Participant characteristics predicting communication outcomes in AAC implementation for individuals with ASD and IDD: a systematic review and meta-analysis.

This meta-analysis examined communication outcomes in single-case design studies of augmentative and alternative communication (AAC) interventions and their relationship to participant characteristics. Variables addressed included chronological age, pre-intervention communication mode, productive repertoire, and pre-intervention imitation skills. Investigators identified 114 single-case design studies that implemented AAC interventions with school-aged individuals with autism spectrum disorder and/or intellectual disability. Two complementary effect size indices, Tau(AB) and the log response ratio, were applied to synthesize findings. Both indices showed positive effects on average, but also exhibited a high degree of heterogeneity. Moderator analyses detected few differences in effectiveness when comparing across diagnoses, age, the number and type of communication modes, participant's productive repertoires, and imitation skills to intervention. A PRISMA-compliant abstract is available: https://bit.ly/30BzbLv.

Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models.

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the "metafor" and "clubSandwich" packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

The Relative Effects of Instruction Linking Word Reading and Word Meaning Compared to Word Reading Instruction Alone on the Accuracy, Fluency, and Word Meaning Knowledge of 4th-5th Grade Students With Dyslexia.

This within-subjects experimental study investigated the relative effects of word reading and word meaning instruction (WR+WM) compared to word-reading instruction alone (WR) on the accuracy, fluency, and word meaning knowledge of 4th-5th graders with dyslexia. We matched word lists on syllables, phonemes, frequency, number of definitions, and concreteness. We assigned half the words to WR and half to WR+WM. Word reading accuracy, word reading fluency, and word meaning knowledge were measured at pretest, immediately following each intervention session, and at posttest, administered immediately following the 12, 45-minute, daily instructional sessions. Compared to WR instruction alone, WR+WM significantly improved accuracy (d = 0.65), fluency (d = 0.43), and word meaning knowledge (d = 1.92) immediately following intervention, and significantly improved accuracy (d = 0.74), fluency (d = 0.84), and word meaning knowledge (d = 1.03) at posttest. Findings support the premise that word meaning knowledge facilitates accurate and fluent word reading, and that instruction explicitly integrating word reading and word meaning may be an effective support for upper elementary students with dyslexia.

A systematic review and meta-analysis of effects of psychosocial interventions on spiritual well-being in adults with cancer.

Objective: Spiritual well-being (SpWb) is an important dimension of health-related quality of life for many cancer patients. Accordingly, an increasing number of psychosocial intervention studies have included SpWb as a study endpoint, and may improve SpWb even if not designed explicitly to do so. This meta-analysis of randomized controlled trials (RCTs) evaluated effects of psychosocial interventions on SpWb in adults with cancer and tested potential moderators of intervention effects. Methods: Six literature databases were systematically searched to identify RCTs of psychosocial interventions in which SpWb was an outcome. Doctoral-level rater pairs extracted data using Covidence following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Standard meta-analytic techniques were applied, including meta-regression with robust variance estimation and risk-of-bias sensitivity analysis. Results: Forty-one RCTs were identified, encompassing 88 treatment effects among 3883 survivors. Interventions were associated with significant improvements in SpWb (g = 0.22, 95% CI [0.14, 0.29], p < 0.0001). Studies assessing the FACIT-Sp demonstrated larger effect sizes than did those using other measures of SpWb (g = 0.25, 95% CI [0.17, 0.34], vs. g = 0.10, 95% CI [-0.02, 0.23], p = 0.03]. No other intervention, clinical, or demographic characteristics significantly moderated effect size. Conclusions: Psychosocial interventions are associated with small-to-medium-sized effects on SpWb among cancer survivors. Future research should focus on conceptually coherent interventions explicitly targeting SpWb and evaluate interventions in samples that are diverse with respect to race and ethnicity, sex and cancer type.

Effects of psychosocial interventions on meaning and purpose in adults with cancer: A systematic review and meta-analysis.

Meaning and purpose in life are associated with the mental and physical health of patients with cancer and survivors and also constitute highly valued outcomes in themselves. Because meaning and purpose are often threatened by a cancer diagnosis and treatment, interventions have been developed to promote meaning and purpose. The present meta-analysis of randomized controlled trials (RCTs) evaluated effects of psychosocial interventions on meaning/purpose in adults with cancer and tested potential moderators of intervention effects. Six literature databases were systematically searched to identify RCTs of psychosocial interventions in which meaning or purpose was an outcome. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, rater pairs extracted and evaluated data for quality. Findings were synthesized across studies with standard meta-analytic methods, including meta-regression with robust variance estimation and risk-of-bias sensitivity analysis. Twenty-nine RCTs were identified, and they encompassed 82 treatment effects among 2305 patients/survivors. Psychosocial interventions were associated with significant improvements in meaning/purpose (g = 0.37; 95% CI, 0.22-0.52; P < .0001). Interventions designed to enhance meaning/purpose (g = 0.42; 95% CI, 0.24-0.60) demonstrated significantly higher effect sizes than those targeting other primary outcomes (g = 0.18; 95% CI, 0.09-0.27; P = .009). Few other intervention, clinical, or demographic characteristics tested were significant moderators. In conclusion, the results suggest that psychosocial interventions are associated with small to medium effects in enhancing meaning/purpose among patients with cancer, and the benefits are comparable to those of interventions designed to reduce depression, pain, and fatigue in patients with cancer. Methodological concerns include small samples and ambiguity regarding allocation concealment. Future research should focus on explicitly meaning-centered interventions and identify optimal treatment or survivorship phases for implementation.

Interventions to enhance self-efficacy in cancer patients: A meta-analysis of randomized controlled trials.

OBJECTIVE: Self-efficacy expectations are associated with improvements in problematic outcomes widely considered clinically significant (ie, emotional distress, fatigue, and pain), related to positive health behaviors, and as a type of personal agency, inherently valuable. Self-efficacy expectancies, estimates of confidence to execute behaviors, are important in that changes in self-efficacy expectations are positively related to future behaviors that promote health and well-being. The current meta-analysis investigated the impact of psychological interventions on self-efficacy expectations for a variety of health behaviors among cancer patients. METHODS: Ovid Medline, PsycINFO, CINAHL, EMBASE, Cochrane Library, and Web of Science were searched with specific search terms for identifying randomized controlled trials (RCTs) that focused on psychologically based interventions. Included studies had (a) an adult cancer sample, (b) a self-efficacy expectation measure of specific behaviors, and (c) an RCT design. Standard screening and reliability procedures were used for selecting and coding studies. Coding included theoretically informed moderator variables. RESULTS: Across 79 RCTs, 223 effect sizes, and 8678 participants, the weighted average effect of self-efficacy expectations was estimated as g = 0.274 (P < .001). Consistent with the self-efficacy theory, the average effect for in-person intervention delivery (g = 0.329) was significantly greater than for all other formats (g = 0.154, P = .023; eg, audiovisual, print, telephone, and Web/internet). CONCLUSIONS: The results establish the impact of psychological interventions on self-efficacy expectations as comparable in effect size with commonly reported outcomes (distress, fatigue, pain). Additionally, the result that in-person interventions achieved the largest effect is supported by the social learning theory and could inform research related to the development and evaluation of interventions.

A Gradual Effects Model for Single-Case Designs.

Single-case designs are a class of repeated measures experiments used to evaluate the effects of interventions for small or specialized populations, such as individuals with low-incidence disabilities. There has been growing interest in systematic reviews and syntheses of evidence from single-case designs, but there remains a need to further develop appropriate statistical models and effect sizes for data from the designs. We propose a novel model for single-case data that exhibit nonlinear time trends created by an intervention that produces gradual effects, which build up and dissipate over time. The model expresses a structural relationship between a pattern of treatment assignment and an outcome variable, making it appropriate for both treatment reversal and multiple baseline designs. It is formulated as a generalized linear model so that it can be applied to outcomes measured as frequency counts or proportions, both of which are commonly used in single-case research, while providing readily interpretable effect size estimates such as log response ratios or log odds ratios. We demonstrate the gradual effects model by applying it to data from a single-case study and examine the performance of proposed estimation methods in a Monte Carlo simulation of frequency count data.

A meta-analytic approach to examining the correlation between religion/spirituality and mental health in cancer.

Religion and spirituality (R/S) are patient-centered factors and often are resources for managing the emotional sequelae of the cancer experience. Studies investigating the correlation between R/S (eg, beliefs, experiences, coping) and mental health (eg, depression, anxiety, well being) in cancer have used very heterogeneous measures and have produced correspondingly inconsistent results. A meaningful synthesis of these findings has been lacking; thus, the objective of this review was to conduct a meta-analysis of the research on R/S and mental health. Four electronic databases were systematically reviewed, and 2073 abstracts met initial selection criteria. Reviewer pairs applied standardized coding schemes to extract indices of the correlation between R/S and mental health. In total, 617 effect sizes from 148 eligible studies were synthesized using meta-analytic generalized estimating equations, and subgroup analyses were performed to examine moderators of effects. The estimated mean correlation (Fisher z) was 0.19 (95% confidence interval [CI], 0.16-0.23), which varied as a function of R/S dimensions: affective R/S (z = 0.38; 95% CI, 0.33-0.43), behavioral R/S (z = 0.03; 95% CI, -0.02-0.08), cognitive R/S (z = 0.10; 95% CI, 0.06-0.14), and 'other' R/S (z = 0.08; 95% CI, 0.03-0.13). Aggregate, study-level demographic and clinical factors were not predictive of the relation between R/S and mental health. There was little indication of publication or reporting biases. The correlation between R/S and mental health generally was positive. The strength of that correlation was modest and varied as a function of the R/S dimensions and mental health domains assessed. The identification of optimal R/S measures and more sophisticated methodological approaches are needed to advance research.

A meta-analytic review of religious or spiritual involvement and social health among cancer patients.

Religion and spirituality (R/S) play an important role in the daily lives of many cancer patients. There has been great interest in determining whether R/S factors are related to clinically relevant health outcomes. In this meta-analytic review, the authors examined associations between dimensions of R/S and social health (eg, social roles and relationships). A systematic search of the PubMed, PsycINFO, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases was conducted, and data were extracted by 4 pairs of investigators. Bivariate associations between specific R/S dimensions and social health outcomes were examined in a meta-analysis using a generalized estimating equation approach. In total, 78 independent samples encompassing 14,277 patients were included in the meta-analysis. Social health was significantly associated with overall R/S (Fisher z effect size = .20; P < .001) and with each of the R/S dimensions (affective R/S effect size = 0.31 [P < .001]; cognitive R/S effect size = .10 [P < .01]; behavioral R/S effect size = .08 [P < .05]; and 'other' R/S effect size = .13 [P < .001]). Within these dimensions, specific variables tied to social health included spiritual well being, spiritual struggle, images of God, R/S beliefs, and composite R/S measures (all P values < .05). None of the demographic or clinical moderating variables examined were significant. Results suggest that several R/S dimensions are modestly associated with patients' capacity to maintain satisfying social roles and relationships in the context of cancer. Further research is needed to examine the temporal nature of these associations and the mechanisms that underlie them.

Religion, spirituality, and physical health in cancer patients: A meta-analysis.

Although religion/spirituality (R/S) is important in its own right for many cancer patients, a large body of research has examined whether R/S is also associated with better physical health outcomes. This literature has been characterized by heterogeneity in sample composition, measures of R/S, and measures of physical health. In an effort to synthesize previous findings, a meta-analysis of the relation between R/S and patient-reported physical health in cancer patients was performed. A search of PubMed, PsycINFO, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library yielded 2073 abstracts, which were independently evaluated by pairs of raters. The meta-analysis was conducted for 497 effect sizes from 101 unique samples encompassing more than 32,000 adult cancer patients. R/S measures were categorized into affective, behavioral, cognitive, and 'other' dimensions. Physical health measures were categorized into physical well-being, functional well-being, and physical symptoms. Average estimated correlations (Fisher z scores) were calculated with generalized estimating equations with robust variance estimation. Overall R/S was associated with overall physical health (z = 0.153, P < .001); this relation was not moderated by sociodemographic or clinical variables. Affective R/S was associated with physical well-being (z = 0.167, P < .001), functional well-being (z = 0.343, P < .001), and physical symptoms (z = 0.282, P < .001). Cognitive R/S was associated with physical well-being (z = 0.079, P < .05) and functional well-being (z = 0.090, P < .01). 'Other' R/S was associated with functional well-being (z = 0.100, P < .05). In conclusion, the results of the current meta-analysis suggest that greater R/S is associated with better patient-reported physical health. These results underscore the importance of attending to patients' religious and spiritual needs as part of comprehensive cancer care.

Four Methods for Analyzing Partial Interval Recording Data, with Application to Single-Case Research.

Partial interval recording (PIR) is a procedure for collecting measurements during direct observation of behavior. It is used in several areas of educational and psychological research, particularly in connection with single-case research. Measurements collected using partial interval recording suffer from construct invalidity because they are not readily interpretable in terms of the underlying characteristics of the behavior. Using an alternating renewal process model for the behavior under observation, we demonstrate that ignoring the construct invalidity of PIR data can produce misleading inferences, such as inferring that an intervention reduces the prevalence of an undesirable behavior when in fact it has the opposite effect. We then propose four different methods for analyzing PIR summary measurements, each of which can be used to draw inferences about interpretable behavioral parameters. We demonstrate the methods by applying them to data from two single-case studies of problem behavior.

A d-statistic for single-case designs that is equivalent to the usual between-groups d-statistic.

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.

Determining Associations Between Intervention Amount and Outcomes for Young Autistic Children: A Meta-Analysis.

Importance: Health professionals routinely recommend intensive interventions (ie, 20-40 hours per week) for autistic children. However, primary research backing this recommendation is sparse and plagued by methodological flaws. Objective: To examine whether different metrics of intervention amount are associated with intervention effects on any developmental domain for young autistic children. Data Sources: A large corpus of studies taken from a recent meta-analysis (with a search date of November 2021) of early interventions for autistic children. Study Selection: Studies were eligible if they reported a quasi-experimental or randomized clinical trial testing the effects of a nonpharmacological intervention on any outcome in participant samples comprising more than 50% autistic children 8 years or younger. Data Extraction and Synthesis: Data were independently extracted by multiple coders. Meta-regression models were constructed to determine whether each index of intervention amount was associated with effect sizes for each intervention type, while controlling for outcome domain, outcome proximity, age of participants, study design, and risk of detection bias. Data were analyzed from June 2023 to February 2024. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Main Outcomes and Measures: The primary predictor of interest was intervention amount, quantified using 3 different metrics (daily intensity, duration, and cumulative intensity). The primary outcomes of interest were gains in any developmental domain, quantified by Hedges g effect sizes. Results: A total of 144 studies including 9038 children (mean [SD] age, 49.3 [17.2] months; mean [SD] percent males, 82.6% [12.7%]) were included in this analysis. None of the meta-regression models evidenced a significant, positive association between any index of intervention amount and intervention effect size when considered within intervention type. Conclusions and Relevance: Findings of this meta-analysis do not support the assertion that intervention effects increase with increasing amounts of intervention. Health professionals recommending interventions should be advised that there is little robust evidence supporting the provision of intensive intervention.

The efficacy of combining cognitive training and noninvasive brain stimulation: A transdiagnostic systematic review and meta-analysis.

Over the past decade, an increasing number of studies investigated the innovative approach of supplementing cognitive training (CT) with noninvasive brain stimulation (NIBS) to increase the effects on outcomes. In this review, we aim to summarize the evidence for this treatment combination. We identified 72 published and unpublished studies (reporting 773 effect sizes), including 2,518 participants from healthy and clinical populations indexed in PubMed, MEDLINE, APA PsycInfo, ProQuest, Web of Science, and https://ClinicalTrials.gov (last search: August 9, 2022) that compared the effects of NIBS combined with CT on cognitive, symptoms, and everyday functioning to CT alone at postintervention and/or follow-up. We performed random-effects meta-analyses with robust variance estimation and assessed risk of bias with the Cochrane ROB tool. Only four studies had low risk of bias in all domains, and many studies lacked standard controls such as keeping the outcome assessor and trainer unaware of the treatment condition. Following sensitivity analyses, only learning/memory robustly improved significantly more when CT was combined with NIBS compared to CT only (g = 0.18, 95% CI [0.07, 0.29]) at postintervention, but not in the long term. The effect was small and limited by substantial heterogeneity. The other seven cognitive outcome domains, symptoms, and everyday functioning did not benefit from adding NIBS to CT. Given the methodological limitation of prior studies, more high-quality trials that focus on the potential of combining NIBS and CT to enhance benefits in everyday functioning in the short and long term are needed to evaluate whether combining NIBS and CT is relevant for clinical practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

High replicability of newly discovered social-behavioural findings is achievable.

Failures to replicate evidence of new discoveries have forced scientists to ask whether this unreliability is due to suboptimal implementation of methods or whether presumptively optimal methods are not, in fact, optimal. This paper reports an investigation by four coordinated laboratories of the prospective replicability of 16 novel experimental findings using rigour-enhancing practices: confirmatory tests, large sample sizes, preregistration and methodological transparency. In contrast to past systematic replication efforts that reported replication rates averaging 50%, replication attempts here produced the expected effects with significance testing (P < 0.05) in 86% of attempts, slightly exceeding the maximum expected replicability based on observed effect sizes and sample sizes. When one lab attempted to replicate an effect discovered by another lab, the effect size in the replications was 97% that in the original study. This high replication rate justifies confidence in rigour-enhancing methods to increase the replicability of new discoveries.

Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data.

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An Examination of Measurement Procedures and Characteristics of Baseline Outcome Data in Single-Case Research.

There has been growing interest in using statistical methods to analyze data and estimate effect size indices from studies that use single-case designs (SCDs), as a complement to traditional visual inspection methods. The validity of a statistical method rests on whether its assumptions are plausible representations of the process by which the data were collected, yet there is evidence that some assumptions-particularly regarding normality of error distributions-may be inappropriate for single-case data. To develop more appropriate modeling assumptions and statistical methods, researchers must attend to the features of real SCD data. In this study, we examine several features of SCDs with behavioral outcome measures in order to inform development of statistical methods. Drawing on a corpus of over 300 studies, including approximately 1,800 cases, from seven systematic reviews that cover a range of interventions and outcome constructs, we report the distribution of study designs, distribution of outcome measurement procedures, and features of baseline outcome data distributions for the most common types of measurements used in single-case research. We discuss implications for the development of more realistic assumptions regarding outcome distributions in SCD studies, as well as the design of Monte Carlo simulation studies evaluating the performance of statistical analysis techniques for SCD data.

Between-case standardized mean differences: Flexible methods for single-case designs.

Single-case designs (SCDs) are a class of research methods for evaluating the effects of academic and behavioral interventions in educational and clinical settings. Although visual analysis is typically the first and main method for analysis of data from SCDs, quantitative methods are useful for synthesizing results and drawing systematic generalizations across bodies of single-case research. Researchers who are interested in synthesizing findings across SCDs and between-group designs might consider using the between-case standardized mean difference (BC-SMD) effect size, which aims to put results from both types of studies into a common metric. Currently available BC-SMD methods are limited to treatment reversal designs with replication across participants and across-participant multiple baseline designs, yet more complex designs are sometimes used in practice. In this study, we extend available BC-SMD methods to several variations of the multiple baseline design, including the replicated multiple baseline across behaviors or settings, the clustered multiple baseline design, and the multivariate multiple baseline across participants. For each variation, we describe methods for estimating BC-SMD effect sizes and illustrate our proposed approach by re-analyzing data from a published SCD study.

Systematic Review of Variables Related to Instruction in Augmentative and Alternative Communication Implementation: Group and Single-Case Design.

PURPOSE: This article provides a systematic review and analysis of group and single-case studies addressing augmentative and alternative communication (AAC) intervention with school-aged persons having autism spectrum disorder (ASD) and/or intellectual/developmental disabilities resulting in complex communication needs (CCNs). Specifically, we examined participant characteristics in group-design studies reporting AAC intervention outcomes and how these compared to those reported in single-case experimental designs (SCEDs). In addition, we compared the status of intervention features reported in group and SCED studies with respect to instructional strategies utilized. PARTICIPANTS: Participants included school-aged individuals with CCNs who also experienced ASD or ASD with an intellectual delay who utilized aided or unaided AAC. METHOD: A systematic review using descriptive statistics and effect sizes was implemented. RESULTS: Findings revealed that participant features such as race, ethnicity, and home language continue to be underreported in both SCED and group-design studies. Participants in SCED investigations more frequently used multiple communication modes when compared to participants in group studies. The status of pivotal skills such as imitation was sparsely reported in both types of studies. With respect to instructional features, group-design studies were more apt to utilize clinical rather than educational or home settings when compared with SCED studies. In addition, SCED studies were more apt to utilize instructional methods that closely adhered to instructional features more typically characterized as being associated with behavioral approaches. CONCLUSION: The authors discuss future research needs, practice implications, and a more detailed specification of treatment intensity parameters for future research.

Multilevel meta-analysis of single-case experimental designs using robust variance estimation.

Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under different conditions. In research areas where SCEDs are prevalent, there is a need for methods to synthesize results across multiple studies. One approach to synthesis uses a multilevel meta-analysis (MLMA) model to describe the distribution of effect sizes across studies and across cases within studies. However, MLMA relies on having accurate sampling variances of effect size estimates for each case, which may not be possible due to auto-correlation in the raw data series. One possible solution is to combine MLMA with robust variance estimation (RVE), which provides valid assessments of uncertainty even if the sampling variances of effect size estimates are inaccurate. Another possible solution is to forgo MLMA and use simpler, ordinary least squares (OLS) methods with RVE. This study evaluates the performance of effect size estimators and methods of synthesizing SCEDs in the presence of auto-correlation, for several different effect size metrics, via a Monte Carlo simulation designed to emulate the features of real data series. Results demonstrate that the MLMA model with RVE performs properly in terms of bias, accuracy, and confidence interval coverage for estimating overall average log response ratios. The OLS estimator corrected with RVE performs the best in estimating overall average Tau effect sizes. None of the available methods perform adequately for meta-analysis of within-case standardized mean differences. (PsycInfo Database Record (c) 2022 APA, all rights reserved).