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An editorial is presented in which the authors emphasize the importance of integrating theory, methods, and data to enhance management research. They critique the overreliance on null hypothesis significance testing (NHST) and suggest using Bayesian techniques, observed power, and longitudinal data to improve causal inference and theoretical development. The editorial also advocates for employing computational models and latent mixture models to better handle theoretical precision and heterogeneity, and it encourages exploring diverse approaches to strengthen research outcomes.

Small number of clusters combined with cluster level heterogeneity poses a great challenge for the data analysis. We have published a weighted Jackknife approach to address this issue applying weighted cluster means as the basic estimators. The current study proposes a new version of the weighted delete-one-cluster Jackknife analytic framework, which employs Ordinary Least Squares or Generalized Least Squares estimators as the fundamentals. Algorithms for computing estimated variances of the study estimators have also been derived. Wald test statistics can be further obtained, and the statistical comparison in the outcome means of two conditions is determined using the cluster permutation procedure. The simulation studies show that the proposed framework produces estimates with higher precision and improved power for statistical hypothesis testing compared to other methods. [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess the impact of unobserved heterogeneity on microfinance social efficiency analysis. Based on recent nonparametric techniques and directional distances, we identify a latent heterogeneity factor related to the microfinance institute (MFI) manager's ability to promote women, independent of MFI size. We test for the significance of this unobserved factor and analyze the impact of MFI social inefficiency measures. Using a cross-country sample of 501 MFIs in 2011 from six main regions of the world, our findings reveal a significant effect of unobserved heterogeneity on the frontier and hence stress the importance of subjective factors in defining the set of production possibilities. We assess the robustness of our findings with the considered profit-oriented status and analyze the link between our unobserved heterogeneity factor and institutional and socioeconomic indicators. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of partner technology heterogeneity on innovation performance of alliance firms both in terms of R&D output and the enhancement of partners' innovation capability. We apply a generalized semi‐parametric model on a questionnaire survey result of 413 High and New Technology Enterprises in China. In order to ensure the robustness and practicability of our result, PCA is applied to extract comprehensive information and SiZer analysis is employed to test the linearity and significance of the nonparametric functions in the model. Our results indicate that collaborations between partners with different industry technologies exert inversed U‐shaped R&D output pattern and affect very little the innovation ability of focal firms. The impact of industry domain divergence is no longer significant when partner technology heterogeneity is added in the model of analysis. Partner technology heterogeneity leads to an ascending S‐shaped R&D output pattern and contributes positively to innovation capabilities. One of the implications of our findings is that when choosing R&D alliance partners, firms are better off avoiding candidates from a different industry domain but opt for potential collaborators who are in the same industry but in the different technical domain, which may facilitate more effective organizational learning. Further, we argue that the reasons behind the S‐shaped R&D output pattern led by technology heterogeneity being the co‐existence of competition and cooperation between partners where firms collaborate in value‐creation by combining diverse resources and compete for acquiring partner's distinct technology and resources. Therefore, we suggest that, for the sustainability of collaborative innovation outcome, both cooperation and competition amongst alliance partners should be encouraged and well balanced at different stages of joint R&D projects. [ABSTRACT FROM AUTHOR]

This study develops methods for conducting uniform inference on quantile treatment effects for sharp regression discontinuity designs. We develop a score test for the treatment significance hypothesis and Wald-type tests for the hypotheses related to treatment significance, homogeneity, and unambiguity. The bias from the nonparametric estimation is studied in detail. In particular, we show that under some conditions, the asymptotic distribution of the score test is unaffected by the bias, without under-smoothing. For situations where the conditions can be restrictive, we incorporate a bias correction into the Wald tests and account for the estimation uncertainty. We also provide a procedure for constructing uniform confidence bands for quantile treatment effects. As an empirical application, we use the proposed methods to study the effect of cash-on-hand on unemployment duration. The results reveal pronounced treatment heterogeneity and also emphasize the importance of considering the long-term unemployed. [ABSTRACT FROM AUTHOR]

Behavioral researchers use analysis of variance (ANOVA) tests of differences between treatment means or chi-square tests of differences between proportions to provide support for empirical hypotheses about consumer behavior. These tests are typically conducted on data from "between-subjects" experiments in which participants were randomly assigned to conditions. We show that, despite using internally valid experimental designs such as this, aggregation biases can arise in which the theoretically critical pattern holds in the aggregate even though it holds for no (or few) individuals. First, we show that crossover interactions-often taken as strong evidence of moderating variables-can arise from the aggregation of two or more segments that do not exhibit such interactions when considered separately. Second, we show that certain context effects that have been reported for choice problems can result from the aggregation of two (or more) segments that do not exhibit these effects when considered separately. Given these threats to the conclusions drawn from experimental results, we describe the conditions under which unobserved heterogeneity can be ruled out as an alternative explanation based on one or more of the following: a priori considerations, derived properties, diagnostic statistics, and the results of latent class modeling. When these tests cannot rule out explanations based on unobserved heterogeneity, this is a serious problem for theorists who assume implicitly that the same theoretical principle works equally for everyone, but for random error. The empirical data patterns revealed by our diagnostics can expose the weakness in the theory but not fix it. It remains for the researcher to do further work to understand the underlying constructs that drive heterogeneity effects and to revise theory accordingly. [ABSTRACT FROM AUTHOR]

Replication is complicated in psychological research because studies of a given psychological phenomenon can never be direct or exact replications of one another, and thus effect sizes vary from one study of the phenomenon to the next—an issue of clear importance for replication. Current large-scale replication projects represent an important step forward for assessing replicability, but provide only limited information because they have thus far been designed in a manner such that heterogeneity either cannot be assessed or is intended to be eliminated. Consequently, the nontrivial degree of heterogeneity found in these projects represents a lower bound on the true degree of heterogeneity. We recommend enriching large-scale replication projects going forward by embracing heterogeneity. We argue this is the key for assessing replicability: if effect sizes are sufficiently heterogeneous—even if the sign of the effect is consistent—the phenomenon in question does not seem particularly replicable and the theory underlying it seems poorly constructed and in need of enrichment. Uncovering why and revising theory in light of it will lead to improved theory that explains heterogeneity and increases replicability. Given this, large-scale replication projects can play an important role not only in assessing replicability but also in advancing theory. [ABSTRACT FROM AUTHOR]

Fisher randomization tests for Neyman's null hypothesis of no average treatment effect are considered in a finite-population setting associated with completely randomized experiments involving more than two treatments. The consequences of using the F statistic to conduct such a test are examined, and we argue that under treatment effect heterogeneity, use of the F statistic in the Fisher randomization test can severely inflate theType I error under Neyman's null hypothesis. We propose to use an alternative test statistic, derive its asymptotic distributions under Fisher's and Neyman's null hypotheses, and demonstrate its advantages through simulations. [ABSTRACT FROM AUTHOR]

For a cluster-robust t-statistic under cluster heterogeneity we establish that the cluster-robust t-statistic has a gaussian asymptotic null distribution and develop the effective number of clusters, which scales down the actual number of clusters, as a guide to the behavior of the test statistic. The implications for hypothesis testing in applied work are that the number of clusters, rather than the number of observations, should be reported as the sample size, and the effective number of clusters should be reported to guide inference. If the effective number of clusters is large, testing based on critical values from a normal distribution is appropriate. [ABSTRACT FROM AUTHOR]

Wildlife populations often exhibit unequal catchability between subgroups such as males and females. This heterogeneity of capture probabilities can bias both population size and sex ratio estimates. Several authors have suggested that this problem can be overcome by treating males and females as separate populations and calculating a population estimate for each of them. However, this suggestion has received little testing, and many researchers do not implement it. Therefore, we used two simulations to test the utility of this method. One simulated a closed population, while the other simulated an open population and used the robust design to calculate population sizes. We tested both simulations with multiple levels of heterogeneity, and we used a third simulation to test several methods for detecting heterogeneity of capture probabilities. We found that treating males and females as separate populations produced more accurate population and sex ratio estimates. The benefits of this method were particularly pronounced for sex ratio estimates. When males and females were included as a single population, the sex ratio estimates became inaccurate when even slight heterogeneity was present, but when males and females were treated separately, the estimates were accurate even when large biases were present. Nevertheless, treating males and females separately reduced precision, and this method may not be appropriate when capture and recapture rates are low. None of the methods for detecting heterogeneity were robust, and we do not recommend that researchers rely on them. Rather, we suggest separating populations by sex, age, or other subgroups whenever sample sizes permit. [ABSTRACT FROM AUTHOR]

Background: Confidence intervals for the between study variance are useful in random-effects meta-analyses because they quantify the uncertainty in the corresponding point estimates. Methods for calculating these confidence intervals have been developed that are based on inverting hypothesis tests using generalised heterogeneity statistics. Whilst, under the random effects model, these new methods furnish confidence intervals with the correct coverage, the resulting intervals are usually very wide, making them uninformative.Methods: We discuss a simple strategy for obtaining 95 % confidence intervals for the between-study variance with a markedly reduced width, whilst retaining the nominal coverage probability. Specifically, we consider the possibility of using methods based on generalised heterogeneity statistics with unequal tail probabilities, where the tail probability used to compute the upper bound is greater than 2.5 %. This idea is assessed using four real examples and a variety of simulation studies. Supporting analytical results are also obtained.Results: Our results provide evidence that using unequal tail probabilities can result in shorter 95 % confidence intervals for the between-study variance. We also show some further results for a real example that illustrates how shorter confidence intervals for the between-study variance can be useful when performing sensitivity analyses for the average effect, which is usually the parameter of primary interest.Conclusions: We conclude that using unequal tail probabilities when computing 95 % confidence intervals for the between-study variance, when using methods based on generalised heterogeneity statistics, can result in shorter confidence intervals. We suggest that those who find the case for using unequal tail probabilities convincing should use the '1-4 % split', where greater tail probability is allocated to the upper confidence bound. The 'width-optimal' interval that we present deserves further investigation. [ABSTRACT FROM AUTHOR]

Sixteen US states have begun to hold teacher preparation programs (TPPs) accountable for teacher quality, where quality is estimated by teacher value-added to student test scores. Yet it is not easy to identify TPPs whose teachers are substantially better or worse than average. True teacher quality differences between TPPs are small; estimated differences are not very reliable; and when many TPPs are compared, multiple comparisons increase the danger of misclassifying ordinary TPPs as good or bad. Using a large and diverse dataset from Texas, we evaluate statistical methods for estimating teacher quality differences between TPPs. The most convincing estimates come from a value-added model where confidence intervals are widened by the inclusion of teacher random effects (or teacher clustering in large TPPs) and further widened by the Bonferroni correction for multiple comparisons. Using these confidence intervals, it is rarely possible to tell which TPPs, if any, are better or worse than average. The potential benefits of TPP accountability may be too small to balance the risk that a proliferation of noisy TPP estimates will encourage arbitrary and ineffective policy actions. [ABSTRACT FROM AUTHOR]

In this paper, the performance of six types of techniques for comparisons of means is examined. These six emerge from the distinction between the method employed (hypothesis testing, model selection using information criteria, or Bayesian model selection) and the set of hypotheses that is investigated (a classical, exploration-based set of hypotheses containing equality constraints on the means, or a theory-based limited set of hypotheses with equality and/or order restrictions). A simulation study is conducted to examine the performance of these techniques. We demonstrate that, if one has specific, a priori specified hypotheses, confirmation (i.e., investigating theory-based hypotheses) has advantages over exploration (i.e., examining all possible equality-constrained hypotheses). Furthermore, examining reasonable order-restricted hypotheses has more power to detect the true effect/non-null hypothesis than evaluating only equality restrictions. Additionally, when investigating more than one theory-based hypothesis, model selection is preferred over hypothesis testing. Because of the first two results, we further examine the techniques that are able to evaluate order restrictions in a confirmatory fashion by examining their performance when the homogeneity of variance assumption is violated. Results show that the techniques are robust to heterogeneity when the sample sizes are equal. When the sample sizes are unequal, the performance is affected by heterogeneity. The size and direction of the deviations from the baseline, where there is no heterogeneity, depend on the effect size (of the means) and on the trend in the group variances with respect to the ordering of the group sizes. Importantly, the deviations are less pronounced when the group variances and sizes exhibit the same trend (e.g., are both increasing with group number). [ABSTRACT FROM AUTHOR]

In the analysis of prevention and intervention studies, it is often important to investigate whether treatment effects vary among subgroups of patients defined by individual characteristics. These 'subgroup analyses' can provide information about how best to use a new prevention or intervention program. However, subgroup analyses can be misleading if they test data-driven hypotheses, employ inappropriate statistical methods, or fail to account for multiple testing. These problems have led to a general suspicion of findings from subgroup analyses. This article discusses sound methods for conducting subgroup analyses to detect moderators. Multiple authors have argued that, to assess whether a treatment effect varies across subgroups defined by patient characteristics, analyses should be based on tests for interaction rather than treatment comparisons within the subgroups. We discuss the concept of heterogeneity and its dependence on the metric used to describe treatment effects. We discuss issues of multiple comparisons related to subgroup analyses and the importance of considering multiplicity in the interpretation of results. We also discuss the types of questions that would lead to subgroup analyses and how different scientific goals may affect the study at the design stage. Finally, we discuss subgroup analyses based on post-baseline factors and the complexity associated with this type of subgroup analysis. [ABSTRACT FROM AUTHOR]

Abstract: The present research examined the impact of perceived ingroup identity heterogeneity on intergroup relations. We predicted that the effect of a heterogeneous national identity on perceived ingroup threat and prejudice toward immigrants would be moderated by strength of national identification. To test this hypothesis, we conducted two studies (N=230) in which we evaluated national identification and experimentally manipulated national identity heterogeneity (low versus high), and then assessed perceived ingroup threat and prejudice toward immigrants. Study 1 showed that perception of immigrants as a threat increased as national identification increased. Moreover, national identity heterogeneity increased the perceived threat when national identification was high, but not when it was low. Study 2 extended these findings to a measure of prejudice against immigrants from both similar and very different countries. We discuss our findings in terms of the impact heterogeneous social identities may have when there is a perceived threat to the clear and representative definition of the ingroup identity. [Copyright &y& Elsevier]

Merging data from multiple studies has been widely adopted in biomedical research. In this paper, we consider two major issues related to merging longitudinal datasets. We first develop a rigorous hypothesis testing procedure to assess the validity of data merging, and then propose a flexible joint estimation procedure that enables us to analyse merged data and to account for different within-subject correlations and follow-up schedules in different studies. We establish large sample properties for the proposed procedures. We compare our method with meta analysis and generalized estimating equations and show that our test provides robust control of Type I error against both misspecification of working correlation structures and heterogeneous dispersion parameters. Our joint estimating procedure leads to an improvement in estimation efficiency on all regression coefficients after data merging is validated. [ABSTRACT FROM PUBLISHER]

We examine the use of fixed-effects and random-effects moment-based meta-analytic methods for analysis of binary adverse-event data. Special attention is paid to the case of rare adverse events that are commonly encountered in routine practice. We study estimation of model parameters and between-study heterogeneity. In addition, we examine traditional approaches to hypothesis testing of the average treatment effect and detection of the heterogeneity of treatment effect across studies. We derive three new methods, a simple (unweighted) average treatment effect estimator, a new heterogeneity estimator, and a parametric bootstrapping test for heterogeneity. We then study the statistical properties of both the traditional and the new methods via simulation. We find that in general, moment-based estimators of combined treatment effects and heterogeneity are biased and the degree of bias is proportional to the rarity of the event under study. The new methods eliminate much, but not all, of this bias. The various estimators and hypothesis testing methods are then compared and contrasted using an example dataset on treatment of stable coronary artery disease. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a frequentistic validation framework for assessment — acceptance or rejection — of the consistency of a proposed parametrized partial differential equation model with respect to (noisy) experimental data from a physical system. Our method builds upon the Hotelling T 2 statistical hypothesis test for bias first introduced by Balci and Sargent in 1984 and subsequently extended by McFarland and Mahadevan (2008). Our approach introduces two new elements: a spectral representation of the misfit which reduces the dimensionality and variance of the underlying multivariate Gaussian but without introduction of the usual regression assumptions; a certified (verified) reduced basis approximation — reduced order model — which greatly accelerates computational performance but without any loss of rigor relative to the full (finite element) discretization. We illustrate our approach with examples from heat transfer and acoustics, both based on synthetic data. We demonstrate that we can efficiently identify possibility regions that characterize parameter uncertainty; furthermore, in the case that the possibility region is empty, we can deduce the presence of “unmodeled physics” such as cracks or heterogeneities. [Copyright &y& Elsevier]

Current methods of testing the equality of conditional correlations of bivariate data on a third variable of interest (covariate) are limited due to discretizing of the covariate when it is continuous. In this study, we propose a linear model approach for estimation and hypothesis testing of the Pearson correlation coefficient, where the correlation itself can be modeled as a function of continuous covariates. The restricted maximum likelihood method is applied for parameter estimation, and the corrected likelihood ratio test is performed for hypothesis testing. This approach allows for flexible and robust inference and prediction of the conditional correlations based on the linear model. Simulation studies show that the proposed method is statistically more powerful and more flexible in accommodating complex covariate patterns than the existing methods. In addition, we illustrate the approach by analyzing the correlation between the physical component summary and the mental component summary of the MOS SF-36 form across a fair number of covariates in the national survey data. [ABSTRACT FROM AUTHOR]

Individual differences in response to treatments have been a long-standing interest in education, psychology, and related fields. This article presents a conceptual framework and hierarchical modeling strategies that may help identify the subgroups for whom, or the conditions under which, a particular treatment is associated with better outcomes. The framework discussed in this article shows how differences in residual dispersion between treatment and control group members can signal omitted individual characteristics that may interact with treatments (Bryk & Raudenbush, 1988) and sensitizes us to individual- and cluster-level confounders of inferences concerning dispersion in quasi-experimental studies in multilevel settings. Based on the framework, hierarchical modeling strategies are developed to uncover interactions between treatments and individual characteristics, which are readily applicable to various settings in multisite evaluation studies. These strategies entail jointly modeling the mean and dispersion structures in hierarchical models. We illustrate the implementation of this framework through fully Bayesian analyses of the data from a study of the effectiveness of a reform-minded mathematics curriculum. [ABSTRACT FROM AUTHOR]

Background: Indirect comparisons are becoming increasingly popular for evaluating medical treatments that have not been compared head-to-head in randomized clinical trials (RCTs). While indirect methods have grown in popularity and acceptance, little is known about the fragility of confidence interval estimations and hypothesis testing relying on this method. Methods: We present the findings of a simulation study that examined the fragility of indirect confidence interval estimation and hypothesis testing relying on the adjusted indirect method. Findings: Our results suggest that, for the settings considered in this study, indirect confidence interval estimation suffers from under-coverage while indirect hypothesis testing suffers from low power in the presence of moderate to large between-study heterogeneity. In addition, the risk of overestimation is large when the indirect comparison of interest relies on just one trial for one of the two direct comparisons. Interpretation: Indirect comparisons typically suffer from low power. The risk of imprecision is increased when comparisons are unbalanced. [ABSTRACT FROM AUTHOR]

Background. The DSM-IV symptomatic criteria for major depression (MD) derive primarily from clinical experience with modest empirical support. Method. The sample studied included 1015 (518 males, 497 females) Caucasian twins from a population-based registry who met criteria for MD in the year prior to the interview. Logistic regression analyses were conducted to compare the associations of : (1) single symptomatic criterion, (2) two groups of criteria reflecting cognitive and neurovegetative symptoms, with a wide range of potential validators including demographic factors, risk for future episodes, risk of MD in the co-twin, characteristics of the depressive episode, the pattern of co-morbidity and personality traits. Results. The individual symptomatic criteria showed widely varying associations with the pattern of co-morbidity, personality traits, features of the depressive episode and demographic characteristics. When examined separately, these two criteria groups showed robust differences in their patterns of association, with the validators with the cognitive criteria generally producing stronger associations than the neurovegetative. Conclusions. Among depressed individuals, individual DSM-IV symptomatic criteria differ substantially in their predictive relationship with a range of clinical validators. These results challenge the equivalence assumption for the symptomatic criteria for MD and suggest a more than expected degree of ' covert ' heterogeneity among these criteria. Part of this heterogeneity is captured by the distinction between cognitive versus neurovegetative symptoms, with cognitive symptoms being more strongly associated with most clinically relevant characteristics. Detailed psychometric evaluation of DSM-IV criteria is overdue. [ABSTRACT FROM AUTHOR]

Aim: Published studies of patients treated with rosiglitazone or pioglitazone have reported greater reductions in HbA1c (A1C) than studies of patients treated with sitagliptin. However, studies of thiazolidinediones tended to enroll patients with higher baseline A1C levels. This meta-analysis investigates the relationship between baseline A1C and perceived efficacy of treatment. Methods: This report describes a Bayesian random effects analysis of 23 published studies. We constructed a random effects model including a factor adjusting for between-study differences in baseline A1C levels. Results: The random effects model correctly predicts post-treatment A1C levels from baseline A1C within a 95% confidence interval (CI) for each of the 23 studies included in the meta-analysis. After applying the model to adjust for differences in baseline A1C, we found that the difference in efficacy between rosiglitazone and sitagliptin was not significantly different from zero (0.12; 95% CI −0.09 to 0.34). Similarly, no significant differences are observed between the effects of pioglitazone and sitagliptin (0.01; 95% CI −0.21 to 0.22) or between rosiglitazone and pioglitazone (0.11; 95% CI −0.37 to 0.146). When baseline values are omitted from the Bayesian model, the findings suggest that rosiglitazone is superior to pioglitazone or sitagliptin. Conclusions: These results illustrate the necessity for careful application of appropriate methodology when comparing results of different studies. When between-study differences in treatment effects are adjusted for baseline differences, then the findings suggest that none of the treatments has an effect that is superior to any of the other treatments. [ABSTRACT FROM AUTHOR]

Previous studies of genetic structure in the European eel have resulted in seemingly conflicting results, ranging from no detectable heterogeneity to small but statistically significant differences and isolation by distance patterns among eels sampled across the continental range. Differences with respect to sampling design and choice of molecular markers, combined with a lack of power estimates, complicate comparisons of existing results. In this study we have used six microsatellite markers and, for the first time, compared maturing silver eels of known age from southern and northern Europe (Italy and Baltic Sea). In comparison with previous studies, our data may give a better representation of potential spawning stocks because eels were sampled when having begun their migration toward the presumed spawning area in the Sargasso Sea. Despite large sample sizes (404 and 806 individuals) we could not observe any signs of genetic differentiation (average FST=−0.00003, P=0.61), and a power analysis showed that the true level of heterogeneity (if existing) must be exceedingly small to have remained undetected (say, FST <0.0004). A tendency for slightly increased genetic differences between cohorts over time could be seen, but the amount of temporal change was minor and not statistically significant. Our findings reiterate the notion that previous reports of continental genetic differentiation in the European eel may be largely explained by uncontrolled temporal variation between juvenile glass eel samples.Heredity (2009) 103, 82–89; doi:10.1038/hdy.2009.51; published online 6 May 2009 [ABSTRACT FROM AUTHOR]

In the present paper, the results of investigation of the heterogeneous nature of coals are given. Microscopic observations of coals of different rank and mineral matter content were performed. The content of minerals and macerals and the size of their grains were determined in the coal samples. Two samples of lignite rich in minerals were analyzed in detail by measuring the ash content for each particle and batches containing numerous [∼100] coal particles. The additional analyses were performed with the particles before and after sink-float separation. The obtained results showed an increase of heterogeneity with mineral matter content. The statistical analysis showed that many particles should be used for an experiment for obtaining reliable data with a desired level of certainty. In the case of batch experiments with 5 g Kolubara B coal (about 100 particles, size 4.76-7.0 mm), the experiment should be repeated at least 7, 19, and 100 times in order to obtain a mean value in the confidence intervals ±5.0%, ±2.5%, and ±1.0%, respectively, for a 95% probability (if sample heterogeneity is the only cause for variations in results). However, regardless of increase of reproducibility and repeatability of obtained results, a large amount of samples and numerous repetitions of experiments cannot exclude differences in experimental results between homogenous and heterogeneous samples. [ABSTRACT FROM AUTHOR]

The true intra-individual change model is generalized by defining individual method effects. This allows the analysis of non-congeneric test-retest variables assumed to measure a common, possibly (temporally) transient, attribute. Temporal change in the attribute between different times of measurement is modelled by the true-change variable. Individual causal method effects, due to heterogeneity of the measurement methods, account for the imperfect correlation of the true-score variables at each time of measurement. The reliability of the composite scores, at each time of measurement, and the reliability of the difference composite score may be estimated with appropriate coefficients derived from the model. Measurements of daily life tension in adult females serve to illustrate how the model can be used empirically. [ABSTRACT FROM AUTHOR]

In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs. [ABSTRACT FROM AUTHOR]

Choice of the appropriate model in meta-analysis is often treated as an empirical question which is answered by examining the amount of variability in the effect sizes. When all of the observed variability in the effect sizes can be accounted for based on sampling error alone, a set of effect sizes is said to be homogeneous and a fixed-effects model is typically adopted. Whether a set of effect sizes is homogeneous or not is usually tested with the so-called Q test. In this paper, a variety of alternative homogeneity tests — the likelihood ratio. Wald and score tests — are compared with the Q test in terms of their Type I error rate and power for four different effect size measures. Monte Carlo simulations show that the Q test kept the tightest control of the Type I error rate, although the results emphasize the importance of large sample sizes within the set of studies. The results also suggest under what conditions the power of the tests can be considered adequate. [ABSTRACT FROM AUTHOR]

Abstract: We develop and test a model of initial play for symmetric normal-form games with multiple Nash equilibria. First, we specify an encompassing model that incorporates both equilibrium selection principles and boundedly rational behavioral models. We then design experimental games that can identify a variety of equilibrium selection principles. Model comparisons and hypothesis tests indicate that (1) boundedly rational behavior is prevalent in initial-period play, (2) homogeneous population models can be strongly rejected in favor of heterogeneous population models, and (3) deductive selection principles add no statistically significant contribution to explaining the data. [Copyright &y& Elsevier]

The article focuses on the suggested modeling of heterogeneous latent growth curve to determine the differences in the growth rate of mathematical cognition of children in the U.S. The method of modeling and prediction is usually used for analysis of individual academic development by way of covariance structure. Researchers used math achievement data of grade 7 to 10 as subject for a longitudinal research on the heterogeneity of the children's academic growth rate with reference to their initial status. The suggested model is an extension of the conventional growth curve model and results of the research reflected that the proposed model is more accurate than the conventional growth model in giving confidence intervals in determining future math achievement of children.

The most simple and commonly used approach for genetic associations is the case-control study design of unrelated people. This design is susceptible to population stratification. This problem is obviated in family-based studies, but it is usually difficult to accumulate large enough samples of well-characterized families. We addressed empirically whether the two designs give similar estimates of association in 93 investigations where both unrelated case-control and family-based designs had been employed. Estimated odds ratios differed beyond chance between the two designs in only four instances (4%). The summary relative odds ratio (ROR) (the ratio of odds ratios obtained from unrelated case-control and family-based studies) was close to unity (0.96 [95% confidence interval, 0.91-1.01]). There was no heterogeneity in the ROR across studies (amount of heterogeneity beyond chance I2 = 0%). Differences on whether results were nominally statistically significant (p < 0.05) or not with the two designs were common (opposite classification rates 14% and 17%); this reflected largely differences in power. Conclusions were largely similar in diverse subgroup analyses. Unrelated case-control and family-based designs give overall similar estimates of association. We cannot rule out rare large biases or common small biases. [ABSTRACT FROM AUTHOR]

We utilize mixture models and nonparametric maximum likelihood estimation to both develop a likelihood ratio test (lrt) for a common simplifying assumption and to allow heterogeneity within premarked cohort studies. Our methods allow estimation of the entire probability model and thus one can not only estimate many parameters of interest but one can also bootstrap from the estimated model to predict many things, including the standard deviations of estimators. Simulations suggest that our lrt has the appropriate protection for Type I error and often has good power. In practice, our lrt is important for determining the appropriateness of estimators and in examining if a simple design with only one capture period could be utilized for a future similar study. [ABSTRACT FROM AUTHOR]

In this paper we analyze transition data by means of the minimum-chi-square (MCS) method instead of the more commonly used maximum-likelihood (ML) method. The analysis includes exits to multiple destinations and unmeasured heterogeneity In the empirical application, turnover in the welfare system in Belgium is found to be very high Median duration is 4.5 months for men and 7 months for women, although these figures overstate turnover in that exits out of welfare include those occurring as a consequence of recipients moving to another municipality while remaining on welfare. [ABSTRACT FROM AUTHOR]

Different versions of the score test for neglected heterogeneity for a right censored exponential model are analyzed. These tests depend on how the information matrix is estimated. A test based on the theoretical information matrix is derived that is shown to outperform all the other tests. Further, the noncentrality parameter of the test is examined to show how the power of the test is reduced when data are censored. [ABSTRACT FROM AUTHOR]

In their advocacy of the rank-transformation (RT) technique for analysis of data from factorial designs, Mendeş and Yiğit ( Statistica Neerlandica, 67, 2013, 1-26) missed important analytical studies identifying the statistical shortcomings of the RT technique, the recommendation that the RT technique not be used, and important advances that have been made for properly analyzing data in a non-parametric setting. Applied data analysts are at risk of being misled by Mendeş and Yiğit, when statistically sound techniques are available for the proper non-parametric analysis of data from factorial designs. The appropriate methods express hypotheses in terms of normalized distribution functions, and the test statistics account for variance heterogeneity. [ABSTRACT FROM AUTHOR]

Three of Meehl’s major concerns are examined in this summary. They are the problem of null-hypothesis testing as a strategy, the central significance of heterogeneity in human behavior, and the over-dependence on rating scales with their inherent lack of precision. [Copyright &y& Elsevier]