using field experiments on the broken windows theory, the strengths and potential pitfalls of experimenting in the field are illustrated. The chapter also covers the nascent area of digital field experiments, which share key features with other types of experiments but offer exciting new ways to study social behavior by enabling the collection large-scale data with fine-grained and unobtrusive behavioral measures at relatively low variable costs.
from
Part II
-
The Practice of Experimentation in Sociology
Davide Barrera, Università degli Studi di Torino, Italy,Klarita Gërxhani, Vrije Universiteit, Amsterdam,Bernhard Kittel, Universität Wien, Austria,Luis Miller, Institute of Public Goods and Policies, Spanish National Research Council,Tobias Wolbring, School of Business, Economics and Society at the Friedrich-Alexander-University Erlangen-Nürnberg
Laboratory experiments are the type of study that most people have in mind when talking about experiments. In this chapter, we first discuss the strengths of laboratory experiments, which offer the highest degree of experimental control as compared to other types of experiments. Single factors can be manipulated according to the requirements of theories under highly controlled conditions. As such, laboratory experiments are well-placed to test theories. We then introduce a sociological laboratory experiment as a leading example, which we use as a reference for a discussion of several principles of laboratory research. Furthermore, we discuss a second goal of laboratory experiments, which is the establishment of empirical regularities in situations where theory does not provide sufficient guidance for deriving behavioral expectations. The chapter concludes with a short discussion of caveats for the analysis of sociological data generated in laboratory experiments.
Over the past decades, bilingualism researchers have come to a consensus around a fairly strong view of nonselectivity in bilingual speakers, often citing Van Hell and Dijkstra (2002) as a critical piece of support for this position. Given the study’s continuing relevance to bilingualism and its strong test of the influence of a bilingual’s second language on their first language, we conducted an approximate replication of the lexical decision experiments in the original study (Experiments 2 and 3) using the same tasks and—to the extent possible—the same stimuli. Unlike the original study, our replication was conducted online with Dutch–English bilinguals (rather than in a lab with Dutch–English–French trilinguals). Despite these differences, results overall closely replicated the pattern of cognate facilitation effects observed in the original study. We discuss the replication of outcomes and possible interpretations of subtle differences in outcomes and make recommendations for future extensions of this line of research.
This study aimed to closely replicate Wiseheart et al. (Bilingualism: Language and Cognition, 19(1), 141–146, 2016) by investigating the transferability of language-switching skills to nonlinguistic task switching. Current evidence is mixed and there is a need to conduct robust replications in this area. Bilingual (n = 31) and monolingual (n = 47) young adults characterized stimuli by either colour or shape based on a given cue. Modifications include online data collection (as opposed to in-person) and adapting the nonverbal intelligence test used. All other aspects of the study mirror those by Wiseheart et al. Results indicate that the bilinguals exhibited better cognitive flexibility in task switching, as evidenced by a reduced global switch cost compared with monolinguals. In contrast, mixed evidence was found for local switch costs. Findings mirror those reported by Wiseheart et al. and suggest that by employing comparable task-switch paradigms and recruiting samples matched on several key variables, including age, gender, variety of languages spoken, and use of English, bilingualism does seem to confer broader executive function advantages. Findings are discussed in relation to theoretical implications to inform future replication studies and advance the bilingual advantage in the switching debate.
Used by politicians, journalists, and citizens, Twitter has been the most important social media platform to investigate political phenomena such as hate speech, polarization, or terrorism for over a decade. A high proportion of Twitter studies of emotionally charged or controversial content limit their ability to replicate findings due to incomplete Twitter-related replication data and the inability to recrawl their datasets entirely. This paper shows that these Twitter studies and their findings are considerably affected by nonrandom tweet mortality and data access restrictions imposed by the platform. While sensitive datasets suffer a notably higher removal rate than nonsensitive datasets, attempting to replicate key findings of Kim’s (2023, Political Science Research and Methods 11, 673–695) influential study on the content of violent tweets leads to significantly different results. The results highlight that access to complete replication data is particularly important in light of dynamically changing social media research conditions. Thus, the study raises concerns and potential solutions about the broader implications of nonrandom tweet mortality for future social media research on Twitter and similar platforms.
This study was an approximate replication of Rothman (2011),examining the determiner phrase syntax of a large sample (n = 211) of L3 learners of Portuguese who spoke English and Spanish. Rothman (2011) investigated whether L3 Italian or Brazilian Portuguese speakers are differently impacted by another known Romance Language, if it was their L1 or L2. The original study concluded that groups did not perform differently on experimental tasks on the basis of a null effect, and that the typological similarity of Spanish, Portuguese, or Italian predicts transfer in the initial stages of L3 acquisition. The present replication recreated all materials, which were unavailable, and examined the same population and questions. However, rather than examining L3 Italian and L3 Brazilian Portuguese, the present work maintained a constant L3 Portuguese. Learners were divided into two groups in a mirror-image design (n = 96 L1 English-L2 Spanish, n = 115 L1 Spanish-L2 English), and data were collected online. Like the original study, there was no main effect of group in any of the two-way analyses of variance. However, results show that it should not be assumed that experimental groups behave equivalently based on a null effect: Of the four total post hoc tests of equivalence, only two were significant when the equivalence bounds were set at a small effect size (d = $ \pm $ .4). Ultimately, it is argued that determining the smallest effect size of interest and subsequent equivalence testing are necessary to answer key questions in the field of L3 acquisition.
Prominent recent work argues that support for democracy behaves thermostatically—that democratic erosion boosts democratic support while deepening democracy yields public backlash—and further contends that there is no evidence for the classic argument that democracy itself increases democratic support over time. Here, we document how these conclusions depend on subtle choices in measurement coding that constitute “researcher degrees of freedom”: analyses employing alternative reasonable choices provide little or no support for the original conclusions. The fragility of the statistical results demonstrates that researcher degrees of freedom in measurement must be taken seriously and that the question of the relationship between democratic institutions and democratic support remains unsettled.
This chapter provides a basic introduction to virology, dealing with the history of viruses, taxonomy, virus replication stages (attachment, entry, uncoating, transcription, translation, assembly and release). It also deals with the immune response to viruses, including innate immune response, adaptive immunity, cell-mediated response, antibody-mediated response, viral pathogenesis, viral tropisms, viral spread, viral persistence, viral virulence and host factors.
Positionality statements have increasingly become the norm in many strands of social science research, including applied linguistics. With reference to current research, theory, and the author’s own work, this paper reviews some of the promises and perils of such statements, including their performativity and lack of reflexivity. The author concludes by arguing that positionality statements need to offer both more and less, to be better targeted, and be more effectively and widely utilized within the field of applied linguistics.
High-quality replication studies are widely understood to be critical to the growth and credibility of our discipline, as shown in commentaries and discussion since the 1970s, at least. Nevertheless, misunderstandings and limitations in the aims, designs, and reporting of replication research remain, thus reducing the usefulness and impact of replications. To address this issue and improve the rigor, quality, and conduct of replication studies in applied linguistics, a new standard for reporting replication studies that captures several critical features of replication research not discussed in current reporting guidelines is proposed. Importantly, this standard applies basic expectations in replication reporting so that outcomes can be better understood and evaluated. By applying this standard, replication studies will better meet their aims to confirm, consolidate, and advance knowledge and understanding within applied linguistics and second language research. In addition, readers will more easily understand how the replication study was carried out and be able to better evaluate the claims being made.
As the scientific community becomes aware of low replicability rates in the extant literature, peer-reviewed journals have begun implementing initiatives with the goal of improving replicability. Such initiatives center around various rules to which authors must adhere to demonstrate their engagement in best practices. Preliminary evidence in the psychological science literature demonstrates a degree of efficacy in these initiatives. With such efficacy in place, it would be advantageous for other fields of behavioral sciences to adopt similar measures. This letter provides a discussion on lessons learned from psychological science while similarly addressing the unique challenges of other sciences to adopt measures that would be most appropriate for their field. We offer broad considerations for peer-reviewed journals in their implementation of specific policies and recommend that governing bodies of science prioritize the funding of research that addresses these measures.
In a ‘very close replication’ study using the same attributes as the original, Chandrashekar et al. (2021) report a failure to replicate some choose–reject problems documented in Shafir (1993). We find that several of the original attributes have changed their valence three decades later, and we compose new versions with updated attributes that fully replicate Shafir’s (1993) original findings. Despite their apparent exactitude, ‘very close replications’ across contexts or time, when stimuli may have changed their meaning or valence, can be highly misleading, further exacerbating replication concerns.
While working memory capacity is associated with superior performance on a number of tasks, could it paradoxically sometimes be associated with suboptimal performance? Corbin et al. (2010, Judgment and Decision Making 5(2), 110–115) found that, in a between-subjects design, higher WMC is associated with a larger risky-choice framing effect, traditionally conceived of as a departure from rational principles. Such surprising findings are of potentially great theoretical importance; yet the original study was underpowered. In this registered report, we aimed to replicate and extend the original findings, by conducting an online experiment among 425 North Americans. To extend the findings beyond the specific single tasks used in the original study, we used three WMC tasks with different processing components and six framing problems involving human lives. In a close replication, the frame significantly interacted with neither the Ospan short absolute score nor the Ospan short partial score in predicting ratings on the disease-framing problem. Similarly, in an extended replication, a composite WMC score did not significantly interact with the frame in predicting ratings on three framing problems involving human lives. The Bayes factors showed that the data were 3 to 10 times more likely under the null hypothesis of no interaction between WMC and frame. Taken together, these findings show an absence of association between the between-subjects risky-choice framing effect and WMC. This outcome is compatible with four out of the six theoretical accounts we considered, and is uniquely predicted by the default-interventionist dual-process account and the pragmatic inference account. Further research can more rigorously pit conflicting predictions of these accounts against each other.
This chapter deals with research priorities that were obtained during the writing of this book. We first illustrate the recent insights that were published since the publication of the first volume. New research topics deal with further exploring and identifying critical habitat components and the effect of land improvement initiatives. Demographics need to be studied in less covered areas using methods that have been perfectioned in the typical highly researched countries. Examining responses of Little Owl populations to land uses and the effects of abiotic environmental factors should allow for more quantitative management and follow-up on the effectiveness of taken measures. The adoption of the information-theoretic approach, focus on process variation and searching for mechanisms will need more statistical background and thoroughness, leading to even more long-term observational studies and focus on the cumulative effects. To do this in an optimal way, more experiments are urgently needed, to enable controling for certain parameters. Finally there is a need for the expansion of the investigated geographic range and an increase in research and experiment maturity in emerging countries, hopefully enabled by highly mature research teams and international co-operation.
Confronting models with data is only effective when the statistical model matches the biological one and the structure of your data collection is right for the statistical model. We outline some basic principles of sampling, emphasizing the importance of randomization. Randomization is also essential to experimental design, but so are controls, replication of experimental units, and independence of experimental units. This chapter emphasizes the distinction between sampling or experimental units representing independent instances and observational units representing things we measure or count from those units. Observational units may be subsamples of experimental units, but shouldn’t be confused with them. In this chapter, we also introduce methods for deciding how much data you need.
It’s surprisingly common for biologists to combine crossed and nested factors. These designs are partly nested or split-plot designs. They are nearly always mixed models, usually a random nested effect and at least two fixed effects. We describe the analysis of these designs, starting with a simple three-factor design with a single between-plot and a single within-plot effect, extending this analysis to include multiple effects, including interactions at this level, and adding continuous predictors (covariates).
Most biological ideas can be viewed as models of nature we create to explain phenomena and predict outcomes in new situations. We use data to determine these models’ credibility. We translate our biological models into statistical ones, then confront those models with data. A mismatch suggests the biological model needs refinement. A biological idea can also be considered a signal that appears in the data among the background noise. Fitting the model to the data lets us see if such a signal exists and, importantly, measure its strength. This approach only works well if our biological hypotheses are clear, the statistical models match the biology, and we collect the data appropriately. This clarity is the starting point for any biological research program.