Top-down attention control does not imply voluntary attention control for all individuals

Top-down information is known to play an important role in the control of visual attention. Often, evidence for top-down attention control is also interpreted as evidence for voluntary attention control. However, this latter theoretical interpretation is not warranted because volition is typically defined in terms of a conscious feeling that prior intentions led to a subsequent action, but this aspect of performance has not been assessed in previous studies. Accordingly, the present study used the construct of "agency" within the context of the spatial cuing paradigm to examine the relation between top-down and voluntary attention control. The results of two experiments consistently showed using growth-curve modeling that standard manipulations of top-down information in the spatial cuing paradigm do not have the same effect on all participants. In particular, the present findings showed that a slight majority of individuals (~60%) exhibited the expected pattern in which they reported feeling more agency when they performed visual search with the aid of an informative (arrow or onset) cue than when they performed this task with an uninformative cue or without any cue at all. However, more importantly, these findings also showed that a substantial number of individuals (~40%) exhibited the opposite pattern in which they reported feeling more agency when they performed visual search with an uninformative cue or without any cue at all. We conclude that the relation between top-down and voluntary attention control is not straightforward and must be studied using methods that are sensitive to individual differences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Optical and mechanical design of a telescope for lunar spectral irradiance measurements from a high-altitude aircraft

We have designed a non-imaging telescope for measurement of the spectral irradiance of the moon. The telescope was designed to be integrated into a wing pod of a National Aeronautics and Space Administration ER-2 research aircraft to measure lunar spectral irradiance during flight. The telescope and support system were successfully flown in August 2018 at altitudes near 21 km and at speeds of ∼760 km/h. The wing pod in which the telescope is mounted has an opening through which the moon can be observed. The mount exposes the telescope to high winds, low pressures, temperatures near -60 °C, and vibrations both due to flight and due to the motion of the aircraft on the ground. This required a telescope design with high thermal stability and high resistance to shock. The optical design of the telescope is optimized to have high throughput and spatially uniform transmission from 380 nm to 1000 nm over a field of view about three times the angular size of the moon as viewed from the Earth. The final design resulted in a telescope with singlet design incorporating a 139.7 mm lens with an effective focal length of 377 mm and a field of view of 1.6°. The light from the telescope is introduced into an integrating sphere, which destroys the image and the polarization for measurement by a fiber-coupled spectroradiometer. Herein, we present an overview of the instrument and support system with emphasis on the telescope design.

Evaluating Supplemental Samples in Longitudinal Research: Replacement and Refreshment Approaches

Despite the wide application of longitudinal studies, they are often plagued by missing data and attrition. The majority of methodological approaches focus on participant retention or modern missing data analysis procedures. This paper, however, takes a new approach by examining how researchers may supplement the sample with additional participants. First, refreshment samples use the same selection criteria as the initial study. Second, replacement samples identify auxiliary variables that may help explain patterns of missingness and select new participants based on those characteristics. A simulation study compares these two strategies for a linear growth model with five measurement occasions. Overall, the results suggest that refreshment samples lead to less relative bias, greater relative efficiency, and more acceptable coverage rates than replacement samples or not supplementing the missing participants in any way. Refreshment samples also have high statistical power. The comparative strengths of the refreshment approach are further illustrated through a real data example. These findings have implications for assessing change over time when researching at-risk samples with high levels of permanent attrition.

Multiplicative treatment effects in randomized pretest-posttest experimental designs

In randomized pretest-posttest experimental designs, experimental treatments are commonly compared through an additive difference. The assumed additive effects of experimental treatments, in randomized pretest-posttest design, also correspond to additive differences between pre- and posttreatment measures. However, it is possible that experimental treatments differ in ratios and the treatment effects are multiplicative. Logarithmic-transformed ANOVA (LANOVA) and logarithmic-transformed ANCOVA (LANCOVA)-reparameterizations of log-log regression models-are proposed to test multiplicative effects given randomized pretest-posttest experimental designs. In addition, a new effect size measure is proposed for treatment effects that are multiplicative instead of additive. Model selection strategy, sample size planning, and power calculation for the proposed methods are also provided. Simulation studies were conducted to compare the Type I error rates and power of proposed methods to those of symmetrized change analysis, ANOVA, ANCOVA, gain score analysis, and ANCOVA with logarithmic-transformed dependent variable given population effect of both additive and multiplicative nature. An empirical data analysis follows to show the interpretational difference between multiplicative and additive effects. While logarithmic transformations are most often recommended to address skewness, our article shows how a log transformation can be used to reconceptualize the fundamental nature of the treatment effect. Finally, recommendations, limitations, and future directions are discussed. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

On Standardizing Within-Person Effects: Potential Problems of Global Standardization

Person-mean centering has been recommended for disaggregating between-person and within-person effects when modeling time-varying predictors. Multilevel modeling textbooks recommended global standardization for standardizing fixed effects. An aim of this study is to evaluate whether and when person-mean centering followed by global standardization can accurately estimate fixed-effects within-person relations (the estimand of interest in this study) in multilevel modeling. We analytically derived that global standardization generally yields inconsistent (asymptotically biased) estimates for the estimand when between-person differences in within-person standard deviations exist and the average within-person relation is nonzero. Alternatively, a person-mean-SD standardization (P-S) approach yields consistent estimates. Our simulation results further revealed (1) how misleading the results from global standardization were under various circumstances and (2) the P-S approach had accurate estimates and satisfactory coverage rates of fixed-effects within-person relations when the number of occasions is 30 or more (in many conditions, performance was satisfactory with 10 or 20 occasions). A daily diary data example, focused on emotional complexity, was used to empirically illustrate the approaches. Researchers should choose standardization approaches based on theoretical considerations and should clearly describe the purpose and procedure of standardization in research articles.

Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty

The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.

Addressing the "Replication Crisis": Using Original Studies to Design Replication Studies with Appropriate Statistical Power

Psychology is undergoing a replication crisis. The discussion surrounding this crisis has centered on mistrust of previous findings. Researchers planning replication studies often use the original study sample effect size as the basis for sample size planning. However, this strategy ignores uncertainty and publication bias in estimated effect sizes, resulting in overly optimistic calculations. A psychologist who intends to obtain power of .80 in the replication study, and performs calculations accordingly, may have an actual power lower than .80. We performed simulations to reveal the magnitude of the difference between actual and intended power based on common sample size planning strategies and assessed the performance of methods that aim to correct for effect size uncertainty and/or bias. Our results imply that even if original studies reflect actual phenomena and were conducted in the absence of questionable research practices, popular approaches to designing replication studies may result in a low success rate, especially if the original study is underpowered. Methods correcting for bias and/or uncertainty generally had higher actual power, but were not a panacea for an underpowered original study. Thus, it becomes imperative that 1) original studies are adequately powered and 2) replication studies are designed with methods that are more likely to yield the intended level of power.

Anova of Residuals and Ancova: Correcting an Illusion by Using Model Comparisons and Graphs

Analysis of covariance is often conceptualized as an analysis of variance of a single set of residual scores that are obtained by regressing the dependent variable on the covariate. Although this conceptualization of an equivalence between the two procedures may be intuitively appealing, it is mathematically incorrect. If residuals are obtained from the pooled within-groups regression coefficient ( bw), an analysis of variance on the residuals results in an inflated α-level. If the regression coefficient for the total sample combined into one group ( bT) is used, ANOVA on the residuals yields an inappropriately conservative test. In either case, analysis of variance of residuals fails to provide a correct test, because the significance test in analysis of covariance requires consideration of both bw and bT, unlike analysis of residuals. It is recommended that the significance test of treatment effects in analysis of covariance be conceptualized, not as an analysis of residuals, but as a comparison of models whose parameters are estimated by the principle of least squares. Focusing on model comparisons and their associated graphs can be used effectively here as in other cases to teach simply and correctly the logic of the statistical test.

On disaggregating between-person and within-person effects with longitudinal data using multilevel models

This article extends current discussion of how to disaggregate between-person and within-person effects with longitudinal data using multilevel models. Our main focus is on the 2 issues of centering and detrending. Conceptual and analytical work demonstrates the similarities and differences among 3 centering approaches (no centering, grand-mean centering, and person-mean centering) and the relations and differences among various detrending approaches (no detrending, detrending X only, detrending Y only, and detrending both X and Y). Two real data analysis examples in psychology are provided to illustrate the differences in the results of using different centering and detrending methods for the disaggregation of between- and within-person effects. Simulation studies were conducted to further compare the various centering and detrending approaches under a wider span of conditions. Recommendations of how to perform centering, whether detrending is needed or not, and how to perform detrending if needed are made and discussed.

Is psychology suffering from a replication crisis? What does "failure to replicate" really mean?

Psychology has recently been viewed as facing a replication crisis because efforts to replicate past study findings frequently do not show the same result. Often, the first study showed a statistically significant result but the replication does not. Questions then arise about whether the first study results were false positives, and whether the replication study correctly indicates that there is truly no effect after all. This article suggests these so-called failures to replicate may not be failures at all, but rather are the result of low statistical power in single replication studies, and the result of failure to appreciate the need for multiple replications in order to have enough power to identify true effects. We provide examples of these power problems and suggest some solutions using Bayesian statistics and meta-analysis. Although the need for multiple replication studies may frustrate those who would prefer quick answers to psychology's alleged crisis, the large sample sizes typically needed to provide firm evidence will almost always require concerted efforts from multiple investigators. As a result, it remains to be seen how many of the recently claimed failures to replicate will be supported or instead may turn out to be artifacts of inadequate sample sizes and single study replications.

A Monte Carlo Comparison of Seven ε-Adjustment Procedures in Repeated Measures Designs With Small Sample Sizes

The purpose of this study was to evaluate seven univariate procedures for testing omnibus null hypotheses for data gathered from repeated measures designs. Five alternate approaches are compared to the two more traditional adjustment procedures (Geisser and Greenhouse’s ε̂ and Huynh and Feldt’s ε̃), neither of which may be entirely adequate when sample sizes are small and the number of levels of the repeated factors is large. Empirical Type I error rates and power levels were obtained by simulation for conditions where small samples occur in combination with many levels of the repeated factor. Results suggested that alternate univariate approaches were improvements to the traditional approaches. One alternate approach in particular was found to be most effective in controlling Type I error rates without unduly sacrificing power.

Pairwise Multiple Comparisons in Repeated Measures Designs

Five methods of performing pairwise multiple comparisons in repeated measures designs were investigated. Tukey's Wholly Significant Difference (WSD) test, recommended by most experimental design texts, requires that all differences between pairs of means have a common variance. However, this assumption is equivalent to the sphericity condition that is necessary and sufficient for the validity of the mixed-model approach to the omnibus test. Monte Carlo methods revealed that Tukey's WSD leads to an inflated alpha level when the sphericity assumption is not met. Consideration of both Type I and Type II error rates found in the simulated conditions for the five procedures suggests that a Bonferroni method utilizing a separate error term for each comparison should be employed.

Interpretation of Interpersonal Interactions

This study explored the relationship between scores on the UCLA Loneliness Scale and perceptions and interpretations of interpersonal interactions. It compared 20 elderly females and 20 young females on the dimensions of loneliness, chronic loneliness, and negativeness of interpretation. No significant age differences were found, but a highly significant relationship was obtained among loneliness, chronic loneliness, and negative interpretations of interpersonal interactions. When the time elapsed since the occurrence of loneliness was taken into consideration, the correlation became even stronger. The relationship between loneliness scores and negative interpretations held true for interactions with a family member, neighbor, and authority figure. An inverse relationship was obtained between education and loneliness and between education and negativeness of interpretation. A developmental perspective of friendship and intimacy was proposed to explain severe or chronic loneliness.

Implications of Covariance Structure Analysis (LISREL) Versus Regression Models for Counseling Research

The advent of new types of multivariate techniques, such as covariance structureanalysis, has the potential for reshaping research on counseling processes.Covariance structure analysis is more theory driven and confirmatory in naturethan regression analysis. Following a description of its strengths and weaknesses, adetailed example is provided showing how the use of covariance structure analysiscan improve research sophistication and theory development in counselingpsychology.

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record

There's more than one way to conduct a replication study: Beyond statistical significance

As the field of psychology struggles to trust published findings, replication research has begun to become more of a priority to both scientists and journals. With this increasing emphasis placed on reproducibility, it is essential that replication studies be capable of advancing the field. However, we argue that many researchers have been only narrowly interpreting the meaning of replication, with studies being designed with a simple statistically significant or nonsignificant results framework in mind. Although this interpretation may be desirable in some cases, we develop a variety of additional "replication goals" that researchers could consider when planning studies. Even if researchers are aware of these goals, we show that they are rarely used in practice-as results are typically analyzed in a manner only appropriate to a simple significance test. We discuss each goal conceptually, explain appropriate analysis procedures, and provide 1 or more examples to illustrate these analyses in practice. We hope that these various goals will allow researchers to develop a more nuanced understanding of replication that can be flexible enough to answer the various questions that researchers might seek to understand.

Bias in longitudinal data analysis with missing data using typical linear mixed-effects modelling and pattern-mixture approach: an analytical illustration

We analytically derive the fixed-effects estimates in unconditional linear growth curve models by typical linear mixed-effects modelling (TLME) and by a pattern-mixture (PM) approach with random-slope-dependent two-missing-pattern missing not at random (MNAR) longitudinal data. Results showed that when the missingness mechanism is random-slope-dependent MNAR, TLME estimates of both the mean intercept and mean slope are biased because of incorrect weights used in the estimation. More specifically, the estimate of the mean slope is biased towards the mean slope for completers, whereas the estimate of the mean intercept is biased towards the opposite direction as compared to the estimate of the mean slope. We also discuss why the PM approach can provide unbiased fixed-effects estimates for random-coefficients-dependent MNAR data but does not work well for missing at random or outcome-dependent MNAR data. A small simulation study was conducted to illustrate the results and to compare results from TLME and PM. Results from an empirical data analysis showed that the conceptual finding can be generalized to other real conditions even when some assumptions for the analytical derivation cannot be met. Implications from the analytical and empirical results were discussed and sensitivity analysis was suggested for longitudinal data analysis with missing data.

Multiheterodyne spectroscopy with optical frequency combs generated from a continuous-wave laser

Dual-drive Mach-Zehnder modulators were utilized to produce power-leveled optical frequency combs (OFCs) from a continuous-wave laser. The resulting OFCs contained up to 50 unique frequency components and spanned more than 200 GHz. Simple changes to the modulation frequency allowed for agile control of the comb spacing. These OFCs were then utilized for broadband, multiheterodyne measurements of CO2 using both a multipass cell and an optical cavity. This technique allows for robust measurements of trace gas species and alleviates much of the cost and complexity associated with the use of femtosecond OFCs produced with mode-locked pulsed lasers.

A Comparison of the Cross-Sectional and Sequential Designs when Assessing Longitudinal Mediation

Mediational studies are often of interest in psychology because they explore the underlying relationship between 2 constructs. Previous research has shown that cross-sectional designs are prone to biased estimates of longitudinal mediation parameters. The sequential design has become a popular alternative to the cross-sectional design for assessing mediation. This design is a compromise between the cross-sectional and longitudinal designs because it incorporates time in the model but has only 1 measurement each of X, M, and Y. As such, this design follows the recommendation of the MacArthur group approach, which stresses the importance of multiple waves of data for studying mediation. These 2 designs were compared to see whether the sequential design assesses longitudinal mediation more accurately than the cross-sectional design. Specifically, analytic expressions are derived for the bias of estimated direct and indirect effects as calculated from the sequential design when the actual mediational process follows a longitudinal autoregressive model. It was found that, in general, the sequential design does not assess longitudinal mediation more accurately than the cross-sectional design. As a result, neither design can be depended on to assess longitudinal mediation accurately.

Bias in Cross-Sectional Analyses of Longitudinal Mediation: Partial and Complete Mediation Under an Autoregressive Model

Maxwell and Cole (2007) showed that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters in the special case of complete mediation. However, their results did not apply to the more typical case of partial mediation. We extend their previous work by showing that substantial bias can also occur with partial mediation. In particular, cross-sectional analyses can imply the existence of a substantial indirect effect even when the true longitudinal indirect effect is zero. Thus, a variable that is found to be a strong mediator in a cross-sectional analysis may not be a mediator at all in a longitudinal analysis. In addition, we show that very different combinations of longitudinal parameter values can lead to essentially identical cross-sectional correlations, raising serious questions about the interpretability of cross-sectional mediation data. More generally, researchers are encouraged to consider a wide variety of possible mediation models beyond simple cross-sectional models, including but not restricted to autoregressive models of change.

Introduction to the special section on Campbell's and Rubin's conceptualizations of causality

Causality plays a fundamental role in scientific explanation. This introduction describes 2 target articles and 3 commentaries on 2 influential perspectives on causal inference, one developed by Donald Campbell and the other developed by Donald Rubin. One goal of this special section is to introduce Rubin's causal model to psychologists who may be largely unfamiliar with it. Another goal is to compare Rubin's conceptualization with Campbell's perspective, to enrich readers' understanding of both views. All of the authors of this special section perceive many similarities between the 2 approaches. Even so, by comparing and contrasting the 2 perspectives, the authors also believe that it is possible to strengthen both approaches.

Statistical methods for risk-outcome research: being sensitive to longitudinal structure

The relation between risk and outcome consists of myriad, complex, longitudinal processes. To study these relations requires research designs and statistical methods that are sensitive to the longitudinal structure of the risk, the outcome, and the risk-outcome relation. This review presents four longitudinal characteristics that can complicate psychopathology risk-outcome research. We represent each complication with an example data set. We demonstrate how conventional statistical approaches can yield highly misleading results. Finally, we review alternative statistical approaches that can handle these complications quite well.

Number fluctuations and energy dissipation in sodium spinor condensates

We characterize fluctuations in atom number and spin populations in F=1 sodium spinor condensates. We find that the fluctuations enable a quantitative measure of energy dissipation in the condensate. The time evolution of the population fluctuations shows a maximum. We interpret this as evidence of a dissipation-driven separatrix crossing in phase space. For a given initial state, the critical time to the separatrix crossing is found to depend exponentially on the magnetic field and linearly on condensate density. This crossing is confirmed by tracking the energy of the spinor condensate as well as by Faraday rotation spectroscopy. We also introduce a phenomenological model that describes the observed dissipation with a single coefficient.

Do Research Literatures Give Correct Answers?

The rationale for insisting on properly designed studies is to ensure the construction of research literatures that are not influenced by biases of any sort. Once a literature is established, however, whatever answers it gives (usually via meta-analysis) are accepted as valid. The results of our favored statistical techniques are understood best when significant relationships are present while the meaning of null results are unclear. Thus, significant findings are more likely to be published. This file drawer problem has been known for many years. However, the effect that the file drawer has on research literatures has never been probed empirically. A new method is described that tests the impact a file drawer has on a research literature. The results of the file drawer on four separate psychological research literatures are presented.

What's Wrong with Research Literatures? And how to Make Them Right

Meta-analysis is now the accepted procedure for summarizing research literatures in areas of applied psychology. Because of the bias for publishing statistically significant findings, while usually rejecting nonsignificant results, our research literatures yield misleading answers to important quantitative questions (e.g., How much better is the average psychotherapy patient relative to a comparable group of untreated controls? How much more aggressive are children who watch a great deal of violent TV than children who watch little or no violence on TV?). While all such research literatures provide overly optimistic meta-analytic estimates, exactly how practically important are these overestimates? Three studies testing the literature on implementation intentions finds only slightly elevated effectiveness estimates. Conversely, in three studies another growing research literature (the efficacy of remote intercessory prayer) is found to be misleading and is in all likelihood not a real effect (i.e., our three studies suggest the literature likely consists of Type I errors). Rules of thumb to predict which research literatures are likely invalid are offered. Finally, revised publication and data analysis procedures to generate unbiased research literatures in the future are examined.

Quantum phase transitions and continuous observation of spinor dynamics in an antiferromagnetic condensate

Condensates of spin-1 sodium display rich spin dynamics due to the antiferromagnetic nature of the interactions in this system. We use Faraday rotation spectroscopy to make a continuous and minimally destructive measurement of the dynamics over multiple spin oscillations on a single evolving condensate. This method provides a sharp signature to locate a magnetically tuned separatrix in phase space which depends on the net magnetization. We also observe a phase transition from a two- to a three-component condensate at a low but finite temperature using a Stern-Gerlach imaging technique. This transition should be preserved as a zero-temperature quantum phase transition.

Bias in cross-sectional analyses of longitudinal mediation

Most empirical tests of mediation utilize cross-sectional data despite the fact that mediation consists of causal processes that unfold over time. The authors considered the possibility that longitudinal mediation might occur under either of two different models of change: (a) an autoregressive model or (b) a random effects model. For both models, the authors demonstrated that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters even under the ideal conditions when mediation is complete. In longitudinal models where variable M completely mediates the effect of X on Y, cross-sectional estimates of the direct effect of X on Y, the indirect effect of X on Y through M, and the proportion of the total effect mediated by M are often highly misleading.

High-flux beam source for cold, slow atoms or molecules

We demonstrate and characterize a high-flux beam source for cold, slow atoms or molecules. The desired species is vaporized using laser ablation, then cooled by thermalization in a cryogenic cell of buffer gas. The beam is formed by particles exiting a hole in the buffer gas cell. We characterize the properties of the beam (flux, forward velocity, temperature) for both an atom (Na) and a molecule (PbO) under varying buffer gas density, and discuss conditions for optimizing these beam parameters. Our source compares favorably to existing techniques of beam formation, for a variety of applications.

Assessment of a multiple-dose drug interaction between ezetimibe, a novel selective cholesterol absorption inhibitor and gemfibrozil

OBJECTIVE

Ezetimibe is a novel lipid-lowering drug that prevents intestinal absorption of dietary and biliary cholesterol leading to significant reduction in total-C, LDL-C, Apo B, and TG and increases in HDL-C in patients with hypercholesterolemia. Gemfibrozil, a fibric acid derivative, is an effective lipid-modulating agent that increases serum high-density lipoprotein cholesterol and decreases serum TG. The objective of this study was to evaluate the potential for a pharmacokinetic (PK) interaction between ezetimibe and gemfibrozil.

METHODS

This was a randomized, open-label, 3-way crossover, multiple-dose study in 12 healthy adult male volunteers. All subjects received the following 3 treatments orally for 7 days: ezetimibe 10 mg once daily, gemfibrozil 600 mg every 12 hours, and ezetimibe 10 mg once daily plus gemfibrozil 600 mg every 12 hours. A washout period of > or = 7 days separated the 3 treatments. In each treatment, blood samples were collected on day 7 to assess the steady-state PK of ezetimibe and gemfibrozil. The oral bioavailability of ezetimibe coadministered with gemfibrozil relative to each drug administered alone was evaluated with an analysis-of-variance model.

RESULTS

Ezetimibe was rapidly absorbed and extensively conjugated to its glucuronide metabolite. Ezetimibe did not alter the bioavailability (based on AUC) of gemfibrozil. The mean AUC0-12 of gemfibrozil was 74.7 and 74.1 microg h/ml with and without ezetimibe coadministration, respectively (log-transformed geometric mean ratio (GMR) = 99.2; 90% confidence interval (CI) = 92 - 107%). Conversely, gemfibrozil significantly (p < 0.05) increased the plasma concentrations of ezetimibe and total ezetimibe (i.e. ezetimibe plus ezetimibe-glucuronide). Exposure to ezetimibe and total ezetimibe was increased approximately 1.4-fold and 1.7-fold, respectively (CI = 109 - 173% for ezetimibe and 142 - 190% for total ezetimibe), however, this increase was not considered to be clinically relevant. Ezetimibe and gemfibrozil administered alone or concomitantly for 7 days was well tolerated.

CONCLUSIONS

The coadministration of ezetimibe and gemfibrozil in patients is unlikely to cause a clinically significant drug interaction. The coadministration of these agents is a promising approach for patients with mixed dyslipidemia. Additional clinical studies are warranted.

The Persistence of Underpowered Studies in Psychological Research: Causes, Consequences, and Remedies

Underpowered studies persist in the psychological literature. This article examines reasons for their persistence and the effects on efforts to create a cumulative science. The "curse of multiplicities" plays a central role in the presentation. Most psychologists realize that testing multiple hypotheses in a single study affects the Type I error rate, but corresponding implications for power have largely been ignored. The presence of multiple hypothesis tests leads to 3 different conceptualizations of power. Implications of these 3 conceptualizations are discussed from the perspective of the individual researcher and from the perspective of developing a coherent literature. Supplementing significance tests with effect size measures and confidence intervals is shown to address some but not necessarily all problems associated with multiple testing.

Sample size for multiple regression: obtaining regression coefficients that are accurate, not simply significant

An approach to sample size planning for multiple regression is presented that emphasizes accuracy in parameter estimation (AIPE). The AIPE approach yields precise estimates of population parameters by providing necessary sample sizes in order for the likely widths of confidence intervals to be sufficiently narrow. One AIPE method yields a sample size such that the expected width of the confidence interval around the standardized population regression coefficient is equal to the width specified. An enhanced formulation ensures, with some stipulated probability, that the width of the confidence interval will be no larger than the width specified. Issues involving standardized regression coefficients and random predictors are discussed, as are the philosophical differences between AIPE and the power analytic approaches to sample size planning.

Obtaining power or obtaining precision. Delineating methods of sample-size planning

Sample-size planning historically has been approached from a power analytic perspective in order to have some reasonable probability of correctly rejecting the null hypothesis. Another approach that is not as well-known is one that emphasizes accuracy in parameter estimation (AIPE). From the AIPE perspective, sample size is chosen such that the expected width of a confidence interval will be sufficiently narrow. The rationales of both approaches are delineated and two procedures are given for estimating the sample size from the AIPE perspective for a two-group mean comparison. One method yields the required sample size, such that the expected width of the computed confidence interval will be the value specified. A modification allows for a defined degree of probabilistic assurance that the width of the computed confidence interval will be no larger than specified. The authors emphasize that the correct conceptualization of sample-size planning depends on the research questions and particular goals of the study.

Analytic methods for questions pertaining to a randomized pretest, posttest, follow-up design

Delineates 5 questions regarding group differences that are likely to be of interest to researchers within the framework of a randomized pretest, posttest, follow-up (PPF) design. These 5 questions are examined from a methodological perspective by comparing and discussing analysis of variance (ANOVA) and analysis of covariance (ANCOVA) methods and briefly discussing hierarchical linear modeling (HLM) for these questions. This article demonstrates that the pretest should be utilized as a covariate in the model rather than as a level of the time factor or as part of the dependent variable within the analysis of group differences. It is also demonstrated that how the posttest and the follow-up are utilized in the analysis of group differences is determined by the specific question asked by the researcher.

Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling

R. M. Baron and D. A. Kenny (1986; see record 1987-13085-001) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data. Graduating from cross-sectional to longitudinal designs enables researchers to make more rigorous inferences about the causal relations implied by such models. In this transition, misconceptions and erroneous assumptions are the norm. First, we describe some of the questions that arise (and misconceptions that sometimes emerge) in longitudinal tests of mediational models. We also provide a collection of tips for structural equation modeling (SEM) of mediational processes. Finally, we suggest a series of 5 steps when using SEM to test mediational processes in longitudinal designs: testing the measurement model, testing for added components, testing for omitted paths, testing the stationarity assumption, and estimating the mediational effects.

Power in randomized group comparisons: the value of adding a single intermediate time point to a traditional pretest-posttest design

Adding a pretest as a covariate to a randomized posttest-only design increases statistical power, as does the addition of intermediate time points to a randomized pretest-posttest design. Although typically 5 waves of data are required in this instance to produce meaningful gains in power, a 3-wave intensive design allows the evaluation of the straight-line growth model and may reduce the effect of missing data. The authors identify the statistically most powerful method of data analysis in the 3-wave intensive design. If straight-line growth is assumed, the pretest-posttest slope must assume fairly extreme values for the intermediate time point to increase power beyond the standard analysis of covariance on the posttest with the pretest as covariate, ignoring the intermediate time point.

The development of multiple domains of child and adolescent self-concept: a cohort sequential longitudinal design

The development of child and adolescent self-concept was examined as a function of the self-concept domain, social/developmental/educational transitions, and gender. In two overlapping age cohorts of public school students (Ns = 936 and 984), five dimensions of self-concept were evaluated every 6 months in a manner that spanned grades 3 through 11 (representing the elementary, middle, and high school years). Domains of self-concept included academic competence, physical appearance, behavioral conduct, social acceptance, and sports competence. Structural equation modeling addressed questions about the stability of individual differences over time. Multilevel modeling addressed questions about mean-level changes in self-concept over time. Significant effects emerged with regard to gender, age, dimension of self-concept, and educational transition.

Sample size and multiple regression analysis

Despite the development of procedures for calculating sample size as a function of relevant effect size parameters, rules of thumb tend to persist in designs of multiple regression studies. One explanation for their persistence may be the difficulty in formulating a reasonable a priori value of an effect size to be detected. This article presents methods for calculating effect sizes in multiple regression from a variety of perspectives and also introduces a new method based on an exchangeability structure among predictor variables. No single method is deemed superior, but rather examples show that a combination of methods is likely to be most valuable in many situations. A simulation provides a 2nd explanation for why rules of thumb for choosing sample size have persisted but also shows that the outcome of such underpowered studies will be a literature consisting of seemingly contradictory results.

Prenatal maternal predictors of cognitive and emotional delays in children of adolescent mothers

The present study examined relationships among prenatal characteristics of 121 adolescent mothers-including cognitive readiness for parenting, intelligence, social support, and personal adjustment-and intellectual-linguistic development, social-emotional functioning, and adaptive behavior in their children at three years of age. Only 28% of the children scored within normal ranges on all three types of outcomes. Intellectual and linguistic delays were predicted best by prenatal measures of maternal Performance IQ and social support from extended family. Socioemotional problems were predicted best by maternal internalizing problems and social support from partner and friends. Adaptive behavior was associated with parenting style. Implications for the early identification of high-risk children--and associated intervention programs--are discussed.

The proof of the pudding: an illustration of the relative strengths of null hypothesis, meta-analysis, and Bayesian analysis

Some methodologists have recently suggested that scientific psychology's over-reliance on null hypothesis significance testing (NHST) impedes the progress of the discipline. In response, a number of defenders have maintained that NHST continues to play a vital role in psychological research. Both sides of the argument to date have been presented abstractly. The authors take a different approach to this issue by illustrating the use of NHST along with 2 possible alternatives (meta-analysis as a primary data analysis strategy and Bayesian approaches) in a series of 3 studies. Comparing and contrasting the approaches on actual data brings out the strengths and weaknesses of each approach. The exercise demonstrates that the approaches are not mutually exclusive but instead can be used to complement one another.

Structural differences in parent and child reports of children's symptoms of depression and anxiety

Two cohorts of public elementary school children and their parents (assessed 3 years apart) completed child and parent forms of the Children's Depression Inventory (CDI) and the Revised Children's Manifest Anxiety Scale (RCMAS). Assessments were conducted twice, once during the fall (N = 562) and again during the spring (N = 630) of the 6th grade. Factor analyses revealed 3 factors for each measure. Two of the 3 parent CDI factors manifested some degree of congruence with their counterparts from the child CDI. Similarly, 2 of the 3 RCMAS factors were somewhat congruent across informant types. Differences between parent and child factor structures suggest that parents' and children's reports focus on somewhat different aspects of child psychopathology, and they can make qualitatively different contributions to the multiaxial assessment of children.

Structural differences in parent and child reports of children's symptoms of depression and anxiety

Two cohorts of public elementary school children and their parents (assessed 3 years apart) completed child and parent forms of the Children's Depression Inventory (CDI) and the Revised Children's Manifest Anxiety Scale (RCMAS). Assessments were conducted twice, once during the fall (N = 562) and again during the spring (N = 630) of the 6th grade. Factor analyses revealed 3 factors for each measure. Two of the 3 parent CDI factors manifested some degree of congruence with their counterparts from the child CDI. Similarly, 2 of the 3 RCMAS factors were somewhat congruent across informant types. Differences between parent and child factor structures suggest that parents' and children's reports focus on somewhat different aspects of child psychopathology, and they can make qualitatively different contributions to the multiaxial assessment of children.