skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Meta‐analyses and effect sizes in applied behavior analysis: A review and discussion
For more than four decades, researchers have used meta‐analyses to synthesize data from multiple experimental studies often to draw conclusions that are not supported by individual studies. More recently, single‐case experimental design (SCED) researchers have adopted meta‐analysis techniques to answer research questions with data gleaned from SCED experiments. Meta‐analyses enable researchers to answer questions regarding intervention efficacy, generality, and condition boundaries. Here we discuss meta‐analysis techniques, the rationale for their adaptation with SCED studies, and current indices used to quantify the effect of SCED data in applied behavior analysis.  more » « less
Award ID(s):
2026513
PAR ID:
10449370
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Journal of Applied Behavior Analysis
Volume:
54
Issue:
4
ISSN:
0021-8855
Page Range / eLocation ID:
p. 1317-1340
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, PLOS ONE)
    Silva, Daniel de (Ed.)
    Quantitatively summarizing results from a collection of primary studies with meta-analysis can help answer ecological questions and identify knowledge gaps. The accuracy of the answers depends on the quality of the meta-analysis. We reviewed the literature assessing the quality of ecological meta-analyses to evaluate current practices and highlight areas that need improvement. From each of the 18 review papers that evaluated the quality of meta-analyses, we calculated the percentage of meta-analyses that met criteria related to specific steps taken in the meta-analysis process (i.e., execution) and the clarity with which those steps were articulated (i.e., reporting). We also re-evaluated all the meta-analyses available from Pappalardo et al. [1] to extract new information on ten additional criteria and to assess how the meta-analyses recognized and addressed non-independence. In general, we observed better performance for criteria related to reporting than for criteria related to execution; however, there was a wide variation among criteria and meta-analyses. Meta-analyses had low compliance with regard to correcting for phylogenetic non-independence, exploring temporal trends in effect sizes, and conducting a multifactorial analysis of moderators (i.e., explanatory variables). In addition, although most meta-analyses included multiple effect sizes per study, only 66% acknowledged some type of non-independence. The types of non-independence reported were most often related to the design of the original experiment (e.g., the use of a shared control) than to other sources (e.g., phylogeny). We suggest that providing specific training and encouraging authors to follow the PRISMA EcoEvo checklist recently developed by O’Dea et al. [2] can improve the quality of ecological meta-analyses. 
    more » « less
  2. (, Handbook of agricultural economics)
    Agricultural and applied economists have begun routinely using behavioral and experimental economics tools to answer important questions about agri-environmental policies and programs. These tools offer valuable insights into decision-making that can advance our economic understanding of human behavior and inform evidence-based policies. However, conducting robust economic experiments on agri-environmental topics presents unique challenges that can make implementation of these studies difficult and limit the applicability of results. This chapter provides a practical guide for researchers regarding best practices for applying experimental and behavioral economics to agri-environmental research focused on producer decision-making. We begin with a brief overview of how insights from behavioral economics have contributed to related literatures over past decades and highlight how economic experiments have been used to answer important research questions in those domains. We describe the types of economic experiments used to answer policy-relevant questions and carefully consider the advantages and limitations of each method in various contexts. We also highlight important trade-offs between control, context, and representativeness to consider when determining the most appropriate type of experiment to conduct. The chapter emphasizes five contemporary issues related to conducting robust experimental economics studies: replicability, statistical power, publication bias, farmer and rural landowner recruitment, and detection of heterogeneous treatment effects. To assist researchers in addressing each issue, we outline best practices and we offer recommendations for researchers, editors, reviewers, and funders. We also discuss research ethics and community engagement. Finally, we present a framework for prioritizing future economics research that can inform agri-environmental programs and policies. 
    more » « less
  3. ; (, Ecology Letters)
    ABSTRACT Ecology often seeks to answer causal questions, and while ecologists have a rich history of experimental approaches, novel observational data streams and the need to apply insights across naturally occurring conditions pose opportunities and challenges. Other fields have developed causal inference approaches that can enhance and expand our ability to answer ecological causal questions using observational or experimental data. However, the lack of comprehensive resources applying causal inference to ecological settings and jargon from multiple disciplines creates barriers. We introduce approaches for causal inference, discussing the main frameworks for counterfactual causal inference, how causal inference differs from other research aims and key challenges; the application of causal inference in experimental and quasi‐experimental study designs; appropriate interpretation of the results of causal inference approaches given their assumptions and biases; foundational papers; and the data requirements and trade‐offs between internal and external validity posed by different designs. We highlight that these designs generally prioritise internal validity over generalisability. Finally, we identify opportunities and considerations for ecologists to further integrate causal inference with synthesis science and meta‐analysis and expand the spatiotemporal scales at which causal inference is possible. We advocate for ecology as a field to collectively define best practices for causal inference. 
    more » « less
  4. ; ; ; ; ; ; (, Molecular Ecology Resources)
    Abstract Patterns of genetic diversity within species contain information the history of that species, including how they have responded to historical climate change and how easily the organism is able to disperse across its habitat. More than 40,000 phylogeographic and population genetic investigations have been published to date, each collecting genetic data from hundreds of samples. Despite these millions of data points, meta‐analyses are challenging because the synthesis of results across hundreds of studies, each using different methods and forms of analysis, is a daunting and time‐consuming task. It is more efficient to proceed by repurposing existing data and using automated data analysis. To facilitate data repurposing, we created a database (phylogatR) that aggregates data from different sources and conducts automated multiple sequence alignments and data curation to provide users with nearly ready‐to‐analyse sets of data for thousands of species. Two types of scientific research will be made easier by phylogatR: large meta‐analyses of thousands of species that can address classic questions in evolutionary biology and ecology, and student‐ or citizen‐ science based investigations that will introduce a broad range of people to the analysis of genetic data. phylogatR enhances the value of existing data via the creation of software and web‐based tools that enable these data to be recycled and reanalysed and increase accessibility to big data for research laboratories and classroom instructors with limited computational expertise and resources. 
    more » « less
  5. ; ; ; ; ; (, Current Protocols)
    Abstract Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command‐line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open‐source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology‐based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta‐analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA‐Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA‐Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology‐based (TB) pathway analysis Basic Protocol 7: Data integration and visualization 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email