Measurement in Social Psychology

278 Pages·2019·4.596 MB·English

by  Hart Blanton, Jessica M. LaCroix, Gregory D. Webster

MEASUREMENT IN SOCIAL
PSYCHOLOGY
Although best known for experimental methods, social psychology also has a
strong tradition of measurement. This volume seeks to highlight this tradition by
introducing readers to measurement strategies that help drive social psychological
research and theory development.
The book opens with an analysis of the measurement technique that dominates
most of the social sciences, self-report. Chapter 1 presents a conceptual framework
for interpreting the data generated from self-report, which it uses to provide practical
advice on writing strong and structured self-report items. From there, attention is
drawn to the many other innovative measurement and data-collection techniques
that have helped expand the range of theories social psychologists test. Chapters 2
through 6 introduce techniques designed to measure the internal psychological
states of individual respondents, with strategies that can stand alone or complement
anything obtained via self-report. Included are chapters on implicit, elicitation, and
diary approaches to collecting response data from participants, as well as neurological
and psychobiological approaches to inferring underlying mechanisms. The remaining
chapters introduce creative data-collection techniques, with particular attention
given to the rich forms of data humans often leave behind. Also included are chapters
on textual analysis, archival analysis, geocoding, and social media harvesting.
The many methods covered in this book complement one another, such that
the full volume provides researchers with a powerful toolset to help them better
explore what is “social” about human behavior. This is fascinating reading for
students and researchers in social psychology.
Hart Blanton is Professor of Communication at Texas A&M University. He
conducts research in the areas of social influence, health communication, and
research methodology.
Jessica M. LaCroix is Research Assistant Professor at the Uniformed Services
University of the Health Sciences and specializes in health psychology, research
methodology, and military suicide prevention.
Gregory D. Webster is Associate Professor of Social Psychology at University
of Florida with graduate degrees from the College of William & Mary and the
University of Colorado.
Frontiers of Social Psychology
Series Editors:
Arie W. Kruglanski
University of Maryland at College Park
Joseph P. Forgas
University of New South Wales
Frontiers of Social Psychology is a series of domain-specific handbooks. Each volume
provides readers with an overview of the most recent theoretical, methodological,
and practical developments in a substantive area of social psychology, in greater
depth than is possible in general social psychology handbooks. The editors and
contributors are all internationally renowned scholars whose work is at the cut-
ting edge of research.
Scholarly, yet accessible, the volumes in the Frontiers series are an essential
resource for senior undergraduates, postgraduates, researchers, and practitioners and
are suitable as texts in advanced courses in specific subareas of social psychology.
Published Titles
Intergroup Conflicts and their Resolution
Bar-Tal
Social Motivation
Dunning
Social Cognition
Strack & Förster
Social Psychology of Consumer Behavior
Wänke
For continually updated information about published and forthcoming titles in
the Frontiers of Social Psychology series, please visit: https://www.routledge.com/
psychology/series/FSP
MEASUREMENT IN
SOCIAL PSYCHOLOGY
Edited by Hart Blanton, Jessica M. LaCroix,
and Gregory D. Webster
First published 2019
by Routledge
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and by Routledge
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Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2019 Taylor & Francis
The right of Hart Blanton, Jessica M. LaCroix, and Gregory D. Webster
to be identified as the authors of the editorial material, and of the authors
for their individual chapters, has been asserted in accordance with
sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced
or utilised in any form or by any electronic, mechanical, or other means,
now known or hereafter invented, including photocopying and recording,
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Trademark notice: Product or corporate names may be trademarks or
registered trademarks, and are used only for identification and explanation
without intent to infringe.
Library of Congress Cataloging-in-Publication Data
A catalog record for this title has been requested
ISBN: 978-1-138-91323-3 (hbk)
ISBN: 978-1-138-91324-0 (pbk)
ISBN: 978-0-429-45292-5 (ebk)
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CONTENTS
1 From Principles to Measurement: Theory-Based Tips
on Writing Better Questions 1
Hart Blanton and James Jaccard
2 Implicit Measures: Procedures, Use, and Interpretation 29
Bertram Gawronski and Adam Hahn
3 Elicitation Research 56
William A. Fisher, Jeffrey D. Fisher, and Katrina Aberizk
4 Psychobiological Measurement 75
Peggy M. Zoccola
5 It’s About Time: Event-Related Brain Potentials and
the Temporal Parameters of Mental Events 102
Meredith P. Levsen, Hannah I. Volpert-Esmond,
and Bruce D. Bartholow
6 Using Daily Diary Methods to Inform and Enrich
Social Psychological Research 127
Marcella H. Boynton and Ross E. O’Hara
7 Textual Analysis 153
Cindy K. Chung and James W. Pennebaker
vi Contents
8 Data to Die For: Archival Research 174
Brett W. Pelham
9 Geocoding: Using Space to Enhance Social Psychological
Research 201
Natasza Marrouch and Blair T. Johnson
10 Social Media Harvesting 228
Man-pui Sally Chan, Alex Morales, Mohsen Farhadloo, Ryan
Joseph Palmer, and Dolores Albarracín
Index 265
1
FROM PRINCIPLES TO
MEASUREMENT
Theory-Based Tips on Writing
Better Questions
Hart Blanton and James Jaccard
Self-reports are the dominant assessment method in the social sciences and a large
part of their appeal is the ease with which questions can be generated and admin-
istered. In our view, however, this apparent ease obscures the care that is needed
to produce questions that generate meaningful data. In this chapter, we introduce
and review basic principles of measurement, which we then use as a foundation to
offer specific advice (“tips”) on how to write more effective questions.
Principles of Measurement
A Measurement Model
Suppose a researcher wanted to measure consumers’ judgments of the quality of
a product. Perceptions of product quality cannot be observed directly—perceived
quality is a latent, theoretical psychological construct, assumed to be continu-
ous in character, such that it can only be inferred indirectly through observable
actions. One such action can be ratings a consumer makes on a rating scale. Sup-
pose consumers are asked to rate the perceived quality of a product on a scale
that ranges from 0 (“very low quality”) to 6 (“very high quality”). By seeking
to quantify product perceptions in this manner—and whether the researcher has
realized it or not—a formal measurement model has been embraced. This model
is depicted in Figure 1.1.
The rectangle labeled “Q” in Figure 1.1 represents the rating on the 0-to-6
scale. This rating does not, by fiat, reveal “true” quality perceptions of the respond-
ent, which is conceptualized as an unobservable latent construct and represented
in Figure 1.1 by the circle with the word “quality” in it. The researcher assumes
that the observed “Q” is influenced by true, latent quality perceptions, but that
2 Hart Blanton and James Jaccard
ε
Q
Quality

FIGURE 1.1 Measurement Model
the correspondence between latent and observed constructs is less than perfect.
Ratings on Q are thus a function of both the consumers’ true evaluations and
measurement error (represented as “ε” in Figure 1.1). This can be expressed alge-
braically in the form of a linear model:
Q =α+λQuality+ε [1]
where α is an intercept, λ is a regression coefficient (also frequently called a
loading), and ε is measurement error. When the relationship is linear, as assumed
in Equation 1, then Q is an interval-level measure of the latent construct of
perceived quality. If the relationship is non-liner but monotonic, Q is an ordinal
measure of the latent construct. Articulation of this formal model focuses atten-
tion on one of the primary challenges facing researchers who wish to create self-
report questions—the need to reduce the influence of error on observed ratings.
We next consider two sources of error, random and systematic, as well as their
implications for characterizing the reliability and validity of self-report items.
Random Error and Reliability
Random error represents random influences, known or unknown, that arbitrarily
bias numeric self-reports upward or downward. Often referred to as “noise,” ran-
dom error can be generated by such factors as momentary distractions, fluke mis-
understandings, transient moods, and so on. This form of error is commonplace,
but its relative magnitude can vary considerably from one question to the next.
As such, it is meaningful to think about the degree to which a given question
From Principles to Measurement 3
or set of questions is susceptible to random error. This represents the concept of
reliability.
The reliability of observed scores conveys the extent to which they are free
of random error. Statistically, a reliability estimate communicates the percentage
of variance in the observed scores that is due to unknown, random influences
as opposed to systematic influences. Thus, if the reliability of a set of scores is
0.80, then 80% of their variation is systematic and 20% is random. The presence
of random error in measures can bias statistical parameter estimates, potentially
attenuating correlations and causing researchers to think they have sufficiently
controlled for constructs in an analysis, when they have not.
Systematic Error and Validity
Another form of measurement error is called systematic error. This source of error
often introduces variance into observed self-report items that is non-random; i.e.,
that is a function of one or more psychological constructs that are something
different than the construct of interest. Consider the model in Figure 1.2. Here
a researcher hopes to measure both drug use and grade-point average (GPA) via
self-report. Each of these constructs are influenced by the true latent constructs
that are of interest (as in Figure 1.2), but another latent construct is also exerting
influence on the two measures, social desirability.
Social
Desirability
ε1 ε2
Reported Reported
Drug Use GPA
True Drug True Drug
Use Use

FIGURE 1.2 Example of Systematic Error