Actions taken by schools when tobacco policies are violated: associations with adolescent smoking prevalence

This study examined the association of adolescent smoking prevalence with actions taken by schools (i.e., informing parents or disciplining pupils at school) for violating the school tobacco policy. Interaction terms between actions taken by schools and role models' smoking behavior or support for adolescents were also examined. Data were collected using self-completed questionnaires from a nationally representative Norwegian sample of 15-year-olds (1,404 pupils, 51% males) and 73 staff members. Informing parents when the school tobacco policy was violated by contrast with other actions taken by schools was not associated with lower levels of adolescent smoking. Disciplining pupils was associated with lower levels of adolescent smoking but the association was no longer significant when examined together with parents' and teachers' smoking behavior or support. Exposure to parents' and teachers' smoking, and teachers' support were associated with adolescent smoking prevalence, irrespective of actions taken by schools. Identifying the mechanism under which these different home and school factors relate to adolescent smoking may be worthwhile to inform intervention initiatives.     

Interval estimates of weighted effect sizes in the one‐way heteroscedastic ANOVA

A framework for comparing normal population means in the presence of heteroscedasticity and outliers is provided. A single number called the weighted effect size summarizes the differences in population means after weighting each according to the difficulty of estimating their respective means, whether the difficulty is due to unknown population variances, unequal sample sizes or the presence of outliers. For an ANOVA weighted for unequal variances, we find interval estimates for the weighted effect size. In addition, the weighted effect size is shown to be a monotone function of a suitably defined weighted coefficient of determination, which means that interval estimates of the former are readily transformed into interval estimates of the latter. Extensive simulations demonstrate the accuracy of the nominal 95% coverage of these intervals for a wide range of parameters.     

Confidence intervals and sample size calculations for the weighted eta-squared effect sizes in one-way heteroscedastic ANOVA

Effect size reporting and interpreting practices have been extensively recommended in academic journals when primary outcomes of all empirical studies have been analyzed. This article presents an alternative approach to constructing confidence intervals of the weighted eta-squared effect size within the context of one-way heteroscedastic ANOVA models. It is shown that the proposed interval procedure has advantages over an existing method in its theoretical justification, computational simplicity, and numerical performance. For design planning, the corresponding sample size procedures for precise interval estimation of the weighted eta-squared association measure are also delineated. Specifically, the developed formulas compute the necessary sample sizes with respect to the considerations of expected confidence interval width and tolerance probability of interval width within a designated value. Supplementary computer programs are provided to aid the implementation of the suggested techniques in practical applications of ANOVA designs when the assumption of homogeneous variances is not tenable.     

On sample size calculation for 2×2 fixed‐effect ANOVA when variances are unknown and possibly unequal

The factorial 2 × 2 fixed‐effect ANOVA is a procedure used frequently in scientific research to test mean differences between‐subjects in all of the groups. But if the assumption of homogeneity is violated, the test for the row, column, and the interaction effect might be invalid or less powerful. Therefore, for planning research in the case of unknown and possibly unequal variances, it is worth developing a sample size formula to obtain the desired power. This article suggests a simple formula to determine the sample size for 2 × 2 fixed‐effect ANOVA for heterogeneous variances across groups. We use the approximate Welch t test and consider the variance ratio to derive the formula. The sample size determination requires two‐step iterations but the approximate sample sizes needed for the main effect and the interaction effect can be determined separately with the specified power. The present study also provides an example and a SAS program to facilitate the calculation process.     

Maximum likelihood estimation of item response parameters when some responses are omitted

A theoretical model is given for dealing with omitted responses. Two special cases are investigated.This work was supported in part by contract N00014-80-C-0402, project designation NR 150-453 between the Office of Naval Research and Educational Testing Service. Reproduction in whole or in part is permitted for any purpose of the United States Government.     

Maximum likelihood solution to factor analysis when some factors are completely specified

In the factor-analytic model, let some of the factors be known (i.e., the factor loadings are given in advance; they maye.g. be obtained from some previous analyses). However, their covariance matrix may, or may not, be known. The remaining factors (if any) are assumed to be uncorrelated among themselves and to the first set. For this model, the maximum likelihood equations are obtained and an iterative method for the solution is proposed.The work was done while the author was at Deutsches Rechenzentrum, Darmstadt, Germany.     

The mismatch effect: when testosterone and status are at odds

Why do some people strive for high status, whereas others actively avoid it? In the present studies, the authors examined the psychological and physiological consequences of a mismatch between baseline testosterone and a person's current level of status. The authors tested this mismatch effect by placing high and low testosterone individuals into high or low status positions using a rigged competition. In Study 1, low testosterone participants reported greater emotional arousal, focused more on their status, and showed worse cognitive functioning in a high status position. High testosterone participants showed this pattern in a low status position. In Study 2, the emotional arousal findings were replicated with heart rate, and the cognitive findings were replicated using a math test. In Study 3, the authors demonstrate that testosterone is a better predictor of behavior than self-report measures of the need for dominance. Discussion focuses on the value of measuring hormones in personality and social psychology.     

Not all effect sizes are the same: Comments on Holden (2008)

Despite the common belief that response bias is a significant moderator of psychological tests in field settings, these biases have been notoriously difficult to identify. Holden (2008) has recently presented evidence suggesting this paradox may at least in part be explained by problems inherent to the use of moderated regression with self-report indicators of response bias. His article offers an innovative proposal for understanding a central issue in applied test use. However, the conclusions drawn about both moderated regression and the general validity of response bias indicators are open to alternative explanations. It would be premature to assume these factors are important contributors to the ephemeral character of response bias effects.     

Estimating the mean effect size in meta-analysis: Bias,precision, and mean squared error of different weighting methods

Although use of the standardized mean difference in meta-analysis is appealing for several reasons, there are some drawbacks. In this article, we focus on the following problem: that a precision-weighted mean of the observed effect sizes results in a biased estimate of the mean standardized mean difference. This bias is due to the fact that the weight given to an observed effect size depends on this observed effect size. In order to eliminate the bias, Hedges and Olkin (1985) proposed using the mean effect size estimate to calculate the weights. In the article, we propose a third alternative for calculating the weights: using empirical Bayes estimates of the effect sizes. In a simulation study, these three approaches are compared. The mean squared error (MSE) is used as the criterion by which to evaluate the resulting estimates of the mean effect size. For a meta-analytic dataset with a small number of studies, theMSE is usually smallest when the ordinary procedure is used, whereas for a moderate or large number of studies, the procedures yielding the best results are the empirical Bayes procedure and the procedure of Hedges and Olkin, respectively.     

Interpreting effect sizes when controlling for stability effects in longitudinal autoregressive models: Implications for psychological science

Effect sizes in longitudinal studies often are dramatically smaller than effect sizes in cross-sectional studies. Indeed, autoregressive models (which are often used in longitudinal studies but not in cross-sectional studies) control for past levels on the outcome (i.e., stability effects) in order to predict change in levels of the outcome over time and thus may greatly reduce the magnitude of the effect of a predictor on the outcome. Unfortunately, however, there have been no attempts to differentiate guidelines for interpreting effect sizes for longitudinal studies versus cross-sectional studies. Consequently, longitudinal effect sizes that fall below the universal guidelines for “small” may be incorrectly dismissed as trivial, when they might be meaningful. In the current paper, we first review the present guidelines for interpreting effect sizes. Next, we discuss several examples of how controlling for stability effects can dramatically attenuate effect sizes of other predictors, in order to support our argument that the current guidelines may be misleading for interpreting longitudinal effects. Finally, we conclude by making recommendations for researchers regarding the interpretation of effect sizes in longitudinal autoregressive models.     

Three-level meta-analysis of dependent effect sizes

Although dependence in effect sizes is ubiquitous, commonly used meta-analytic methods assume independent effect sizes. We describe and illustrate three-level extensions of a mixed effects meta-analytic model that accounts for various sources of dependence within and across studies, because multilevel extensions of meta-analytic models still are not well known. We also present a three-level model for the common case where, within studies, multiple effect sizes are calculated using the same sample. Whereas this approach is relatively simple and does not require imputing values for the unknown sampling covariances, it has hardly been used, and its performance has not been empirically investigated. Therefore, we set up a simulation study, showing that also in this situation, a three-level approach yields valid results: Estimates of the treatment effects and the corresponding standard errors are unbiased.     

Latent Growth Curve and Repeated Measures ANOVA Contrasts: What the Models are Telling You

The relationship between the latent growth curve and repeated measures ANOVA models is often misunderstood. Although a number of investigators have looked into the similarities and differences among these models, a cursory reading of the literature can give the impression that they are very different models. Here we show that each model represents a set of contrasts on the occasion means. We demonstrate that the fixed effects parameters of the estimated basis vector latent growth curve model are merely a transformation of the repeated measures ANOVA fixed effects parameters. We further show that differences in fit in models that estimate the same means structure can be due to the different error covariance structures implied by the model. We show these relationships both algebraically and through using data from a simulation.     

Misunderstandings and omissions in textbook accounts of effect sizes

There have been frequent attempts in psychology to reduce the reliance on null hypothesis significance testing (NHST) as the criterion for establishing the importance of results. Many authorities now recommend the reporting of effect sizes (ESs) as a supplement or alternative to NHST. However, there is extensive specialist literature highlighting problems associated with the use and interpretation of ESs. A review of the coverage of ESs in over 100 textbooks on statistical analysis in behavioural science revealed widespread neglect of ESs and the relevant critical issues that have widespread coverage in the more specialist literature. For example, many textbooks claim that ESs should be interpreted as a simple measure of the practical real-world importance of a result despite the fact that ESs are profoundly influenced by features of design and analysis strategy. We seek to highlight areas of misunderstanding about ESs found in the pedagogical literature in the light of the more specialist literature and make recommendations to researchers for the appropriate use and interpretation of ESs. This is critical as statistics textbooks have a crucial role in the education of researchers.     

The categorical and the hypothetical: a critique of some fundamental assumptions of standard semantics

The hypothetical notion of consequence is normally understood as the transmission of a categorical notion from premisses to conclusion. In model-theoretic semantics this categorical notion is ??truth??, in standard proof-theoretic semantics it is ??canonical provability??. Three underlying dogmas, (I) the priority of the categorical over the hypothetical, (II) the transmission view of consequence, and (III) the identification of consequence and correctness of inference are criticized from an alternative view of proof-theoretic semantics. It is argued that consequence is a basic semantical concept which is directly governed by elementary reasoning principles such as definitional closure and definitional reflection, and not reduced to a categorical concept. This understanding of consequence allows in particular to deal with non-wellfounded phenomena as they arise from circular definitions.     

Testing attitude-behaviour theories using non-experimental data: An examination of some hidden assumptions

This chapter presents a detailed causal analysis of the two leading theories of attitude-behaviour relations, the theory of reasoned action (TRA) and the theory of planned behaviour (TPB). It is noted that the direct path from perceived behavioural control to behaviour in the TPB is causally ambiguous. Focusing on the attitude-intention relationship, timeline and path diagrams are used to illustrate some of the hidden assumptions that underlie the way these theories are usually tested using standard multiple regression applied to nonexperimental data. Randomised experiments are recommended as the best solution to the serious problems arising from omitted causes.     

The TAR effect: when the ones who dislike become the ones who are disliked

Four studies tested whether a source's evaluations of other individuals can recursively transfer to the source, such that people who like others acquire a positive valence, whereas people who dislike others acquire a negative valence (Transfer of Attitudes Recursively; TAR). Experiment 1 provides first evidence for TAR effects, showing recursive transfers of evaluations regardless of whether participants did or did not have prior knowledge about the (dis)liking source. Experiment 2 shows that previously but not subsequently acquired knowledge about targets that were (dis)liked by a source overrode TAR effects in a manner consistent with cognitive balance. Finally, Experiments 3 and 4 demonstrate that TAR effects are mediated by higher order propositional inferences (in contrast to lower order associative processes), in that TAR effects on implicit attitude measures were fully mediated by TAR effects on explicit attitude measures. Commonalities and differences between the TAR effect and previously established phenomena are discussed.