First Advisor

Thomas Kindermann

Term of Graduation

Spring 2020

Date of Publication

7-14-2020

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Psychology

Department

Psychology

Language

English

Subjects

Observation (Psychology), Reinforcement (Psychology), Behavior modification, Developmental psychology

DOI

10.15760/etd.7427

Physical Description

1 online resource (viii, 170 pages)

Abstract

How do consequences affect future behaviors in real-world social interactions? The term positive reinforcer refers to those consequences that are associated with an increase in probability of an antecedent behavior (Skinner, 1938). To explore whether reinforcement occurs under naturally occuring conditions, many studies use sequential analysis methods to detect contingency patterns (see Quera & Bakeman, 1998). This study argues that these methods do not look at behavior change following putative reinforcers, and thus, are not sufficient for declaring reinforcement effects arising in naturally occuring interactions, according to the Skinner's (1938) operational definition of reinforcers.

This study presents the conceptual and technical development of an algorithmic strategy and web-app used to restructuring naturalistic observations in a way that can be used to assess behavior change as a function of putative reinforcers. I demonstrate this strategy by re-examining some of the hypotheses Sage and Kindermann's (1999) study on contingency patterns across fifth and sixth grade students. Using methods of generalized estimating equations (e.g. Liang & Zeger, 1986) and Allison and Liker (1982) z-scores, this study found full support for reinforcement effects of Social Approval for children's Off-Task classroom behaviors, and partial support for Social Approval following On-Task behaviors. Social Disapproval was shown to reinforce Off-Task behaviors, contrary to expectations. This paper concludes by a comprehensive discussion of these results and directions for future modifications of these reinforcement detection strategies.

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Persistent Identifier

https://archives.pdx.edu/ds/psu/33662

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