Table Of ContentQuantitative Research Methods in Translation and
Interpreting Studies
Quantitative Research Methods in Translation and Interpreting Studies encompasses all stages of the research
process that include quantitative research methods, from conceptualization to reporting. In five parts, the authors
cover:
• sampling techniques, measurement, and survey design;
• how to describe data;
• how to analyze differences;
• how to analyze relationships;
• how to interpret results.
Each part includes references to additional resources and extensive examples from published empirical work. A
quick reference table for specific tests is also included in the appendix. This user-friendly guide is the essential
primer on quantitative methods for all students and researchers in translation and interpreting studies.
Accompanying materials are available online, including step-by-step walk-throughs of how analysis was
conducted, and extra sample data sets for instruction and self study: https://www.routledge.com/9781138124967.
Further resources for Translation and Interpreting Studies are available on the Routledge Translation Studies Portal:
http://cw.routledge.com/textbooks/translationstudies.
Christopher D. Mellinger is Assistant Professor of Spanish at Walsh University, USA and the managing editor of
the journal Translation and Interpreting Studies.
Thomas A. Hanson is Assistant Professor of Finance at Minnesota State University Moorhead, USA.
Quantitative Research
Methods in Translation
and Interpreting Studies
Christopher D. Mellinger and
Thomas A. Hanson
First published 2017
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
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Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2017 Christopher D. Mellinger and Thomas A. Hanson
The right of Christopher D. Mellinger and Thomas A. Hanson to be identified as author of this work has been asserted by him/her in accordance with
sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
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British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
Names: Mellinger, Christopher D. (Christopher Davey), author. | Hanson, Thomas A. (Translator) author.
Title: Quantitative research methods in translation and interpreting studies / by Christopher D. Mellinger and Thomas A. Hanson.
Description: Milton Park, Abingdon, Oxon; New York, NY : Routledge, [2016] | Includes bibliographical references and index.
Identifiers: LCCN 2016007454| ISBN 9781138124950 (hardback) | ISBN 9781138124967 (pbk.) | ISBN 9781315647845 (ebook)
Subjects: LCSH: Translating and interpreting—Study and teaching—Research. | Translating and interpreting—Study and teaching—Methodology. |
Translating and interpreting—Research. | Quantitative research.
Classification: LCC P306.5 .M455 2016 | DDC 418/.020721—dc23
LC record available at https://lccn.loc.gov/2016007454
ISBN: 978-1-138-12495-0 (hbk)
ISBN: 978-1-138-12496-7 (pbk)
ISBN: 978-1-3156-4784-5 (ebk)
Typeset in Times New Roman
by Swales & Willis Ltd, Exeter, Devon, UK
Contents
List of figures
List of tables
Acknowledgements
Introduction to the volume
Part I
Preparing
1 Research questions, hypotheses, and design
2 Sampling and ethics
3 Measurement
Part II
Describing
4 Descriptive statistics
5 Probability distributions
6 Statistical terminology and one-sample tests
Part III
Analyzing differences
7 Comparing two groups
8 Comparing multiple groups
9 Comparing groups on multiple factors
10 Testing categorical data
Part IV
Analyzing relationships
11 Correlation and reliability
12 Linear regression
Part V
Interpreting results
13 Interpreting results
References
Appendix A: Guide to selecting a statistical test
Index
Figures
3.1 Scree plot example
4.1 Graphical representation of the median and the mean
4.2 Histograms of unimodal and bimodal frequency distributions
4.3 Graphs of positive and negative skewness
4.4 Bar chart example
4.5 Histogram example
4.6 Box plot example
4.7 Scatterplot example
4.8 Line graph example
5.1 Normal (Gaussian) distribution
5.2 Quantile-Quantile (QQ) plot example
5.3 Left-skewed and leptokurtic QQ plot examples
5.4 The Anscombe quartet (adapted from Anscombe 1973)
6.1 Reported statistics for mean time on task example
6.2 Reported statistics for median test score example
7.1 Box plot of sample data for Russia and Canada elevation example
7.2 Histograms of sample data for Russia and Canada elevation example
7.3 Example of complete reporting of t-test results
8.1 Box plot of elevation example data
8.2 QQ plot of elevation example data
8.3 Null and alternative hypothesis models for ANOVA
8.4 Total and partitioned variation (within and between groups)
8.5 Box plot examples of ANOVA
8.6 Trend analysis example
8.7 Example of complete reporting of ANOVA results
9.1 Factorial ANOVA outcomes—no interaction
9.2 Factorial ANOVA outcomes—with interaction
11.1 Correlation example
11.2 Correlation example—outlier removed
11.3 The Anscombe quartet (adapted from Anscombe 1973)
11.4 Correlation example—restricted range
12.1 Simple linear regression raw data—scatterplot
12.2 Residuals vs. predicted values and independent variable—scatterplot
12.3 Scatterplots exhibiting heteroscedasticity—increasing and decreasing variance
12.4 Scatterplot with line of best fit examples
12.5 Scatterplots implying a need for transformation
12.6 Scatterplot example with outlier
12.7 Example of complete reporting of study results including regression results
Tables
4.1 Example data for zero-sum-deviation property of the mean
4.2 Example data for mode
4.3 Frequency table (absolute) example data (adapted from Ribas 2012, Table 6)
4.4 Frequency table (relative) example data (adapted from Ribas 2012, Table 6)
6.1 One-sample test for the median example data
7.1 Comparing two means: Examples from T&I literature
7.2 Mann-Whitney U-test example data with rank transformation
7.3 Wilcoxon matched-pairs signed-ranks test data with rank transformation
8.1 Summary statistics for elevation example
8.2 One-way ANOVA: Examples from T&I literature
8.3 ANOVA table with formulas
8.4 ANOVA table for elevation example
8.5 Example data adapted from Köpke and Nespoulous (2006)
8.6 Kruskal-Wallis example data
8.7 Repeated measures ANOVA example data
8.8 Repeated measures ANOVA table
8.9 Between-subjects repeated measures ANOVA
8.10 Repeated measures ANOVA table of example data
8.11 Repeated measures ANOVA with corrections for sphericity
8.12 Friedman’s test example
9.1 Independent factorial ANOVA study design
9.2 Sums of squares, mean squares, and F-statistics
9.3 Factorial ANOVA with no significant effects
9.4 Factorial ANOVA with significant main effect
9.5 Adapted from Christoffels and de Groot (2004: 234, Table 1)
10.1 Testing categorical data: Examples from T&I literature
10.2 Chi-squared example data
10.3 Chi-squared example data—expected vs. observed values
10.4 Goodness-of-fit example data
10.5 Goodness-of-fit example data—expected values
11.1 Correlation: Examples from T&I literature
11.2 Correlation matrix example
11.3 Correlation matrix example—incorrect reporting
11.4 Test-retest reliability example data
11.5 Internal reliability example data
11.6 Cohen’s κ example data
11.7 Cohen’s κ example data, κ = 0
12.1 Linear regression: Examples from T&I literature
12.2 ANOVA table—linear regression
12.3 ANOVA table for regression example
12.4 Parameter coefficient estimates
12.5 Individual coefficient t-test example data
Acknowledgements
There are many people we would like to thank for their encouragement and support. We would first like to thank
several friends and colleagues for taking time from their busy schedules to read and comment on various sections of
the volume, including Brian Baer, Loubna Bilali, Jeff Child, Coral De Jesús, Alan Hanson, Adrienne Hollowell,
Katie Lyman, Michael Mellinger, Jonathan Mellinger, Scott St. Amand, Valerie Reed, and Leslie Whetstine. Their
suggestions challenged us to rethink and revise the manuscript. We would also like to extend our thanks to Walsh
University’s Library and Division of Languages and Letters for their support. Sara Laviosa and the anonymous
reviewer of the manuscript deserve our thanks for their insightful comments on the initial proposal, which
undoubtedly improved the final project. Lastly, a heartfelt thank-you to our families for their encouragement and
support throughout the entire process.
Every effort has been made to contact copyright-holders. Please advise the publisher of any errors or omissions,
and these will be corrected in subsequent editions.
Introduction to the volume
As translation and interpreting (T&I) studies evolve, investigators have incorporated novel research designs and
methods to glean new insights on a broad range of research questions. Innovative data collection methods and their
associated samples are drawn from a variety of sources for the purpose of describing specific phenomena and
generalizing findings to larger populations. With the growth of quantitative data generated by new data collection
tools—such as eye-tracking software, keystroke logging, corpus linguistics tools, and audio and video recording—it
is not surprising that scholars have looked to statistical methods to make sense of the data. The science of statistics
provides a wealth of tools and methods that can be used to address and analyze the proliferation of data. Scholars
engage these tools in a number of areas to identify patterns, evaluate relationships and differences, and generate new
research questions.
Yet the inclusion of quantitative data in a field often associated with the humanities has left some scholars without
the requisite foundation to read, interpret, and use statistics in their work. Graduate-level translation and interpreting
programs, traditionally housed in comparative literature programs, do not regularly incorporate quantitative research
methods in their curricula, and opportunities for formal study using empirical methods are scarce. Oakes and Ji
(2012) note, for instance, that corpus-based translation studies have suffered from a lack of quantitative analytical
methods, despite burgeoning attention being placed on corpus studies. O’Brien (2009) describes the proliferation of
data available when conducting eye-tracking studies that can prove unwieldly to the uninitiated. Likewise, Gile
(2016) articulates the need for larger sample sizes in experimental research, though this requirement can prove
problematic when attempting to recruit sufficient samples for T&I studies. Sun (2016) similarly describes growth in
the use of quantitative data derived from survey instruments in the social sciences and its inclusion in translation and
interpreting studies.
Systematic reflection on quantitative research methods has largely been limited to the types of statistical tests and
analyses needed for specific studies and research projects. Some exceptions include articles by Balling (2008),
Steiner (2012), and Balling and Hvelplund (2015), as well as several contributions to Oakes and Ji’s (2012) edited
collection. These works have described specific approaches to various aspects of statistical analysis, but no book-
length treatment on the topic is available. Discipline-specific research methods volumes often mention quantitative
research methods but remain silent on the specifics of statistical analysis, presupposing an understanding on the part
of readers. Consequently, researchers instead turn outside the discipline to related works, such as those by Johnson
(2008), Rasinger (2008), and Turner (2014). While these volumes provide a foundation of quantitative analysis, they
do not recognize the challenges inherent to research in T&I studies, nor do they provide a comprehensive overview
that can address its specificities. This volume endeavors to fill this gap.
This book approaches quantitative research methods with the explicit understanding that T&I studies pose unique
challenges to data collection, analysis, and interpretation. Researchers are often faced with data feast or famine;
either there is an overwhelming amount of data that can be unwieldy for even the seasoned researcher or else a
dearth that leaves the researcher in want of additional data points. Moreover, experimental or quasi-experimental
studies in T&I studies are not easily replicable, given the wide variety of participant and linguistic variables that are
often under consideration. When working with professional translators and interpreters, there are limited participant
pools, and every researcher must compete for their time. Results from these studies aim, in many cases, to generalize
to a limited population (i.e., translators and interpreters), which further complicates research designs and analysis.
Consequently, each situation must be approached based on the constraints imposed by the research design and data
collection method. Therefore, the methods described in this book recognize the importance of the underlying
assumptions of various analytic methods.
Of equal importance to the authors of this book is that its statistical advice is in line with current research in the
field of statistics. Just as the discipline of T&I studies evolves, so too does the body of scholarship in statistics.
Statistical methods are certainly portable across disciplines and domains, yet their constant evolution necessarily
requires past recommendations to be revisited. By way of example, Osborne (2010: 64), in his offer of guidance on
reviewing research that employs correlational analysis, states that several approaches to the calculation of a
particular test are in fact “archaic.” He continues his discussion as follows:
Essentially, before massive computing power was available in every office, these formulae provided computational shortcuts for people computing
