Tai ngay!!! Ban co the xoa dong chu nay!!! Introduction to Research Methods To Maia and Ludmilla Antonia and their bright futures Introduction to Research Methods A Hands-On Approach Bora Pajo Mercyhurst University FOR INFORMATION: SAGE Publications, Inc 2455 Teller Road Thousand Oaks, California 91320 E-mail: order@sagepub.com SAGE Publications Ltd Oliver’s Yard 55 City Road London EC1Y 1SP United Kingdom SAGE Publications India Pvt Ltd B 1/I Mohan Cooperative Industrial Area Mathura Road, New Delhi 110 044 India SAGE Publications Asia-Pacific Pte Ltd Church Street #10-04 Samsung Hub Singapore 049483 Copyright © 2018 by SAGE Publications, Inc All rights reserved No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher Printed in the United States of America Library of Congress Cataloging-in-Publication Data Names: Pajo, Bora, author Title: Introduction to research methods : a hands-on approach / Bora Pajo Description: Los Angeles : SAGE, 2017 | Includes bibliographical references and index Identifiers: LCCN 2017021397 | ISBN 9781483386959 (pbk : alk paper) Subjects: LCSH: Social sciences—Research—Methodology Classification: LCC H62 P235 2017 | DDC 001.4/2—dc23 LC record available at https://lccn.loc.gov/2017021397 This book is printed on acid-free paper Acquisitions Editor: Leah Fargotstein Editorial Assistant: Yvonne McDuffee Development Editor: Eve Oettinger eLearning Editor: Laura Kirkhuff Production Editor: Andrew Olson Copy Editor: Janet Ford Typesetter: C&M Digitals (P) Ltd Proofreader: Sarah J Duffy Indexer: Nancy Fulton Cover Designer: Karine Hovsepian Marketing Manager: Shari Countryman Brief Contents Preface Acknowledgments Chapter • The Purpose of Research Research in Action 1.1: Illustration of a Qualitative Study Research in Action 1.2: Illustration of a Quantitative Study Research Workshop 1.1: Complete a Course on Protecting Human Research Participants Chapter • Formulating a Research Question Research Workshop 2.1: An Example of Narrowing Down a Research Interest Ethical Consideration 2.1: Operationalizing Constructs Feature Research Workshop 2.2: How to Identify Control Variables Research in Action 2.1: Illustration of Operationalization of Concepts Chapter • Researching and Writing the Literature Review Research in Action 3.1: Illustration of Annotated Bibliographies Ethical Consideration 3.1: Research Funding Research in Action 3.2: Illustration of the Organization of Literature Research Workshop 3.1: Writing the Literature Review Chapter • Quantitative Designs Research Workshop 4.1: The Advantages and Disadvantages of Cross-Sectional Designs Ethical Consideration 4.1: Informed Consent During a Longitudinal Study Research in Action 4.1: Illustration of an Experimental Design Chapter • Measurement Errors, Reliability, Validity Research Workshop 5.1: How to Minimize Measurement Error Research in Action 5.1: Details on Strengthening a Questionnaire Ethical Consideration 5.1: Important When Selecting the Appropriate Instrument Research Workshop 5.2: How to Select the Perfect Valid and Reliable Instrument for a Study Chapter • Sampling Ethical Consideration 6.1: To Remember When Sampling Research Workshop 6.1: Tips to Remember When Selecting NonProbability Sampling Research in Action 6.1: Two Studies Using Proportionate and Disproportionate Stratified Sampling Research Workshop 6.2: Tips to Remember When Selecting a Probability Sampling Method Chapter • Data Collection for Quantitative Research Research Workshop 7.1: Conducting the Poverty-Simulation Experiment Ethical Consideration 7.1: Code of Research Ethics Research in Action 7.1: The Use of Protocol to Ensure High Validity Research Workshop 7.2: Collecting Data Research Workshop 7.3: Running Your Survey Online Chapter • Secondary Data Research in Action 8.1: Illustration of a Replication of a Previous Study Research Workshop 8.1: Scraping Data Research in Action 8.2: An Illustration of Available Datasets Ethical Consideration 8.1: Study Participants and Secondary Data Chapter • Entering and Organizing Quantitative Data Research Workshop 9.1: Exploring Software Packages Ethical Consideration 9.1: Protecting the Anonymity of Participants Research in Action 9.1: Illustration of a Codebook Chapter 10 • Analyzing Quantitative Data Ethical Consideration 10.1: Representation of Data Research in Action 10.1: Sample Size and Skewness Research Workshop 10.1: How to Find Correlation Using Excel, R, and SPSS Chapter 11 • Qualitative Designs And Data Collection: Understanding What Behavior Means in Context Ethical Consideration 11.1: Field Work Research Workshop 11.1: Entering and Exiting the Field Research Workshop 11.2: Some Good Practices for Conducting an In-Depth Qualitative Interview Research Workshop 11.3: Preparing for Focus Groups Ethical Consideration 11.2: Qualitative Interviewing Research in Action 11.1: Illustration of a Mixed-Method Example Chapter 12 • Entering, Coding, and Analyzing Qualitative Data Research Workshop 12.1: Good Transcription Practices Research in Action 12.1: Illustration of an Interview Transcript Research in Action 12.2: Diagrams Ethical Consideration 12.1: Confidentiality Chapter 13 • Results and Discussion Research in Action 13.1: Reporting Results Research in Action 13.2: Organizing Findings Research Workshop 13.1: Organization of Tables Ethical Consideration 13.1: Misrepresenting Results Research in Action 13.3: Illustration of Organizing the Discussion Research Workshop 13.2: Two Ways of Organizing the Discussion Chapter 14 • Presenting Your Research Ethical Consideration: Accurate Presentations and Anonymity Research Workshop 14.1: Applying to Conferences Research in Action 14.1: Illustration of a Presentation References Name Index Subject Index About the Author standardized survey, 161–162, 175 strengthening, 124–125 surveys vs., 161, 175 Questionnaires, data collection through, 161–170 answer scale for, 169–170, 176 clarity and brevity in, 168 considering all possible answers to questions, 165, 175 contingency questions and, 168–169, 176 developing list of constructs, 162–164, 175 measuring of constructs, 164–165, 175 organization issues, 167–168 pretest run for, 170, 176 wording the questions, 166–167, 175 Questions open-ended, 46–47, 262 socially desirable, 118 Quota sampling, types of, 145–146 R data entry in, 200 (figure) finding correlation in, 244–245 free courses on using, 202 importing Excel spreadsheet into, 201 notations for responses stored in, 210 text-mining of, 288 Random assignment, in classic experimental design, 102–103, 112 Random error, 116–118, 132, 217 Randomization, 139, 152 Randomized one-group posttest-only design, 106–107, 107 (figure), 160 Randomized posttest-only control group design, 107–108, 107 (figure), 160 Range, 216, 226, 229–230, 248 formula for, 229 interquartile, 229, 248 Rapport fieldwork and, 256, 277 interviewing and, 262, 263 Ratio variables, 205, 211, 212 Raw scores, 234 R coefficient, 240, 241 Reactivity, 269 Reading critical, 78–79 transcribed interviews, 284 Reagan, Ronald, 13 Reality, knowledge vs., Reasoning deductive, 14, 23 inductive, 11, 23 Recommendations, 320–321 common types of, 320 for future research, 320, 322 Recording device, in-depth qualitative interviews and, 264 Record keeping, 282, 289 Reference lists, 59–60 Regional conferences, 330, 331, 332, 338 Regression, 238, 245–246, 249 Regression line, 245, 249 Relevance of the problem, publishing your work and, 334–335, 339 Reliability, 120–125 accuracy vs., 121 defined, 121, 132 discussion and, 318 internal consistency, 123, 125, 133 inter-observer or inter-rater, 122, 133 questionnaires tested for, 161, 175 sample size, publishing your work, and, 338 of SDQ, 124–125 test-retest, 122–123, 133 validity vs., 131–132, 131 (figure), 132 (figure) Replication of previous study, 182 secondary data and, 181, 194 Reporting results See Results, reporting Research applied, 29, 49 causality in, 97–100, 112 ethical See Ethical research fundamental, 28–29, 49 funding, 72 idiographic, 99–100, 112 love and passion in, 28 nomothetic, 98–99, 112 peer review and, 332 quasi-experimental, 160–161 reasons for conducting, undergraduate, journals dedicated to, 332, 333 (table) as work of art, 293 See also Presenting your findings to an audience; Publishing an article Research design, characteristics of, 88–89, 111 See also Experimental designs; Quantitative designs Researchers, students as, 54 Research ethics code of, 160 defined, 18 Research in Action annotated bibliographies, 62–64 codebook, 206–208 datasets, 189–190 diagrams, 290 experimental design, 104–105 interview transcript, 286–288 mixed-method, 274–276 operationalization of concepts, 44–45 organization of literature, 75–77 organizing findings, 310 organizing the discussion, 319–320 presentation, 336–337 qualitative study, 12–14 quantitative study, 15–16 replication of a previous study, 182 reporting results, 307–308 sample size and skewness, 236–237 strengthening a questionnaire, 124–125 studies using proportionate and disproportionate stratified sampling, 150–151 use of protocol to ensure high validity, 171 Research methods mixed methods, 10, 23 qualitative research, 10, 11, 23 quantitative research, 10, 11, 14, 17, 23 scientific methods, (figure) Research question formulation, 27–52 choosing a research topic, 28–31 operationalization of constructs, 32–37 types of hypotheses, 42–47 types of variables, 37–42 visualizing research question, 47–49 Research studies analyzing, 79–80 categorizations of, 88–91 conceptualizing purpose of, 88 cross-sectional vs longitudinal studies, 91–97 defending, 54–55 descriptive studies, 90 designing, 88–89 explanatory studies, 90–91 exploratory studies, 89 publishing your work and design of, 335, 339 selecting perfect valid and reliable instrument for, 130–131 Research topic choosing, 28–31 deciding between fundamental or applied research, 28–29 narrowing, 29–31, 30 (figure) Research university data, 186–187 Research Workshop applying to conferences, 331–332 conducting poverty-simulation experiment, 159 cross-sectional designs, advantages/disadvantages of, 93 data collection, 173 entering and exiting the field, 259–260 exploring software packages, 201–202 finding correlation using Excel, R, and SPSS, 244–245 good transcription practices, 283–284 identifying control variables, 39–40 in-depth qualitative interview practices, 264–265 minimizing measurement error, 120 narrowing down a research interest, 31–32 non-probability sampling tips, 146 online surveys, 174 organization of tables, 315 organizing the discussion , outlines for, 321 probability sampling method tips, 152 protecting human research participants, 20 scraping data, 184 selecting perfect valid and reliable instrument, 130–131 writing the literature review, 83 Resonate: Present Visual Stories That Transform Audiences (Duarte), 330 Results misrepresenting, 317 in qualitative studies, 309, 311, 322 Results, reporting, 302–305, 307–308, 322 demographics section, 302–304, 322 findings section, 304–305, 322 Results, visually presenting, 311–317, 322 figures, 315–317, 316 (figure), 322 overview, 311 tables, 312–315, 313–314 (table), 322 Results in quantitative studies, 305–309, 322 failing to reject the null hypothesis, 306, 309 rejecting the null hypothesis, 305–306 Results section outline for, 311 of research paper, 302 Review process, journal publication and, 334 Revolution, scientific, 7–10, (figure) Right-skewed distribution, 235, 237, 248 Right-tailed distribution, 235 Ritalin, 327, 328 Roncaglia, Sara, 311 RQDA package of R, 288 R-studio graphs from, 201 (figure) importing Excel spreadsheet into, 201 Sample, 138, 152 Sample size attrition, panel studies and, 95 publishing your work and, 337–338, 339 skewness and, 236–237 Sampling, 137–155 convenience (or accidental), 141–142, 141 (figure), 153 defining, 137–139, 152 deviant case, 144 disproportionate stratified, 149, 150, 151, 151 (figure), 153 ethical considerations for, 143 homogenous, 144 judgmental, 144 non-probability, tips for, 146 non-probability, types of, 140–146, 153 non-proportional quota, 145–146, 146 (figure) probability, 139–140 probability, types of, 147–152, 153 proportional quota, 145, 145 (figure) proportionate stratified, 149, 149 (figure), 150, 153 purposive, 144–145, 144 (figure) simple random, 147–148, 147 (figure), 153 snowball, 142–143, 143 (figure), 153 stratified random, 148–151, 153 Sampling errors, 153, 217 Sampling frame, 138, 139, 152 SAS (Statistical Analysis System), 170, 199 notations for responses stored in, 210 variable descriptions entered in, 209 Saturation, 153, 259 Scales, psychometric, 210–211 Scatterplots, 240, 241 (figure), 246, 247 (figure) Science normal, 7–8, (figure), 10, 22– 23 revolution of, Scientific community, publishing an article for, 332 Scientific knowledge, basis of, 2–3 Scientific literature, 33 Scientific research, purpose of, 2–4 Scientific revolution, Kuhn’s structure of, 7–10, (figure), 22 Scraping data, 183, 184, 194 SDQ See Strengths and Difficulties Questionnaire Search engines, using, 58–59 Secondary data, 179–195, 272, 278 ambiguity of measurement error, 192, 194 availability of information, 180, 194 benefits of using, 180–181, 183, 194 cost effectiveness of, 183, 194 disadvantages of, 191–193 government statistics, 185 institutional data, 187 large datasets and, 183, 194 major sources of, 184–188 online sources of, 187–188 opportunities for replication, 181, 194 passage of time and, 193, 194 protection of participants, 181, 194 research university data, 186–187 resources for, 188 (table) study participants and, 193 time effectiveness of, 183, 194 uncertainty of constructs, 191–192, 194 Selection bias, systematic reviews vs literature reviews and, 81–82 Selective observation, 21–22, 23 Self-esteem data, 36–37 (table) operationalizing topic of, 34–35 Self-reflection memos, 285, 299 Semi-structured interviews, 277 coding and, 291 conducting, 261–262 SES See Socioeconomic status Silences, face-to-face interviewing and, 263 Simple random sampling, 147–148, 147 (figure) Skewed distributions, 235, 235 (figure), 237, 248 Skewness defined, 235 sample size and, 236–237 Slides, presentations and, 329, 338 Slope, 240, 245 Snowball sampling, 142–143, 143 (figure), 153 Social desirability, 118, 166 Socially desirable questions, avoiding on questionnaires, 166, 175 Social Media Mining with R (Danneman and Heimann), 184 Socioeconomic status (SES), alcohol use and, 247–248 Sociology, 256 Software packages, 199–202, 200 (figure), 201 (figure) Solomon four-group experimental design, 105, 106 (figure) Sounds, trace data and, 270–271 Sources, formulating research question and, 32–33 Specialization, 276 Spreadsheets, 211 compatibility issues with, 201 logical formatting and, 199 SPSS (Statistical Package for Social Science), 170, 199 absolute frequency table in, 218 (table) data entry in, 200 (figure) finding correlation in, 244–245 free courses on using, 202 notations for responses stored in, 210 percentage and cumulative percent in, 219 (table) variable descriptions entered in, 209 Spurious relationship, 99, 112, 243 Standard deviation, 226, 230, 248 calculating, 231 (table) continuous variables and, 238 formula for, 232 standard scores and, 233 Standardized survey questionnaire, 161–162, 175 Standard scores, 232–234 Stata, 170, 199, 210 Statistical discovery software, 199 Statistics, 216 coordinate system in, 223 descriptive, 217, 248 inferential, 217 measures of central tendency in, 226 Still images, trace data and, 270 Storytelling, in presentations, 327–328, 338 Strata, 148, 153 Stratified random sampling, 148–151, 153 Strengths and Difficulties Questionnaire (SDQ), reliability of, 124–125 Structured interviews, 261, 277 Structure of Scientific Revolution, The (Kuhn), Studies See Research studies Study participants protecting anonymity of, 203 secondary data and ethical considerations, 193 secondary data and protection of, 181, 194 Subjective thinking, 21, 21 (figure), 23 SurveyMonkey, 198 Survey on Household Income and Wealth (Italy), 186 Surveys internal consistency reliability and, 123 online, 174 questionnaires vs., 161, 175 Swedish National Data Service, 186 Systematic error, 118–119, 132 Systematic random sampling, 153 Systematic reviews assessment and analyses of studies and, 82–83 literature reviews vs., 81–83, 84 Tables example of, 313–314 (table) missing value and, 312–313 organization of, 315 presenting your findings to an audience and, 329 uses for, 312, 322 Technical terms, avoiding on questionnaires, 166 Technology Entertainment and Design (TED), 33 TED NPR, 33 Testing group, 19 Test-retest reliability, 122–123, 133 Textual material, trace data and, 271–272 Themes, 292, 299 Theoretical memos, 285 Theoretical thinking, qualitative research and, 297, 299 Theories falsifiability, 5–7 of knowledge, 4–10 of migration, 28–29 Thick description, 253, 256, 277, 278 Thinking critically, qualitative research and, 298, 299, 300 Thinking theoretically, qualitative research and, 297, 299 Time effectiveness, of secondary data, 183, 194 Time order, nomothetic causality and, 98, 99, 112 Tone of voice, presentations and, 329, 330 Topics ranking, in your presentation to an audience, 327, 338 for research, choosing, 28–31 of the study, recommendations on, 320, 321 Trace data moving images: video, 271, 278 physical objects, 269–270, 278 sounds, 270–271, 278 still images, 270, 278 texts, 271–272, 278 Traces, 268, 278 Traditional knowledge, 2, (figure) Transcription, 282–284 confidentiality and, 292 good practices for, 283–284 ground rules for, 283 interview, 265, 271, 286–288, 299 Trend studies, 95–97 True zero point, 205, 212 Tuskegee Institute, 20 Tuskegee syphilis study, 20–21 Twitter scraping data from, 184 secondary data and, 187 Udacity, 202, 219 Udemy, 202 Undergraduate research, journals dedicated to, 332, 333 (table), 339 Unimodal mode, 228 Unique identification numbers, assigning in data entry, 203, 211 United States Public Health Service, 20 Univariate analysis, 216–219, 217, 248 defined, 216 frequency distributions, 218–219 University conferences, 330, 332 University libraries, 59 University of Michigan, Panel Study of Income Dynamics, 95, 186 Unobtrusive data collection, 268–269, 278 Unstructured interviews, 261, 277 U.S Census Bureau, 149 Validity, 125–132 coding and, 291 concurrent, 129, 130, 133 construct, 127–128, 133 content, 127, 133 criterion, 129, 133 defined, 125, 132 discussion and, 318 face, 127, 133, 162 high, protocol and, 171 predictive, 129, 130, 133 questionnaires tested for, 161, 175 reliability vs., 131–132, 131 (figure), 132 (figure) sample size, publishing your work, and, 338 Variability, 228–229 defined, 228 measures of, 230–232, 248 Variables categorical, 204, 211, 237–238 causal relationship between, 98 conceptualization of, 72 confounding or intervening, 40, 49 continuous, 238 control, 38–39, 49 defined, 37 dependent, 38, 49, 246 discrete, 238 disturbance or extraneous, 41, 50 independent, 38, 49, 246 interval, 205, 211, 212 mediators, 42, 50 moderators, 41–42, 50 nominal (or qualitative), 203, 210, 211 numerical (or quantitative), 204, 210, 211 ordinal, 204, 210, 211 path diagrams and, 315, 316, 316 (figure) quantitative studies, 305 ratio, 205, 211, 212 types of, 37–42, 49 values of, 34 See also Constructs Variables, organizing and inputting, 209–211 notations for responses, 210 scales, 210–211 variable descriptions, 209–210 variable types, 210 Variance, 226, 230, 231, 248 calculating, 231 (table) formula for, 232 Video data, 271 Virtual data collection, 174, 176 Visual aids, presenting findings to audience and, 329, 338 Visualization of data, 311–317, 322 figures, 315–316 path diagrams, 315, 316, 316 (figure) tables, 312–315, 313–314 (table) Visualizing research question, 47–49 Voice, presenting your findings to an audience and, 329, 330 Waves, in longitudinal studies, 94 Web Scraping With Excel (Phillips), 184 Web Scraping With Python (Mitchell), 184 Website Scraping and Tools for Beginners (Izland), 184 “Wink” example (Geertz), 253–254 Writing standards, journal publication and, 334 Writing style, publishing your work and, 335, 339 Writing thoughts on paper, power of, 47, 47 (photo) Written text, trace data and, 271–272 x-axis, 222 y-axis, 222, 245 z scores, 232, 248 About the Author Bora Pajo is a social scientist at Mercyhurst University, Department of Applied Sociology and Social Work Her research focuses on the daily dynamics surrounding children diagnosed with emotional and behavioral problems and their parents Bora is passionate about data science and machine-learning algorithms and strongly believes that we can only advance our scientific knowledge if we have high-quality data available She is dedicated to teaching social research methods and statistical analyses in the hopes of transmitting her enthusiasm of conducting scientific research studies