Applied Behavior Analysis Chapter 10

Applied Behavior Analysis - Chapter 10

• This chapter focuses on planning and evaluating research in Applied Behavior Analysis.
• The importance of single-subject designs is emphasized.
• Between-groups designs have limitations; they don't show specific individual behaviors. They also hide variability and don't represent real behavioral processes. 
• Single-subject designs are crucial for observing individual subject behavior.  They are more detailed and more reliable to identify functional relations.
• Planning and evaluation involve determining dependent and independent variables.
• Evaluating and monitoring treatment integrity is key to understanding and evaluating research. 
• Components of treatment packages can be studied through component analyses.
• Ways to assess treatment integrity are to develop precise operational definitions and simplify treatments.
• Types and methods of replication are important to study the generality of behaviors.
• Identifying social validity: factors include understanding client goals, how appropriate treatment is, and social importance of the results. 

Learning Objectives

• Experimental Design: Distinguish between dependent and independent variables, internal and external validity, and the defining features, advantages, and applications of single-subject designs (e.g., reversal, multiple baseline, multielement, changing criterion). 
• Conducting Component and Parametric Analyses Describes rationales for conducting comparative, component, and parametric analyses.
• Selecting and Implementing Interventions: Monitor client progress and treatment integrity. 

Importance of the Individual Subject in Behavior Analysis Research

• Behavior analysis research uses direct and repeated measures.
• Between-groups approaches to experimental design has been used frequently, but has limitations. It assumes averaging numerous subjects' measures controls for variability. 
• Increasing the number of subjects enhances external validity, but doesn't clarify individual subject behavior. 

Four Fundamental Concerns with Typical Between-Groups Designs

• May not represent the performance of individual subjects; 
• Mask individual variability;
• Do not represent real behavioral processes;
• Lack intrasubject replication.

Group Data May Not Represent the Performance of Individual Subjects

• Average performance of groups doesn't reveal information about individual subjects' performance.
• Factors for one subject's improvement might be different from another subject's lack of improvement. 

Figure 10.1 (Hypothetical data)

• Illustrates how average group data may not accurately reflect individual subject behavior. 

Group Data Mask Variability

• Group means hide individual variability.
• A researcher focusing only on group means might miss important individual trends. 
• Significant individual variability indicates a need to investigate factors influencing behavior.

Group Data Do Not Represent Real Behavioral Processes

• Focusing on groups might miss real behavioral processes and true cause-and-effect relationships.
• Individual subjects are more important than group means.

Between-Groups Designs Lack Intrasubject Replication

• Single-subject designs allow replication to convincingly demonstrate functional relations.
• Combining data from multiple subjects doesn't imply a real behavioral process or effect.
• Improving overall performance of a group isn't always reflective of individual subject improvements.

Importance of Flexibility in Experimental Design

• An effective design is any sequence of variable manipulations that produces interesting and convincing data.
• No one-size-fits-all experimental design.

Experimental Designs Combining Analytic Tactics

• Combining multiple analytic tactics (like multiple baselines, reversals, and multielement) can demonstrate more convincing experimental control than using only one.
• Comparing effects of two or more independent variables is important with multiple treatments and multiple settings.

Figure 10.2 (Experimental Designs)

• Shows examples of experimental designs using multiple baselines, reversals, and other techniques to evaluate different treatment conditions across multiple subjects and settings..

Treatment Packages

• Multiple-component interventions constitute treatment packages.
• Interventions can be more effective when combined. 
• Some interventions are mildly effective on their own but more effective when combined with other interventions (additive effect.)

Component Analyses

• Component analyses aim to identify active elements within a treatment package.
• Two methods: drop-out (removing components) and add-in (adding components). 

Internal Validity

• Experiments with clear functional relations have high internal validity.
• A strong design reduces the likelihood that factors besides the independent variable caused the changes in the dependent variable.
• Experimental control signifies reliably changing behavior through manipulating the independent variable.

Confounds

• Confounds are uncontrolled variables influencing the dependent variable.
• It is crucial to control or account for these uncontrolled elements.
• Potential kinds of confounding variables include the subject, setting, measurement, and independent variables.

Subject Confounds

• Maturation (changes within a subject over time).
• External events affecting subject behavior.
• Repeated measurements help control and measure these external events.
• Intrasubject direct replication help demonstrate specific effects.

Setting Confounds

• Natural settings are easily affected by uncontrollable outside events.
• 'Bootlegging' reinforcement where subjects have access to unwanted stimuli. 

Measurement Confounds

• Observer drift, observer bias, reactivity to measurement (where presence of a measurement impacts the results that are intended to be measured.)

Independent Variable Confounds

• Independent variables can be multifaceted (have more factors than just the specific variable of interest).
• Placebo control separates treatment effects from subject expectations.
• Double-blind control shields both subjects and researchers from biases.

Treatment Integrity

• Treatment integrity reflects how consistently a treatment is implemented as planned.
• Low treatment integrity can seriously confound results.

Threats to Treatment Integrity

• Experimenter bias: researcher might unintentionally alter treatment application in favor of one particular result.
• Treatment drift: treatment is not consistent throughout the study.

Methods for Ensuring Treatment Integrity

• Develop complete operational definitions of treatment procedures.
• Simplify and standardize independent variables with easy-to-use, consistent techniques.
• Provide criterion-based training for people implementing treatments.

Methods for Measuring Treatment Integrity

• Procedural fidelity data reveal consistency of implementation over time.
• Observation and recording help evaluate treatment implementation.
• Graphic displays of treatment integrity can help judge effectiveness.

Social Validity

• Social significance of interventions' goals, procedures and outcomes.
• Client's understanding and satisfaction is important.

Validating the Social Importance of Behavior Change Goals

• Clear descriptions of social significance
• Expert and client perspectives aid selection of valid behavioral targets
• Evaluating participant performance within natural environments.

Validating the Social Acceptability of Interventions

• Measurement tools for evaluating treatment acceptability (like IRP-15, Behavior Intervention Rating Scale, TARF).
• Graphic displays of treatability, and allowing choices to evaluate acceptability. 

Validating the Social Importance of Behavior Change

• Assess social validity with methods like consumer ratings, expert ratings, normative comparisons, or testing in natural environments.. 

External Validity and Between-Groups Research Design

• Group designs often assume that more subjects lead to greater external validity, but this assumption has limitations.
• Demonstrating functional relations across subjects and settings is accurate evaluation. 

External Validity and Applied Behavior Analysis

• External Validity is determined by replicating experiments, both direct and systematic.

Direct Replication

• Duplicating the exact conditions and procedures of previous experiments.
• Intrasubject and intersubject replication:  intrasubject replicates with the same participant, and intersubject replicates with a different participant, allowing for generalization of conclusions across subjects. 

Systematic Replication

• Intentionally varying aspects (subjects, settings, variables, target behaviors) of a previous experiment.
• Using systematic replication to determine generalizability across subjects.
• Research often includes multiple experiments.

Evaluating Applied Behavior Analysis Research-Internal Validity

• Evaluate if a functional relationship was demonstrated. 
• Review measurement systems, experimental designs, confound control, and data analysis methods.
• Identify types of errors (Type I and Type II) in a statistical context.

Figure 10.12

• Illustrates the possible outcomes of a study when a functional relationship exists or does not exist based on researcher's conclusion.

Evaluating Applied Behavior Analysis Research-Social Validity

• Judge the social significance of the target behavior, procedure appropriateness and the practicality, as well as the social value of the resulting outcomes.   

Evaluating Applied Behavior Analysis Research-External Validity

• Examine whether the findings of a study can be generalized to other settings or individuals.
• Methodological rigor in replication is important for generalizability.

Evaluating Applied Behavior Analysis Research-Theoretical Significance

• Evaluate the experiment's scientific merit. 
• Assess conceptual understanding of underlying principles.
• Component analyses and parametric analyses are important steps in understanding behavior.

This quiz covers Chapter 10 of Applied Behavior Analysis, which delves into the planning and evaluation of research methods in the field. It highlights the significance of single-subject designs over between-groups designs and discusses the evaluation of treatment integrity. Get ready to test your understanding of crucial concepts in behavior analysis research!