Western Michigan University ScholarWorks at WMU Dissertations Graduate College 4-1992 Effects of Feedback Type and Signal Probability on Quality Inspection Accuracy Matthew A. Mason Western Michigan University Follow this and additional works at: https://scholarworks.edu/dissertations Part of the Experimental Analysis of Behavior Commons Recommended Citation Mason, Matthew A., "Effects of Feedback Type and Signal Probability on Quality Inspection Accuracy" (1992).edu/dissertations/1980 This Dissertation-Open Access is brought to you for free and open access by the Graduate College at ScholarWorks at WMU. It has been accepted for inclusion in Dissertations by an authorized administrator of ScholarWorks at WMU. For more information, please contact wmu-scholarworks@wmich.
EFFECTS OF FEEDBACK TYPE AND SIGNAL PROBABILITY ON QUALITY INSPECTION ACCURACY by Matthew A. Mason A Dissertation Submitted to the Faculty of The Graduate College in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Department of Psychology Western Michigan University Kalamazoo, Michigan April 1992 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. EFFECTS OF FEEDBACK TYPE AND SIGNAL PROBABILITY ON QUALITY INSPECTION ACCURACY Matthew A.
Western Michigan University, 1992 A computer simulation was developed to examine the effects of feedback type (immediate, delayed, or none) and signal probability (p = 0.12) on the accuracy of identifying signals (missing components), inspection response rate, and response sensitivity (d'). Subjects were randomly assigned to one of six experimental groups: (1) immediate feedback with a signal probability of 0.05), (2) delayed feedback with a signal probability of 0.05), (3) no feedback with a signal probability of 0.05), (4) immediate feedback with a signal probability of 0.12), (5) delayed feedback with a signal probability of 0.12), and (6) no feedback with a signal probability of 0. In a self-paced computer tutorial, subjects learned to identify the presence/absence of signals in a schematic diagram of a hard disk drive on a computer screen. During experimental sessions, subjects were exposed to series of 200 machine-paced samples and were required to indicate whether or not each sample contained a signal.
Low signal probability resulted in higher inspection accuracy and lower response sensitivity compared to high signal probability. Type of feedback did not affect inspection accuracy across experimental gioups. However, some minimal effects of feedback type were evident, including (a) delayed feedback resulted in lower inspection accuracy during earlier experimental sessions than in later sessions (immediate and no-feedback conditions showed no such difference); and (b) high signal probability with delayed feedback resulted in slower response rates than high signal probability with immediate or no feedback. Reproduced with permission of the copyright owner.
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O rder N u m b er 9221696 Effects o f feedback type and signal probability on quality inspection accuracy M ason, M atthew A braham , Ph. Western Michigan University, 1992 Copyright © 1992 by M ason, M atthew Abraham. All rights reserved. Ann Arbor, MI 48106 Reproduced with permission of the copyright owner.
Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Copyiight by Matthew A.
Mason 1992 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGEMENTS The preparation of a doctoral dissertation is rarely the effort of a single individual; there are many to whom I am indebted. First and foremost, I owe gratitude to William K., for his close supervision, guidance, and keen eye for revision.
Few are as deserving of the title of “mentor” as Dr. Special thanks me due to Alyce M., for generously extending the use of her laboratory facilities and equipment. Heartfelt thanks are extended to Katie Cronin, whose dedicated assistance was invaluable to the expeditious completion of this project. I would also like to thank each of my dissertation committee members for their expertise and guidance throughout my doctoral studies: Drs.
Malott, Jack Michael, and Helen D. My future efforts will be a reflection of your teachings. Financial support for this dissertation was provided by the Organizational Behavior Management (OEM) Network. The dedication of the OEM Network to research and students of OEM is an investment in the future.
Last, but never least, a special acknowledgement is given to Asiah Mayang, companion and best friend, for her constant encouragement, easy laugh, and generous affection throughout our academic lives. Mason Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS ACKNOWLEDGEMENTS.
ü LIST OF TABLES. v LIST OF FIG U R ES. 8 Subjects and Setting. 8 Quality Control T a sk.
11 Type of Feedback. 12 E xperim ental D e sig n. 15 Social V a lid atio n. 17 Summary of Effects on Inspection Accuracy, Rate, and Sensitivity.
17 Effects on Inspection Accuracy. 21 Effects on Inspection Response R ates. 24 iii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table of Contents-Continued CHAPTER Effects on Inspection ResponseSensitivity. 26 Social V alidation R esults. Informed Consent Form. Reproduction of the Computer Tutorial.
71 IV Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF TABLES 1. Experimental Conditions and Group Assignment.
Summary of Mean Inspection Accuracy (% Correct), Response Rates (R/s), Number of Hits (H), Correct Acceptances (CA), False Alarms (FA), and Misses (M), Proportion of Hits (p[H]), Proportion of False Alarms ^[F ]), and Response Sensitivity (d') Across Experimental Groups. Summary of Social Validation Questionnaire. Percentage of Correct Inspection Responses, Response Rates (R/s), Number of Hits (H), Correct Acceptances (CA), False Alarms (FA), and Misses (M), Proportion of Hits (p[H]) and False Alarms (p[F]), and Response Sensitivity (d') Across Subjects by Experimental Session and G roup. Two-Factor ANOVA on Inspection Accuracy (Percentage of Correct Responses).
Two-Factor ANOVA on Split-Half Inspection Accuracy (Mean Percent C h a n g e ). Multiple Comparisons (Tukey Procedure): Feedback Type on Split-Half Mean Percent Change in Inspection Accuracy. Two-Factor ANOVA on Inspection Rate (Responses per Second) 67 9. One Factor ANOVAs: Signal Probability and Feedback Type on Mean Response R ate.
Multiple Comparisons (Tukey Procedure): Feedback Type (Signal p = 0.12) on Response Rates. Two-Factor ANOVA on Response Sensitivity (d '). Two-Factor ANOVA on Number of False Alarms. Two-Factor ANOVA on Number of M isses.
70 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF FIGURES 1. Sample Stimulus Screen (Actual Size).
Mean Percentage of Correct Inspection Responses by G roup. Mean Number of False Alarms and Misses by Group. Mean Response Rates (Responses per Second) by Group. Mean Response Sensitivity (d') by G roup.
26 VI Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER I INTRODUCITON A familiar theme in American manufacturers’ advertisements has been the quality of their merchandise. The importance of quality control to American industry has grown steadily over the past few decades, ostensibly for economic (e., production cost reduction and industry competition) and societal (e., consumer demands) reasons.
In this regard, Deming (1975) emphasized that the poor quality of manufactured products in the United States has been responsible for the decline of the American economy, and recommended the adoption of quality control procedures that focus on detecting defects during the manufacturing process. Visual inspection of manufactured products is an important part of many quality control procedures; however, human inspection often results in low levels of accuracy of detection of defects (Colquhoun, 1961; Drury & Addison, 1973; Drury & Fox, 1975; Fortune, 1979; Harris, 1968; Harris & Chaney, 1969; Synfelt & Brunskill, 1986; Wiener, 1984). Empirical investigations have examined factors that influence the accuracy of visual inspection, including inspection methods, supervision (e., form of feedback used or knowledge of results regarding inspection), and the nature and complexity of the task and stimuli (Chaney & Teel, 1967; Harris, 1968, 1969; Harris & Chaney, 1969). Early studies examined detection of signals in monotonous monitoring tasks through what is popularly known as vigilance research.
Mackworth (1950) developed a continuous clock test, in which a circular dial with a moving pointer advanced one discrete unit each second, like a clock. The pointer would infrequently and at irregular 1 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2 intervals move two units instead of one, and subjects were required to detect such signals during a two-hour monitoring session.
In general, subjects failed to detect up to 30% of these signals; however, when subjects were provided information regarding their accuracy, or knowledge of results (KOR), the percentage of signals missed was dramatically reduced. Studies have also indicated that the percentage of signals detected decreases as the time spent at the vigilance task progresses (Bakan, 1955; Gallwey & Drury, 1986; Jenkins, 1957) and increases as the probability of signal presentation increases (Baddeley & Colquhoun, 1969; Colquhoun, 1961; Craig, 1980; Fortune, 1979; Fox & Haslegrave, 1969; Gallwey & Drury, 1986; Harris, 1968, 1969; Harris & Chaney, 1969; Jenkins, 1957). A wide range of signal probabilities have been studied, from 0. Fortune, 1979; Fox & Haslegrave, 1969), to 0., Colquhoun, 1961; Craig, 1981), to 0., Baddeley & Colquhoun, 1969; Craig, 1980, 1981).
The broad experimental base of vigilance research contributed to the development of signal detection theory (SDT) and related research first introduced by Tanner and Swets (1954). Signal detection theory places heavy emphasis on the effects of environmental conditions (i., complexity of the inspection task, frequency of signal occurrences) on the discriminability, or detectability, of a stimulus change. Fortune (1979) investigated the effects of probability of occurrence of signals on the accuracy of signal detection in a microscopic inspection task. Zoology graduate students were required to deteimine, through microscopic examination, whether tissue slides prepared from animals exposed to chronic doses of selected chemical compounds were abnormal (signals) or normal.
Fortune concluded that lower inspection accuracy occurred when the abnormal microscopic signals were less frequent.