Researchers are often interested in comparing statistical network models estimated from groups that are defined by the sum-score of the modeled variables. A prominent example is an analysis that ...compares networks of individuals with and without a diagnosis of a certain disorder. Recently, several authors suggested that this practice may lead to invalid inferences by introducing Berkson's bias. In this article, we show that whether bias is present or not depends on which research question one aims to answer. We review five possible research questions one may have in mind when separately estimating network models in groups that are based on sum-scores. For each research question, we provide an illustration with a simulated bivariate example and discuss the nature of the bias, if present. We show that if one is indeed interested in the network models of the groups defined by the sum-score, no bias is introduced. However, if one is interested in differences across groups defined by a variable other than the sum-score, detecting population heterogeneity, the network model in the general population, or inferring causal relations, then bias will be introduced in most situations. Finally, we discuss for each research question how bias can be avoided.
Translational AbstractResearchers in clinical psychology and psychiatry increasingly study relationships between symptoms of mental disorders using network models. In this context, it is often interesting to compare network models across groups, for example based on gender, age, or whether individuals are diagnosed with a disorder. The latter case is different from the first two, because diagnosis groups are themselves defined by the presence or absence of symptoms. Recently, several researchers have suggested that creating groups in this way leads to biased estimates. In this article, we show that whether or not bias occurs depends on the research question at hand. We consider five different types of research questions and in each case determine what the target of our analysis is, whether bias is present, and if so, where that bias is coming from. In each case, we outline how that bias could best be dealt with or avoided, showing that each research question requires a qualitatively different approach.
Testing the equality of two proportions is a common procedure in science, especially in medicine and public health. In these domains, it is crucial to be able to quantify evidence for the absence of ...a treatment effect. Bayesian hypothesis testing by means of the Bayes factor provides one avenue to do so, requiring the specification of prior distributions for parameters. The most popular analysis approach views the comparison of proportions from a contingency table perspective, assigning prior distributions directly to the two proportions. Another, less popular approach views the problem from a logistic regression perspective, assigning prior distributions to logit‐transformed parameters. Reanalyzing 39 null results from the New England Journal of Medicine with both approaches, we find that they can lead to markedly different conclusions, especially when the observed proportions are at the extremes (ie, very low or very high). We explain these stark differences and provide recommendations for researchers interested in testing the equality of two proportions and users of Bayes factors more generally. The test that assigns prior distributions to logit‐transformed parameters creates prior dependence between the two proportions and yields weaker evidence when the observations are at the extremes. When comparing two proportions, we argue that this test should become the new default.
We propose to use the squared multiple correlation coefficient as an effect size measure for experimental analysis‐of‐variance designs and to use Bayesian methods to estimate its posterior ...distribution. We provide the expressions for the squared multiple, semipartial, and partial correlation coefficients corresponding to four commonly used analysis‐of‐variance designs and illustrate our contribution with two worked examples.
Empirical scientists cannot do without statistics. This fact is reflected by the pervasiveness of statistics courses in the curricula of essentially all scientific disciplines. Unfortunately, many ...students exhibit statistics anxiety, that is, "feelings of anxiety . . . when taking a statistics course or doing statistical analyses" (Cruise, Cash, & Bolton, 1985, p. 92). In a recent publication, Siew, McCartney, and Vitevitch (2019) aim to shed new light on this highly relevant topic by using data analysis tools from the field of network science. However, just as with any other statistical model, one has to carefully assess the adequacy and robustness of a network model. In this commentary, we point to a number of shortcomings in the article by Siew et al. (2019) with respect to this goal that question their main conclusions. We explain each problem and suggest ways to address it. We hope that these suggestions help to put future investigation of statistics anxiety using network models on a firm methodological basis.
Researchers frequently wish to assess the equality or inequality of groups, but this comes with the challenge of adequately adjusting for multiple comparisons. Statistically, all possible ...configurations of equality and inequality constraints can be uniquely represented as partitions of the groups, where any number of groups are equal if they are in the same partition. In a Bayesian framework, one can adjust for multiple comparisons by constructing a suitable prior distribution over all possible partitions. Inspired by work on variable selection in regression, we propose a class of flexible beta-binomial priors for Bayesian multiple comparison adjustment. We compare this prior setup to the Dirichlet process prior suggested by Gopalan and Berry (1998) and multiple comparison adjustment methods that do not specify a prior over partitions directly. Our approach to multiple comparison adjustment not only allows researchers to assess all pairwise (in)equalities, but in fact all possible (in)equalities among all groups. As a consequence, the space of possible partitions grows quickly - for ten groups, there are already 115,975 possible partitions - and we set up a stochastic search algorithm to efficiently explore the space. Our method is implemented in the Julia package EqualitySampler, and we illustrate it on examples related to the comparison of means, variances, and proportions.
Registered Reports for Student Research Maedbh King; Fabian Dablander; Lea Jakob ...
Journal of European psychology students,
04/2016, Letnik:
7, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The pre-registration of research via registered reports is a recent development in the field of psychology. The aim of pre-registration is to encourage research that presents sound hypotheses and ...methodology (Chambers, 2014) in order to counter undesirable but prevalent research practices such as cherry-picking and p-hacking. In this Letter from the Editors, we wish to echo calls for registered reports and outline how we, the Editors at the Journal of European Psychology Students (JEPS), plan to introduce registered reports for student research. We address the issues necessitating the introduction of registered reports and outline the approach needed for implementing this initiative in a student journal.
Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, ...requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions - since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time - analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called 'contact sequence centrality', which quantifies the impact of an individual on the contact sequences, reflecting the individual's behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential 'behavioral super-spreaders'. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.
Testing the equality of two proportions is a common procedure in science, especially in medicine and public health. In these domains it is crucial to be able to quantify evidence for the absence of a ...treatment effect. Bayesian hypothesis testing by means of the Bayes factor provides one avenue to do so, requiring the specification of prior distributions for parameters. The most popular analysis approach views the comparison of proportions from a contingency table perspective, assigning prior distributions directly to the two proportions. Another, less popular approach views the problem from a logistic regression perspective, assigning prior distributions to logit-transformed parameters. Reanalyzing 39 null results from the New England Journal of Medicine with both approaches, we find that they can lead to markedly different conclusions, especially when the observed proportions are at the extremes (i.e., very low or very high). We explain these stark differences and provide recommendations for researchers interested in testing the equality of two proportions and users of Bayes factors more generally. The test that assigns prior distributions to logit-transformed parameters creates prior dependence between the two proportions and yields weaker evidence when the observations are at the extremes. When comparing two proportions, we argue that this test should become the new default.
PDF
