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The increasing popularity of cognitive interventions for patients with psychosis calls for further exploration on how these interventions may benefit functional outcomes. We conducted a meta-analysis of randomized controlled trials (RCTs) to examine the effectiveness of cognitive interventions (i.e. Cognitive Remediation, Cognitive Training, Social Cognition, and their combination) on functioning of patients with recent onset psychosis, established as the period within the first five years from the first episode. The following databases were searched: Proquest, PUBMED/MEDLINE, PsycINFO, WOS, Scopus for research published until January 2022. In total, 12 studies were eligible. The total number of participants was 759, of which 32.2% in the intervention and 30.8% in the control group were female. We extracted data to calculate the standardized mean change from pre-test to post-test comparing the intervention with the control conditions. Overall, there was no effect of any of the cognitive intervention types on functioning. None of the examined factors (intervention type, length, and modality; control condition, follow-up time; cognitive functions; medication; symptoms) seemed to moderate these findings. Our results indicate that cognitive interventions as standalone interventions do not appear to improve functioning in patients with recent onset psychosis. Given the small number of eligible studies, further RCTs with larger and more refined samples are needed to test whether these interventions should be applied as single interventions with these patients.

Background Technology-based interventions (TBIs) are a useful approach when attempting to provide therapy to more patients with psychosis. Methods Randomized controlled trials of outcomes of TBIs v. face-to-face interventions in psychosis were identified in a systematic search conducted in PubMed/Ovid MEDLINE. Data were extracted independently by two researchers, and standardized mean changes were pooled using a three-level model and network meta-analysis. Results Fifty-eight studies were included. TBIs complementing treatment as usual (TAU) were generally superior to face-to-face interventions (g = 0.16, p ≤ 0.0001) and to specific outcomes, namely, neurocognition (g = 0.13, p ≤ 0.0001), functioning (g = 0.25, p = 0.006), and social cognition (g = 0.32, p ≤ 0.05). Based on the network meta-analysis, the effect of two TBIs differed significantly from zero; these were the TBIs cognitive training for the neurocognitive outcome [g = 0.16; 95% confidence interval (CI) 0.09–0.23] and cognitive behavioral therapy for quality of life (g = 1.27; 95% CI 0.46–2.08). The variables educational level, type of medication, frequency of the intervention, and contact during the intervention moderated the effectiveness of TBIs over face-to-face interventions in neurocognition and symptomatology. Conclusions TBIs are effective for the management of neurocognition, symptomatology, functioning, social cognition, and quality of life outcomes in patients with psychosis. The results of the network meta-analysis showed the efficacy of some TBIs for neurocognition, symptomatology, and quality of life. Therefore, TBIs should be considered a complement to TAU in patients with psychosis.

Network meta-analysis (NMA) allows the combination of evidence on the effectiveness of several interventions. NMA has mainly been applied in the medical science field, whereas in the domain of psychology and educational sciences its use is less frequent. Consequently, systematic reviews that describe the characteristics of published NMAs are limited to the field of medicine, and nothing is known about the characteristics of NMAs published in the psychology and educational sciences field. However, this information is still relevant for the design of future simulation studies and for detecting good and bad research practices. Thus, this study describes the features of the meta-analytic datasets of NMAs published in the field of psychology and educational sciences, as well as their methodological characteristics, and compares them to those observed in the medical domain. Results show that the number of studies included is larger in NMAs from psychology and educational sciences, the most commonly used effect size is the standardized mean difference (unlike the odds ratio in medicine), the sample size is smaller, more intervention groups are included, and inconsistent effects are observed more often. These results can be used in future simulation studies to generate realistic datasets. Finally, we warn about the poor quality of reporting of some technical aspects of the NMA, such as the statistical model used. ispartof: BEHAVIOR RESEARCH METHODS vol:55 issue:4 pages:2093-2108 ispartof: location:United States status: published

Single-case designs (SCDs) are used to evaluate the effects of interventions on individual participants. By repeatedly measuring participants under different conditions, SCD studies focus on individual effects rather than on group summaries. The main limitation of SCDs remains its generalisability to wider populations, reducing the relevance of their findings for practice and policy making. With this limitation in mind, methodological developments for synthesising SCD data from different studies that investigate the same research question have intensified in the past decades (e.g. multilevel modelling). However, these techniques are restricted to comparing two interventions at a time and can only incorporate evidence from studies that directly compare the two treatments of interest. These limitations could be addressed by using network meta-analysis that incorporates both direct and indirect evidence to simultaneously compare multiple interventions. Despite its potential, network meta-analytical techniques have yet to be applied to SCD data. Thus, in this paper, we argue that network meta-analysis can be a valuable tool to synthesise SCD data. We demonstrate the use of network meta-analysis in SCD data using a real dataset, and we conclude by reflecting on the challenges that SCD researchers might face when applying network meta-analysis methods to their data.

Some previous research works have reported potential benefits of screen media use in children aged under 6 years, and others have evidenced that screen media use might be particularly detrimental. To our knowledge, this is the first study to provide the first meta-analytic synthesis of existing research on the relation between overall screen time use and EFs. To address this issue, the current meta-analysis aimed to review the relations between overall screen time and EF in infants, toddlers and preschoolers. A systematic search was done on Web of Science and EBSCO to identify the eligible studies published until January 2023. Fifteen manuscripts with 6922 participants aged 0–6 years were included, and yielded 44 effect sizes. Three-level models were carried out, and the following study characteristics were tested as potential moderators: mean age, percentage of females, EF type, and whether exposure was active or passive. There was no statistically significant association in the relation between overall time use and EF or in the chosen moderators. Nevertheless, the study highlights the need to consider other contextual-related and development-related factors to determine the overall screen time use effect on EF in children.

We investigate the relationship of morality and political orientation by focusing on the influential results showing that liberals and conservatives rely on different moral foundations. We conducted a comprehensive literature search from major databases and other sources for primary studies that used the Moral Foundations Questionnaire and a typical measure of political orientation, a political self-placement item. We used a predefined process for independent extraction of effect sizes by two authors and ran both study-level and individual-level analyses. With 89 samples, 605 effect sizes, and 33,804 independent participants, in addition to 192,870 participants from the widely used YourMorals.org website, the basic differences about conservatives and liberals are supported. Yet, heterogeneity is moderate, and the results may be less generalizable across samples and political cultures than previously thought. The effect sizes obtained from the YourMorals.org data appear inflated compared with independent samples, which is partly related to political interest and may be because of self-selection. The association of moral foundations to political orientation varies culturally (between regions and countries) and subculturally (between White and Black respondents and in response to political interest). The associations also differ depending on the choice of the social or economic dimension and its labeling, supporting both the bidimensional model of political orientation and the findings that the dimensions are often strongly correlated. Our findings have implications for interpreting published studies, as well as designing new ones where the political aspect of morality is relevant. The results are primarily limited by the validity of the measures and the homogeneity of the included studies in terms of sample origins. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Contains fulltext : 240018.pdf (Publisher’s version ) (Closed access) The associations between physiological measures (i.e., heart rate and skin conductance) of autonomic nervous system (ANS) activity and severe antisocial spectrum behavior (AB) were meta-analyzed. We used an exhaustive partitioning of variables relevant to the ANS–AB association and investigated four highly relevant questions (on declining effect sizes, psychopathy subscales, moderators, and ANS measures) that are thought to be transformative for future research on AB. We investigated a broad spectrum of physiological measures (e.g., heart rate (variability), pre-ejection period) in relation to AB. The search date for the current meta-analysis was on January 1st, 2020, includes 101 studies and 769 effect sizes. Results indicate that effect sizes are heterogeneous and bidirectional. The careful partitioning of variables sheds light on the complex associations that were obscured in previous meta-analyses. Effects are largest for the most violent offenders and for psychopathy and are dependent on the experimental tasks used, parameters calculated, and analyses run. Understanding the specificity of physiological reactions may be expedient for differentiating between (and within) types of AB. 30 p.

In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as amodel with a randomstudy effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models and cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of multilevel meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified randomeffects models are not often used although theymight account better for the meta-analytic data structure of the analyzed datasets. Also, we found that the simulation studies done on multilevel metaanalysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed, and further suggestions are given. ispartof: Behavior Research Methods vol:52 issue:5 pages:2031-2052 ispartof: location:United States status: published

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a multivariate multilevel model ignoring covariance at higher levels, and a multivariate multilevel model modeling the existing covariance) to deal with the dependent effect sizes. The results indicate better estimates of the overall treatment effects and variance components when a multivariate multilevel model is applied, independent of modeling or ignoring the existing covariance. These findings confirm the robustness of multilevel modeling to misspecifying the existing covariance at the case and study level in terms of estimating the overall treatment effects and variance components. The results also show that the overall treatment effect estimates are unbiased regardless of the underlying model, but the between-case and between-study variance components are biased in certain conditions. In addition, the between-study variance estimates are particularly biased when the number of studies is smaller than 40 (i.e., 10 or 20) and the true value of the between-case variance is relatively large (i.e., 8). The observed bias is larger for the between-case variance estimates compared to the between-study variance estimates when the true between-case variance is relatively small (i.e., 0.5). ispartof: BEHAVIOR RESEARCH METHODS vol:52 issue:5 pages:2008-2019 ispartof: location:United States status: published

In science in general and in the context of single-case experimental designs, replication of theeffects of the intervention within and/or across participants or experiments is crucial for esta-blishing causality and for assessing the generality of the intervention effect. Specific develop-ments and proposals for assessing whether an effect has been replicated or not (or to whatextent) are scarce, in the general context of behavioral sciences, and practically null in the single-case experimental designs context. We propose an extension of the modified Brinley plot forassessing how many of the effects replicate. To make this assessment possible, a definition ofreplication is suggested, on the basis of expert judgment, rather than on statistical criteria. Thedefinition of replication and its graphical representation are justified, presenting their strengthsand limitations, and illustrated with real data. A user-friendly software is made available forobtaining automatically the graphical representation. ispartof: Journal Of Applied Behavior Analysis vol:55 issue:3 pages:997-1024 ispartof: location:United States status: Published online

To conduct a multilevel meta-analysis of multiple single-case experimental design (SCED) studies, the individual participant data (IPD) can be analyzed in one or two stages. In the one-stage approach, a multilevel model is estimated based on the raw data. In the two-stage approach, an effect size is calculated for each participant and these effect sizes and their sampling variances are subsequently combined to estimate a meta-analytic multilevel model. The multilevel model in the two-stage approach has fewer parameters to estimate, in exchange for the reduction of information of the raw data to effect sizes. In this paper we explore how the one-stage and two-stage IPD approaches can be applied in the context of meta-analysis of single-case designs. Both approaches are compared for several single-case designs of increasing complexity. Through a simulation study we show that the two-stage approach obtains better convergence rates for more complex models, but that model estimation does not necessarily converge at a faster speed. The point estimates of the fixed effects are unbiased for both approaches across all models, as such confirming results from methodological research on IPD meta-analysis of group-comparison designs. In light of these results, we discuss the implementation of both methods in R. ispartof: MULTIVARIATE BEHAVIORAL RESEARCH vol:57 issue:2-3 ispartof: location:United States status: published

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size. However, researchers are sometimes interested in obtaining separate effect size estimates for different types of outcomes. A recent simulation study (Park & Beretvas, 2019) has compared the performance of the three-level approach and the RVE method in estimating outcome-specific effects when several effect sizes are reported for different types of outcomes within studies. The goal of this paper is to extend that study by incorporating additional simulation conditions and by exploring the performance of additional models, such as the multivariate model, a three-level model that specifies different study-effects for each type of outcome, a three-level model that specifies a common study-effect for all outcomes, and separate three-level models for each type of outcome. Additionally, we also tested whether the a posteriori application of the RV correction improves the standard error estimates and the 95% confidence intervals. Results show that the application of separate three-level models for each type of outcome is the only approach that consistently gives adequate standard error estimates. Also, the a posteriori application of the RV correction results in correct 95% confidence intervals in all models, even if they are misspecified, meaning that Type I error rate is adequate when the RV correction is implemented. ispartof: BEHAVIOR RESEARCH METHODS vol:53 issue:2 pages:702-717 ispartof: location:United States status: published

BACKGROUND: Methodological rigor is a fundamental factor in the validity and credibility of the results of a meta-analysis. AIM: Following an increasing interest in single-case experimental design (SCED) meta-analyses, the current study investigates the methodological quality of SCED meta-analyses. METHODS AND PROCEDURES: We assessed the methodological quality of 178 SCED meta-analyses published between 1985 and 2015 through the modified Revised-Assessment of Multiple Systematic Reviews (R-AMSTAR) checklist. OUTCOMES AND RESULTS: The main finding of the current review is that the methodological quality of the SCED meta-analyses has increased over time, but is still low according to the R-AMSTAR checklist. A remarkable percentage of the studies (93.80% of the included SCED meta-analyses) did not even reach the midpoint score (22, on a scale of 0-44). The mean and median methodological quality scores were 15.57 and 16, respectively. Relatively high scores were observed for "providing the characteristics of the included studies" and "doing comprehensive literature search". The key areas of deficiency were "reporting an assessment of the likelihood of publication bias" and "using the methods appropriately to combine the findings of studies". CONCLUSIONS AND IMPLICATIONS: Although the results of the current review reveal that the methodological quality of the SCED meta-analyses has increased over time, still more efforts are needed to improve their methodological quality. ispartof: Res Dev Disabil ispartof: location:United States

The purpose of this study is to evaluate whether conformity with a traditional female role is related to the presence of anxiety symptoms in a sample of women (N = 234). To do so, symptoms of anxiety were evaluated through the Inventory of Situations and Responses of Anxiety (ISRA; Miguel-Tobal, & Cano-Vindel, 2002) and compliance with feminine gender roles was measured through the Inventory of Conformity to Feminine Norms (CFNI; Mahalik et al., 2005). Our results showed that higher levels of Cognitive Anxiety in women are related to higher compliance with the Thinness, Sexual Fidelity and Investment in Appearance dimensions of the CFNI. Also, a significant relationship was found between Total Anxiety and the Thinness dimension of the CFNI. These results suggest that some variables related to gender are related to anxiety responses, and that this relationship could occur through different interactions among cognitive variables.

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a multivariate multilevel model ignoring covariance at higher levels, and a multivariate multilevel model modeling the existing covariance) to deal with the dependent effect sizes. The results indicate better estimates of the overall treatment effects and variance components when a multivariate multilevel model is applied, independent of modeling or ignoring the existing covariance. These findings confirm the robustness of multilevel modeling to misspecifying the existing covariance at the case and study level in terms of estimating the overall treatment effects and variance components. The results also show that the overall treatment effect estimates are unbiased regardless of the underlying model, but the between-case and between-study variance components are biased in certain conditions. In addition, the between-study variance estimates are particularly biased when the number of studies is smaller than 40 (i.e., 10 or 20) and the true value of the between-case variance is relatively large (i.e., 8). The observed bias is larger for the between-case variance estimates compared to the between-study variance estimates when the true between-case variance is relatively small (i.e., 0.5).

Background.The implications of cannabis use in the onset of early psychosis and the severity of psychotic symptoms have resulted in a proliferation of studies on this issue. However, few have examined the effects of cannabis use on the cognitive symptoms of psychosis (i.e., neurocognitive functioning) in patients with first-episode psychosis (FEP). This systematic review and meta-analysis aim to assess the neurocognitive functioning of cannabis users (CU) and nonusers (NU) with FEP.Methods.Of the 110 studies identified through the systematic review of 6 databases, 7 met the inclusion criteria, resulting in 14 independent samples and 78 effect sizes. The total sample included 304 CU with FEP and 369 NU with FEP. The moderator variables were age at first use, duration of use, percentage of males, and age.Results.Effect sizes were not significantly different from zero in any neurocognitive domain when users and NU were compared. Part of the variability in effect sizes was explained by the inclusion of the following moderator variables: (1) frequency of cannabis use (β= 0.013,F= 7.56,p= 0.017); (2) first-generation antipsychotics (β= 0.019,F= 34.46,p≤ 0.001); and (3) country where the study was carried out (β= 0.266,t= 2.06,p= 0.043).Conclusions.This meta-analysis indicates that cannabis use is not generally associated with neurocognitive functioning in patients with FEP. However, it highlights the deleterious effect of low doses of cannabis in some patients. It also stresses the importance of the type of antipsychotic prescription and cannabis dose as moderator variables in the neurocognitive functioning of CU with FEP.

This study attempts to clarify the effects of varying testing conditions on creative performance in divergent thinking and product-based tasks. Two meta-analyses, one for time limits (short vs. long) and one for instructions (standard vs. explicit), were conducted. Moderator analyses were performed to examine whether the effects of time limits and instructions differ by measurement method, domain of creative performance, gender, study’s country of origin, educational level, study quality, and scoring method of originality. It was also investigated whether the effects of time limits vary depending on whether long time condition is timed or untimed, and whether the effects of instructions vary under timed versus untimed conditions. A total of 57 effect sizes (12 studies) for time limits and 165 effect sizes (38 studies) for instructions were analyzed using a meta-analytic three-level model. The time limits meta-analysis showed that long time limits (vs. short time limits) significantly enhanced creative performance, with smaller effects in studies from the USA (vs. non-USA). Analyses on divergent thinking indicators showed that long time limits (vs. short time limits) yielded higher originality scores, with no significant differences for fluency or flexibility. Long time limits showed greater effects on fluency and flexibility in timed (vs. untimed) long conditions. The instructions meta-analysis revealed that explicit (vs. standard) instructions significantly enhanced creative performance. An examination of divergent thinking indicators showed that explicit (vs. standard) instructions resulted in higher originality scores, with no significant differences for fluency or flexibility. Explicit instructions demonstrated larger effects on originality in non-college (vs. college) subjects, as well as in untimed (vs. timed) conditions. ispartof: Psychology of Aesthetics, Creativity, and the Arts vol:14 issue:1 pages:1-24 status: Published online

The development of divergent thinking (DT) in school-age children and adolescents has received considerable attention in the educational psychology literature since the 1970s. A body of research has outlined the existence of slumps (i.e., temporary declines) in this development with, however, conflicting findings concerning the magnitude and timing of these slumps. This study is the first to meta-analyze prior research findings regarding DT development from Grades 1 to 12, with a particular emphasis on the widely controversial fourth-grade slump. A total of 2139 standardized means from 41 studies involving 40,918 subjects were analyzed using a meta-analytic three-level model. The findings showed an overall upward developmental trend of DT across grade levels, with some discontinuities. Specifically, there was no evidence of a general fourth-grade slump; rather, evidences for a seventh-grade slump were found. Moderator analyses indicated that a fourth-grade slump might be observed depending on DT test, task content domain, intellectual giftedness, and country of study. The existence of the seventh-grade slump was also moderated by DT test, task content domain, and gender. Together, this study deciphers a longstanding debate regarding DT development, a prerequisite knowledge to support age-appropriate educational strategies that encourage creativity development. Implications of these findings for creativity research and practice are discussed.

Previous research found bias in the estimate of the overall fixed effects and variance components using multilevel meta-analyses of standardized single-case data. Therefore, we evaluate two...

© 2018 Psychonomic Society, Inc. It is common for the primary studies in meta-analyses to report multiple effect sizes, generating dependence among them. Hierarchical three-level models have been proposed as a means to deal with this dependency. Sometimes, however, dependency may be due to multiple random factors, and random factors are not necessarily nested, but rather may be crossed. For instance, effect sizes may belong to different studies, and, at the same time, effect sizes might represent the effects on different outcomes. Cross-classified random-effects models (CCREMs) can be used to model this nonhierarchical dependent structure. In this article, we explore by means of a simulation study the performance of CCREMs in comparison with the use of other meta-analytic models and estimation procedures, including the use of three- and two-level models and robust variance estimation. We also evaluated the performance of CCREMs when the underlying data were generated using a multivariate model. The results indicated that, whereas the quality of fixed-effect estimates is unaffected by any misspecification in the model, the standard error estimates of the mean effect size and of the moderator variables’ effects, as well as the variance component estimates, are biased under some conditions. Applying CCREMs led to unbiased fixed-effect and variance component estimates, outperforming the other models. Even when a CCREM was not used to generate the data, applying the CCREM yielded sound parameter estimates and inferences. ispartof: Behavior Research Methods vol:51 issue:3 pages:1286-1304 ispartof: location:United States status: published

© 2019, © 2019 Taylor & Francis Group, LLC. This study explores the performance of classical methods for detecting publication bias—namely, Egger’s regression test, Funnel Plot test, Begg’s Rank Correlation and Trim and Fill method—in meta-analysis of studies that report multiple effects. Publication bias, outcome reporting bias, and a combination of these were generated. Egger’s regression test and the Funnel Plot test were extended to three-level models, and possible cutoffs for the estimator of the Trim and Fill method were explored. Furthermore, we checked whether the combination of results of several methods yielded a better control of Type I error rates. Results show that no method works well across all conditions and that performance depends mainly on the population effect size value and the total variance. ispartof: JOURNAL OF EXPERIMENTAL EDUCATION vol:89 issue:1 pages:125-144 status: published

Background Although the use of technological advances in psychiatric treatment is relatively recent, the availability and sophistication of technologies are growing, research on psychological interventions for patients with psychosis has incorporated the use of various electronic applications, social networks, and other similar tools to provide new methods/routes of communication between therapists and patients. This new therapeutic approach may provide patients with personalized, flexible, and evidence-based interventions in their communities and even in their own homes. The aim of this study is reviewing the scientific literature about the use of online intervention for patients with psychosis to show the current state of online intervention and asses the effectiveness of these treatments. Methods The review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We have identified articles from Pubmed and Medline databases. We included all randomized controlled trials designed to test the efficacy of the online intervention or web-based intervention to patients with psychosis. We excluded observational trials, non-randomised trial and protocol study. Studies with RCT design and the participants must not have more than 5 to 10 years of evolution since the diagnosis, and the papers have been published between 2010 and 2019, in English language. The researchers independently assessed the articles and the discrepancies were resolved by consensus. Results We presented data about all randomized controlled trials of online interventions/ e-Health interventions/ App-mobile based and/or computer based intervention programs than were specifically designed for people with first episode psychosis or psychotic symptoms that had measured changes in functioning, quality of life or positive/negative symptoms. As unique treatment or as a complementary treatment with other psychological or psychopharmacology intervention. Discussion Online psychological interventions can play an important role in the well-being of people with schizophrenia. The strengths and limitations of our systematic review and meta-analysis will be discussed.

The synthesis of standardized regression coefficients is still a controversial issue in the field of meta-analysis. The difficulty lies in the fact that the standardized regression coefficients belonging to regression models that include different sets of covariates do not represent the same parameter, and thus their direct combination is meaningless. In the present study, a new approach called concealed correlations meta-analysis is proposed that allows for using the common information that standardized regression coefficients from different regression models contain to improve the precision of a combined focal standardized regression coefficient estimate. The performance of this new approach was compared with that of two other approaches: (1) carrying out separate meta-analyses for standardized regression coefficients from studies that used the same regression model, and (2) performing a meta-regression on the focal standardized regression coefficients while including an indicator variable as a moderator indicating the regression model to which each standardized regression coefficient belongs. The comparison was done through a simulation study. The results showed that, as expected, the proposed approach led to more accurate estimates of the combined standardized regression coefficients under both random- and fixed-effect models. ispartof: Behavior Research Methods vol:51 issue:1 pages:316-331 ispartof: location:United States status: published

When (meta-)analyzing single-case experimental design (SCED) studies by means of hierarchical or multilevel modeling, applied researchers almost exclusively rely on the linear mixed model (LMM). This type of model assumes that the residuals are normally distributed. However, very often SCED studies consider outcomes of a discrete rather than a continuous nature, like counts, percentages or rates. In those cases the normality assumption does not hold. The LMM can be extended into a generalized linear mixed model (GLMM), which can account for the discrete nature of SCED count data. In this simulation study, we look at the effects of misspecifying an LMM for SCED count data simulated according to a GLMM. We compare the performance of a misspecified LMM and of a GLMM in terms of goodness of fit, fixed effect parameter recovery, type I error rate, and power. Because the LMM and the GLMM do not estimate identical fixed effects, we provide a transformation to compare the fixed effect parameter recovery. The results show that, compared to the GLMM, the LMM has worse performance in terms of goodness of fit and power. Performance in terms of fixed effect parameter recovery is equally good for both models, and in terms of type I error rate the LMM performs better than the GLMM. Finally, we provide some guidelines for applied researchers about aspects to consider when using an LMM for analyzing SCED count data. ispartof: Behavior Research Methods vol:51 issue:6 pages:2477-2497 ispartof: location:United States status: published

© 2017, Springer Science+Business Media Dordrecht. Previous research on children’s and adolescents’ well-being at school has been focused on the possible determinants. However, no previous research has analysed children’s and adolescents’ lay-beliefs or conceptualizations of happiness at school. In the present work, we studied children’s (N = 104, 9–10-year-olds) and adolescents’ (N = 113, 15–16-year-olds) conceptualizations of happiness at school and its link with self-reported happiness (assessed 3 months later) and academic achievement (assessed 7 months later). For both samples, seven conceptualizations emerged: happiness as ‘being with friends’, ‘being praised’, ‘getting good grades’, ‘learning’, ‘leisure’, ‘enjoyment’, and ‘helping’. Age differences appeared for the conceptualizations of ‘being friends’ and ‘helping’, as children mentioned significantly more the former and adolescents the latter. No gender differences emerged. For adolescents, the conceptualizations of happiness at school as ‘being with friends’, ‘being praised’, ‘helping’, and not ‘having leisure time’ were positively related to self-reported happiness, which was positively related to academic achievement. For children, none of the conceptualizations were positively related to self-reported happiness. The conceptualization of happiness as ‘learning’ was positively related to academic achievement. The results are discussed in regards to their implications for children’s and adolescents’ well-being at school. ispartof: JOURNAL OF HAPPINESS STUDIES vol:19 issue:6 pages:1811-1830 status: published

Hyperspectral remote sensing is increasingly being recognized as a powerful tool to map ecosystem properties and functions through time and space. However, general information on the accuracy of this technology to assess the vegetation's biophysical and -chemical trait composition, and on the variables which are mediating this accuracy, is often lacking so far. Here, we addressed this knowledge gap for grass- and shrubland ecosystems and applied novel three-level meta-analytical regression equations to 77 studies that validated hyperspectral remote sensing data with field observations. Our results showed that the accuracy of hyperspectral sensors is generally high, but strongly depends on the trait being studied (leaf area index: R² = 0.79 and nRMSE = 0.19, chlorophyll: R² = 0.77 and nRMSE = 0.21, carotenoids: R² = 0.80 and nRMSE = 0.29, phosphorus: R² = 0.75 and nRMSE = 0.14, nitrogen: R² = 0.74 and nRMSE = 0.09, water: R² = 0.69 and nRMSE = 0.13, and lignin content: R² = 0.64 and nRMSE = 0.26). Moreover, they indicated that the use of multivariate signal processing techniques could improve these estimation accuracies (adjusted p

Background Patients with first episode psychosis (FEP) frequently report cannabis use although its effects on cognitive functioning are still unclear. Several studies suggest a decrease in the executive function, verbal memory and working memory of FEP cannabis users (González-Pinto et al., 2016; Mata et al. 2008) while other studies show improvements in the neurocognitive function of this group (Setién-Suero et al., 2017, Cuhna et al., 2013, Leeson et al., 2012, Yücel et al., 2012; Rodríguez-Sánchez et al., 2010) or even absence of neurocognitive differences between FEP cannabis users and non-users (Burgra et al., 2013). This meta-analysis aims to explore the magnitude of effect of cannabis use on neurocognition in patients with FEP. Methods Articles for consideration were identified through extensive literature searches using online databases, which included PubMed, Medline and PsychInfo. The search was limited to English language articles. The used keywords were: “first episode psychosis” OR, “neurocognition and cannabis”, in combination with a number of neuropsychology-related terms including “neurocog*” and “neuropsycholog*”. Given that other substances including alcohol, cocaine, and stimulants are associated with altered cognitive performance, studies in which participants met for poly-substance use disorders, even if there was preferential use towards cannabis, were excluded. Eight studies from 2008 to 2017 met inclusion criteria from a total sample of 16 initial studies. Five hundred and eighteen of these participants were cannabis users with FEP, and 639 were patients with no cannabis use. A total of 58 effect sizes of neuropsychological test variables were categorized into 4 cognitive domains (premorbid IQ, executive functioning, working memory and verbal memory and learning). Age of first cannabis use, duration of cannabis use, percentage of males and age were abstracted and assembled as moderator variables. Standardized mean differences were computed for each cognitive domain between cannabis-using patients and patients with no history of cannabis use. Negative effect sizes would display better cognitive functioning of non-cannabis users. We employed a meta-analytic three level model to combine effect sizes across studies. Results Effect sizes were not significantly different from zero in any of the neurocognitive domains when FEP cannabis users and non-users patients were compared [working memory (d= -0.03, SE=0.15, CI = -0.33–0.26, p=0.83), executive function (d= 0,14, SE=0.16, CI = -0.17–0.45, p=0.37), verbal memory and learning (d= 0.04, SE=0.15, CI = -0.25–0.33, p=0.27) and premorbid IQ (d= 0.06, SE=0.09, CI = -0.24–0.12, p=0.50)]. Only one moderator variable resulted significant in the executive function denoting superior performance in FEP cannabis-using patients as they were older. Discussion Cannabis use is not related to an ameliorated or improved neurocognitive functioning in patients with a first episode psychosis. This is consistent with previous studies which showed absence of differences in the neurocognitive functioning between FEP cannabis users and nonusers (Burgra et al., 2013). However, it has been demonstrated that continued cannabis intake worsens cognitive performance although some of the FEP patients had better premorbid capacities (González-Pinto, 2016). Moreover, the doses and the different types of cannabis preparations may interfere the present results. Meta-analysis on longitudinal studies which include these potential moderator variables may be performed in the future.

IntroductionReproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. Scientific claims should not gain credence because of the status or authority of their originator but by the replicability of their supporting evidence. Even research of exemplary quality may have irreproducible empirical findings because of random or systematic error.RationaleThere is concern about the rate and predictors of reproducibility, but limited evidence. Potentially problematic practices include selective reporting, selective analysis, and insufficient specification of the conditions necessary or sufficient to obtain the results. Direct replication is the attempt to recreate the conditions believed sufficient for obtaining a previously observed finding and is the means of establishing reproducibility of a finding with new data. We conducted a large-scale, collaborative effort to obtain an initial estimate of the reproducibility of psychological science.ResultsWe conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. There is no single standard for evaluating replication success. Here, we evaluated reproducibility using significance and P values, effect sizes, subjective assessments of replication teams, and meta-analysis of effect sizes. The mean effect size (r) of the replication effects (Mr = 0.197, SD = 0.257) was half the magnitude of the mean effect size of the original effects (Mr = 0.403, SD = 0.188), representing a substantial decline. Ninety-seven percent of original studies had significant results (P < .05). Thirty-six percent of replications had significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.ConclusionNo single indicator sufficiently describes replication success, and the five indicators examined here are not the only ways to evaluate reproducibility. Nonetheless, collectively these results offer a clear conclusion: A large portion of replications produced weaker evidence for the original findings despite using materials provided by the original authors, review in advance for methodological fidelity, and high statistical power to detect the original effect sizes. Moreover, correlational evidence is consistent with the conclusion that variation in the strength of initial evidence (such as original P value) was more predictive of replication success than variation in the characteristics of the teams conducting the research (such as experience and expertise). The latter factors certainly can influence replication success, but they did not appear to do so here. Reproducibility is not well understood because the incentives for individual scientists prioritize novelty over replication. Innovation is the engine of discovery and is vital for a productive, effective scientific enterprise. However, innovative ideas become old news fast. Journal reviewers and editors may dismiss a new test of a published idea as unoriginal. The claim that “we already know this” belies the uncertainty of scientific evidence. Innovation points out paths that are possible; replication points out paths that are likely; progress relies on both. Replication can increase certainty when findings are reproduced and promote innovation when they are not. This project provides accumulating evidence for many findings in psychological research and suggests that there is still more work to do to verify whether we know what we think we know.