importance of survey research design

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The Importance of Survey Research Standards

Jack e fincham , phd, jolaine r draugalis , phd.

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Corresponding Author: Jack E. Fincham, PhD, Professor, School of Pharmacy, Adjunct Professor, Henry W. Bloch School of Management, The University of Missouri Kansas City, 4246 Health Sciences Bldg, 2464 Charlotte Street, Kansas City, MO 64108. Tel: 815-235-5909. E-mail: [email protected]

Corresponding author.

Received 2012 Sep 27; Accepted 2012 Nov 4.

Every discipline within fields of research has instituted guidelines and templates for research endeavors and subsequent publications of findings, with the ultimate result being an increase in quality and acceptance by researchers within and across disciplines. These significant efforts are by nature ongoing, as well they should. These enhancements and guideline developments have been instituted in basic science disciplines, clinical pharmacy, and pharmacy administration relevant and related to subsequent scholarly publication of research findings. Specific research endeavors have included bench research, clinical trials and randomized clinical trials, meta analyses, outcomes research, and large scale database analyses. A similar need for quality and standardization also exists for survey research and scholarship. The purpose of this paper is to clarify why this is important and crucial for the Journal and our academy.

INTRODUCTION

In the Research Standards section of Instructions to Authors ( http://archive.ajpe.org/instructions.asp ), the Journal provides guidelines for authors to consider when preparing a manuscript for submission to the Journal . These standards are important for a number of reasons, and may be seen as unique and groundbreaking with regard to other academic health professions journals. This paper is intended to add clarity to this sometimes controversial set of Journal guidelines.

Whether referring to sampling texts such as Cochran’s Sampling Techniques , 3rd edition, 1 or Kish’s Survey Sampling , 2 or using guidelines or tables generated based on these classics as found in Krejcie and Morgan, 3 Salant and Dillman, 4 Bartlett and colleagues, 5 and Dillman, 6 the researcher will find that small populations require a high number of data elements (ie, high response rates) to confidently generalize results because of the potential for sampling error. The recommended minimum sample size for a study depends upon desired confidence level (typically 95%) and how varied the population is with respect to the variable(s) of interest.

Using the conservative approach of a 50/50 split (in other words, an equal chance of one response versus another) on a dichotomous variable of interest at the conventional 95% confidence level for a population of 100, we would need a sample of 80 to ensure a sampling error of no more than +/- 5% at the 95% confidence level. For a population of 100, if a response rate of 50% was achieved for an item with a simple yes/no answer (eg, “Do you have a full-time biostatistician employed by the college?”) and responses were evenly split (50% yes and 50% no), it would not be prudent to extrapolate those findings to the overarching population (100) because the range of possible true percentages would be 25%-75% (that is, all, some, or none of the 50 nonrespondents could have a biostatistician at their college.) 7 (p55)

For a variable with a smaller standard deviation in response to a survey item, say an 80/20 split (eg, 80% agree, 20% disagree), a sample size of only 71 (rather than 80) would be required to maintain the same precision as in the previous example, ie, a 95% confidence level. However, according to Salant and Dillman, 4 (p55) “unless we know the split ahead of time, it is best to be conservative and use 50/50.” Continuous data sets may not require as many data points, however, “if a categorical variable will play a primary role in data analyses…the categorical sample size formulas should be used.” 5(p46) To estimate the sample size required for a continuous variable would necessitate a measure of variability in the population, which may not be easily discerned, thus “the sample size for the proportion is frequently preferred.” 8(p4) As well, “the effect of nonresponse on one variable can be very different than for others in the same survey.” 7 (p54)

Others have simply called for a census in small populations, again necessitating high response rates. 8,9 These considerations supported the rationale for the expectations set forth in the Viewpoint by Fincham. 10

There are 129 doctor of pharmacy degree programs in academic pharmacy in 1 of 3 classifications of accreditation: 109 full accreditation, 15 candidates, and 5 precandidates. 11 The recommended sample size for N=129 at +/- 5% sampling error and 95% confidence level is 97, or a 75% response rate for a 50/50 split. Modeling on a variable with an 80/20 split (ie, less variability in the population) would result in a recommended sample size of 85 or a 66% response rate. Because of the increase in the number of colleges and schools of pharmacy in the United States, the Journal will now accept a 70% response rate threshold for those survey projects collecting data on multiple variable types with the intent of generalizing results to the entire population.

The paper by Draugalis and Plaza 12 provides several examples of the importance of striving for a census and how much confidence readers would have in a published study with a data set with less than optimal response rates, including the annual AACP Faculty Salary Survey. As an example of the potential effects of nonresponse on specific variables in a study, consider the following from a published study on career planning and preparation strategies of pharmacy deans. 13 The subjects were 53 “new” deans with less than 5 years’ experience and 40 “experienced” deans previously in the database with greater than 5 years’ experience, for a cohort of 93 sitting permanent deans (ie, acting and interim deans were excluded) in 2009. Descriptive findings were presented for the total cohort as well as for separate groups on a number of variables when contrasts were desired. “Newly named deans spent an average of 17.1 +/- 8.7 years in the professoriate prior to assuming their first deanship, compared with established deans who had spent an average of 19.0 +/- 5.1 years ( p = 0.006).” If just 3 of the new dean respondents with no or few years in the professoriate had not participated in the study, the mean would have increased to 18.1, the comparison would not have been significant, and an important finding would have been missed. In the career path ladder variable, 9 of the 53 new deans fell in the nontraditional category. If any number of these subjects had actually been nonrespondents, and the closer to actually all 9 of them not participating, this would have skewed descriptive findings and obscured longitudinal comparisons.

High response rates to a research survey do not ensure the validity of the findings as there are other potential sources of error to consider. While attaining a high response rate is a necessary first step, it is not sufficient in and of itself. The specific research question determines the acceptable research methods. For example, in some inquiries, a survey of all colleges and schools of pharmacy may not be necessary or desirable. Depending on the research question, interviews or focus groups may be useful, but the results cannot be generalized to all institutions. Some projects may be intended to gather information only from certain types of institutions, such as private entities, or programs affiliated with a health sciences center. A demonstration project with descriptive findings may be useful to others and in a sense, the argument would be for a methodological development, with the method being generalizable and useful to others, but not the specific institutional findings pertinent to their research. Also, the accepted tools of modeling and decision analytic methods may be appropriate alternatives.

IMPORTANCE OF RESEARCH GUIDELINES AND STANDARDS

In several other research arenas, standards for research methods have been proposed, implemented, and well accepted. Other journals have set standards for research and publications appearing in such. In the 1990s, an international collaboration set in motion a process whereby research standards were developed to enhance the quality and validity of results from clinical trials. A thorough scrutiny of refereed journals accessed through MEDLINE, Embase, Cochrane Central, and associated reference lists was accomplished, and then experts determined the CONSORT checklist, which was subsequently proven to improve the methodology, quality, and external validity aspects of reports of randomized clinical trials. 14,15

Similarly a checklist has been published for qualitative research in hopes of promoting explicit, comprehensive reporting of such research. 16 A Canadian group has proposed developing a survey reporting guideline for health research beginning in 2013 (David Moher, Director, Evidence-based Practice Centre, University of Ottawa, Canada, personal communication, May 17, 2012).

The EQUATOR network (the resource center for good reporting of health research studies) also has been developed to address and make recommendations dealing with the “growing evidence demonstrating widespread deficiencies in the reporting of health research studies.” 17 The EQUATOR Web site provides a list of collected tools and guidelines available for assessing health research issues ( www.equator-network.org ).

Poor reporting guidelines lead to subsequent deficient outcome segments in written summaries of research. Bennett and colleagues have summarized this problem as follows: “There is limited guidance and no consensus regarding the optimal reporting of survey research. As in other areas of research poor reporting compromises both transparency and reliability, which are fundamental tenets of research.” 18 (p.8)

In addressing their concerns over established response rates, Mészáros and colleagues 19 point to the Journal of Dental Education and Academic Medicine as similar publications to the Journal that do not specify response rate criteria. Actually, the issue of response rates has been addressed repeatedly and specifically in these journals. As early as 1983, Creswell and Kuster 20 writing in the Journal of Dental Education noted that at that juncture, 40% of papers published over the previous 5 years were survey studies. Thirty years ago, they called for increased diligence in assessing appropriate sample sizes, adequate attention paid to survey response rates, and greater effort in improving the quality of survey-related research in the Journal of Dental Education.

In 2009, in an excellent analysis of survey research issues in the Journal of Dental Education , Chambers and Licari suggest that: “Evidence that is not grounded in theory is just data. There is a natural pull on the authors of surveys to interpret their findings as supporting policies or positions they favor.” 21(p288) The authors also speak to the importance of adequate response rates: “…that the precision of any claim based on a survey is strongly affected by sample size.” 21(p294) The authors point to sample saturation as a technique to reduce the impact of bias in surveys. This technique directly addresses the response rate issue by noting that the larger the sample size and the higher the response rate, the more accuracy can be attributed to the study results. A built in assumption is that even unknown missing data adversely affect the conclusions of the analyses. Subsequently, even contrary results that may have potentially come from the nonrespondents would result in a less likely scenario. In effect, the results would be different from what was obtained from the analyses of the data in hand.

Response rates matter a great deal, and this point has been made in the Journal of Dental Education over a 30-year period. The issue is not that the Journal of Dental Education has chosen not to develop standards for survey research papers, but rather that the American Journal of Pharmaceutical Education has taken a leadership role in this regard.

Although it is true that Academic Medicine does not explicitly list an acceptable response rate, the October 2011 issue provided summary guidance for survey research published in their journal. 22 In this excellent summary of good research practices relative to survey design and reporting, 5 references are listed. 23-27 These seminal references provide explicit information regarding sampling, research design, response rates and associated problems with biases, and acceptability indices in other components of survey research. In one of these “gold standard” references, Krosnick notes that: “It is important to recognize the inherent limitations of nonprobability sampling methods and to draw conclusions about populations or differences between populations tentatively when nonprobability sampling methods are used.” 25(p541) This point becomes even more significant when low response rates are achieved in nonprobability samples.

GUIDELINES AND STANDARDS AS A QUALITY CONTROL MECHANISM

Setting standards and suggesting guidelines are in no way a move on the part of the Journal editors to stifle research or unfairly limit the reporting of research findings; nor are they intended in any manner to arbitrarily curtail creativity. Many fine survey research papers are published in the Journal and contribute to the academy. There are simply no published studies that have pointed out the negative impact of such standard-setting processes on the research endeavors related to clinical, health services research, or sociological research.

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Encyclopedia of Survey Research Methods

• Edited by: Paul J. Lavrakas
• Publisher: Sage Publications, Inc.
• Publication year: 2008
• Online pub date: January 01, 2011
• Discipline: Anthropology
• Methods: Sampling , Survey research , Data collection , Response rates , Random sampling
• DOI: https:// doi. org/10.4135/9781412963947
• Keywords: cell phones , errors , estimates , exit poll , households , polls , population , surveying , telephones Show all Show less
• Print ISBN: 9781412918084
• Online ISBN: 9781412963947
• Buy the book icon link

Reader's guide

Entries a-z, subject index.

To the uninformed, surveys appear to be an easy type of research to design and conduct, but when students and professionals delve deeper, they encounter the vast complexities that the range and practice of survey methods present. To complicate matters, technology has rapidly affected the way surveys can be conducted; today, surveys are conducted via cell phone, the Internet, email, interactive voice response, and other technology-based modes. Thus, students, researchers, and professionals need both a comprehensive understanding of these complexities and a revised set of tools to meet the challenges. In conjunction with top survey researchers around the world and with Nielsen Media Research serving as the corporate sponsor, the Encyclopedia of Survey Research Methods presents state-of-the-art information and methodological examples from the field of survey research. Although there are other 'how-to' guides and references texts on survey research, none are as comprehensive as this encyclopedia, nor do they present the material in such a focused and approachable manner. With more than 600 entries, this resource uses a Total Survey Error perspective which considers all aspects of possible survey error from a cost//benefit standpoint. Key Features Covers all major facets of survey research methodology, from selecting the sample design and the sampling frame, designing and pretesting the questionnaire, data collection, and data coding, to the thorny issues surrounding diminishing response rates, confidentiality, privacy, informed consent and other ethical issues, data weighting, and data analyses Presents a Reader s Guide to organize entries around themes or specific topics and easily guide users to areas of interest Offers cross-referenced terms, brief listing of further readings, and stable website URLs following most entries Provides appendices that include a general bibliography to build on 'Further Readings,' an annotated list of organizations relevant to survey research, and sample survey designs and actual instruments to further offer the user guidance in designing his or her own research The Encyclopedia of Survey Research Methods is specifically written to appeal to beginning, intermediate, and advanced students, practitioners, researchers, consultants, and consumers of survey-based information.

Front Matter

• Editorial Board
• List Of Entries
• Reader's Guide
• About The General Editor
• Contributors
• Introduction

Reader’s Guide

• Beneficence
• Cell Suppression
• Certificate of Confidentiality
• Common Rule
• Confidentiality
• Consent Form
• Disclosure Limitation
• Ethical Principles
• Falsification
• Informed Consent
• Institutional Review Board (IRB)
• Minimal Risk
• Perturbation Methods
• Protection of Human Subjects
• Respondent Debriefing
• Survey Ethics
• Voluntary Participation
• Conversational Interviewing
• Dependent Interviewing
• Interviewer Effects
• Interviewer Neutrality
• Interviewer Variance
• Interviewer-Related Error
• Nondirective Probing
• Standardized Survey Interviewing
• Verbatim Responses
• Mode Effects
• Mode-Related Error
• Aided Recall
• Aided Recognition
• Attitude Measurement
• Attitude Strength
• Aural Communication
• Balanced Question
• Behavioral Question
• Bipolar Scale
• Bogus Question
• Check All That Apply
• Closed-Ended Question
• Cognitive Interviewing
• Construct Validity
• Context Effect
• Contingency Question
• Demographic Measure
• Dependent Variable
• Don't Knows (DKs)
• Double Negative
• Double-Barreled Question
• Drop-Down Menus
• Event History Calendar
• Factorial Survey Method (Rossi's Method)
• Feeling Thermometer
• Forced Choice
• Gestalt Psychology
• Graphical Language
• Guttman Scale
• Item Order Randomization
• Item Response Theory
• Knowledge Question
• Language Translations
• Likert Scale
• List-Experiment Technique
• Mail Questionnaire
• Mutually Exclusive
• Open-Ended Question
• Paired Comparison Technique
• Precoded Question
• Psychographic Measure
• Question Order Effects
• Question Stem
• Questionnaire
• Questionnaire Design
• Questionnaire Length
• Questionnaire-Related Error
• Radio Buttons
• Random Order
• Random Start
• Randomized Response
• Reference Period
• Response Alternatives
• Response Order Effects
• Self-Administered Questionnaire
• Self-Reported Measure
• Semantic Differential Technique
• Sensitive Topics
• Step-Ladder Question
• Unaided Recall
• Unbalanced Question
• Unfolding Question
• Vignette Question
• Visual Communication
• Acquiescence Response Bias
• Behavior Coding
• Cognitive Aspects of Survey Methodology (CASM)
• Comprehension
• Extreme Response Style
• Key Informant
• Misreporting
• Nonattitude
• Nondifferentiation
• Overreporting
• Panel Conditioning
• Panel Fatigue
• Positivity Bias
• Primacy Effect
• Recency Effect
• Record Check
• Respondent Burden
• Respondent Fatigue
• Respondent-Related Error
• Response Bias
• Response Latency
• Reverse Record Check
• Satisficing
• Social Desirability
• Telescoping
• Underreporting
• Coder Variance
• Content Analysis
• Field Coding
• Focus Group
• Intercoder Reliability
• Interrater Reliability
• Interval Measure
• Level of Measurement
• Litigation Surveys
• Measurement Error
• Nominal Measure
• Ordinal Measure
• Ratio Measure
• Reliability
• Replication
• Missing Data
• Nonresponse
• Completed Interview
• Completion Rate
• Contact Rate
• Contactability
• Cooperation Rate
• Final Dispositions
• Hang-Up During Introduction (HUDI)
• Household Refusal
• Language Barrier
• Noncontact Rate
• Noncontacts
• Noncooperation Rate
• Nonresidential
• Nonresponse Rates
• Number Changed
• Out of Order
• Out of Sample
• Partial Completion
• Refusal Rate
• Respondent Refusal
• Response Rates
• Standard Definitions
• Temporary Dispositions
• Unable to Participate
• Unavailable Respondent
• Unknown Eligibility
• Unlisted Household
• Advance Contact
• Contingent Incentives
• Controlled Access
• Cooperation
• Differential Attrition
• Differential Nonresponse
• Economic Exchange Theory
• Fallback Statements
• Ignorable Nonresponse
• Leverage-Saliency Theory
• Noncontingent Incentives
• Nonignorable Nonresponse
• Nonresponse Bias
• Nonresponse Error
• Refusal Avoidance
• Refusal Avoidance Training (RAT)
• Refusal Conversion
• Refusal Report Form (RRF)
• Response Propensity
• Social Exchange Theory
• Social Isolation
• Total Design Method (TDM)
• Unit Nonresponse
• Advance Letter
• Bilingual Interviewing
• Data Management
• Dispositions
• Field Director
• Field Period
• Mode of Data Collection
• Multi-Level Integrated Database Approach (MIDA)
• Paper-and-Pencil Interviewing (PAPI)
• Quality Control
• Reinterview
• Research Management
• Sample Management
• Sample Replicates
• Survey Costs
• Technology-Based Training
• Verification
• Video Computer-Assisted Self-Interviewing (VCASI)
• Audio Computer-Assisted Self-Interviewing (ACASI)
• Case-Control Study
• Computer-Assisted Personal Interviewing (CAPI)
• Computer-Assisted Self-Interviewing (CASI)
• Computerized Self-Administered Questionnaires (CSAQ)
• Control Sheet
• Face-to-Face Interviewing
• Residence Rules
• Interviewer
• Interviewer Characteristics
• Interviewer Debriefing
• Interviewer Monitoring
• Interviewer Monitoring Form (IMF)
• Interviewer Productivity
• Interviewer Training
• Interviewing
• Nonverbal Behavior
• Respondent-Interviewer Rapport
• Role Playing
• Training Packet
• Usability Testing
• Cover Letter
• Disk by Mail
• Mail Survey
• Access Lines
• Answering Machine Messages
• Call Forwarding
• Call Screening
• Calling Rules
• Computer-Assisted Telephone Interviewing (CATI)
• Do-Not-Call (DNC) Registries
• Federal Communications Commission (FCC) Regulations
• Federal Trade Commission (FTC) Regulations
• Inbound Calling
• Interactive Voice Response (IVR)
• Listed Number
• Matched Number
• Nontelephone Household
• Number Portability
• Number Verification
• Outbound Calling
• Predictive Dialing
• Privacy Manager
• Research Call Center
• Reverse Directory
• Suffix Banks
• Supervisor-to-interviewer Ratio
• Telephone Consumer Protection Act 1991
• Telephone Penetration
• Telephone Surveys
• Touchtone Data Entry
• Unmatched Number
• Unpublished Number
• Videophone Interviewing
• Voice over Internet Protocol (VoIP) and the Virtual Computer-Assisted Telephone Interview (CATI) Facility
• ABC News/Washington Post Poll
• Approval Ratings
• Bandwagon and Underdog Effects
• Call-in Polls
• Computerized-Response Audience Polling (CRAP)
• Convention Bounce
• Deliberative Poll
• Election Night Projections
• Election Polls
• Favorability Ratings
• Horse Race Journalism
• Leaning Voters
• Likely Voter
• Media Polls
• Methods Box
• National Council on Public Polls (NCPP)
• National Election Pool (NEP)
• National Election Studies (NES)
• New York Times/CBS News Poll
• Polling Review Board (PRB)
• Pre-Election Polls
• Pre-Primary Polls
• Precision Journalism
• Prior Restraint
• Probable Electorate
• Pseudo-Polls
• Rolling Averages
• Sample Precinct
• Self-Selected Listener Opinion Poll (SLOP)
• Straw Polls
• Subgroup Analysis
• Tracking Polls
• Trend Analysis
• Trial Heat Question
• Undecided Voters
• Agenda Setting
• Consumer Sentiment Index
• Issue Definition (Framing)
• Knowledge Gap
• Mass Beliefs
• Opinion Norms
• Opinion Question
• Perception Question
• Political Knowledge
• Public Opinion
• Public Opinion Research
• Quality of Life Indicators
• Question Wording as Discourse Indicators
• Social Capital
• Spiral of Silence
• Third-Person Effect
• Topic Saliency
• Trust in Government
• Adaptive Sampling
• Add-a-Digit Sampling
• Address-Based Sampling
• Area Probability Sample
• Capture-Recapture Sampling
• Cell Phone Only Household
• Cell Phone Sampling
• Cluster Sample
• Complex Sample Surveys
• Convenience Sampling
• Coverage Error
• Cross-Sectional Survey Design
• Cutoff Sampling
• Designated Respondent
• Directory Sampling
• Disproportionate Allocation to Strata
• Dual-Frame Sampling
• Duplication
• Eligibility
• Email Survey
• EPSEM Sample
• Equal Probability of Selection
• Error of Nonobservation
• Errors of Commission
• Errors of Omission
• Establishment Survey
• External Validity
• Field Survey
• Finite Population
• Geographic Screening
• Hagan and Collier Selection Method
• Half-Open Interval
• Internet Pop-Up Polls
• Internet Surveys
• Interpenetrated Design
• Inverse Sampling
• Kish Selection Method
• Last-Birthday Selection
• List Sampling
• List-Assisted Sampling
• Log-in Polls
• Longitudinal Studies
• Mall Intercept Survey
• Mitofsky-Waksberg Sampling
• Multi-Mode Surveys
• Multi-Stage Sample
• Multiple-Frame Sampling
• Multiplicity Sampling
• Network Sampling
• Neyman Allocation
• Noncoverage
• Nonprobability Sampling
• Nonsampling Error
• Optimal Allocation
• Overcoverage
• Panel Survey
• Population of Inference
• Population of Interest
• Post-Stratification
• Primary Sampling Unit (PSU)
• Probability of Selection
• Probability Proportional to Size (PPS) Sampling
• Probability Sample
• Propensity Scores
• Propensity-Weighted Web Survey
• Proportional Allocation to Strata
• Proxy Respondent
• Purposive Sample
• Quota Sampling
• Random Sampling
• Random-Digit Dialing (RDD)
• Ranked-Set Sampling (RSS)
• Rare Populations
• Registration-Based Sampling (RBS)
• Repeated Cross-Sectional Design
• Replacement
• Representative Sample
• Research Design
• Respondent-Driven Sampling (RDS)
• Reverse Directory Sampling
• Rotating Panel Design
• Sample Design
• Sample Size
• Sampling Fraction
• Sampling Frame
• Sampling Interval
• Sampling Pool
• Sampling Without Replacement
• Self-Selected Sample
• Self-Selection Bias
• Sequential Sampling
• Simple Random Sample
• Small Area Estimation
• Snowball Sampling
• Stratified Sampling
• Superpopulation
• Systematic Sampling
• Target Population
• Telephone Households
• Troldahl-Carter-Bryant Respondent Selection Method
• Undercoverage
• Unit Coverage
• Unit of Observation
• Within-Unit Coverage
• Within-Unit Coverage Error
• Within-Unit Selection
• Zero-Number Banks
• American Association for Public Opinion Research (AAPOR)
• American Community Survey (ACS)
• American Statistical Association Section on Survey Research Methods (ASA-SRMS)
• Behavioral Risk Factor Surveillance System (BRFSS)
• Bureau of Labor Statistics (BLS)
• Cochran, W. G.
• Council for Marketing and Opinion Research (CMOR)
• Council of American Survey Research Organizations (CASRO)
• Crossley, Archibald
• Current Population Survey (CPS)
• Gallup Poll
• Gallup, George
• General Social Survey (GSS)
• Hansen, Morris
• Institute for Social Research (ISR)
• International Field Directors and Technologies Conference (IFD&TC)
• International Journal of Public Opinion Research (IJPOR)
• International Social Survey Programme (ISSP)
• Joint Program in Survey Methodology (JPSM)
• Journal of Official Statistics (JOS)
• Kish, Leslie
• National Health and Nutrition Examination Survey (NHANES)
• National Health Interview Survey (NHIS)
• National Household Education Surveys (NHES) Program
• National Opinion Research Center (NORC)
• Pew Research Center
• Public Opinion Quarterly (POQ)
• Roper Center for Public Opinion Research
• Roper, Elmo
• Sheatsley, Paul
• Statistics Canada
• Survey Methodology
• Survey Sponsor
• Telemarketing
• U.S. Bureau of the Census
• World Association for Public Opinion Research (WAPOR)
• Alpha, Significance Level of Test
• Alternative Hypothesis
• Analysis of Variance (ANOVA)
• Attenuation
• Auxiliary Variable
• Balanced Repeated Replication (BRR)
• Bootstrapping
• Composite Estimation
• Confidence Interval
• Confidence Level
• Contingency Table
• Control Group
• Correlation
• Cronbach's Alpha
• Cross-Sectional Data
• Data Swapping
• Design Effects (deff)
• Design-Based Estimation
• Ecological Fallacy
• Effective Sample Size
• Experimental Design
• Factorial Design
• Finite Population Correction (fpc) Factor
• Frequency Distribution
• Hot-Deck Imputation
• Independent Variable
• Interaction Effect
• Internal Validity
• Interval Estimate
• Intracluster Homogeneity
• Jackknife Variance Estimation
• Level of Analysis
• Main Effect
• Margin of Error (MOE)
• Mean Square Error
• Model-Based Estimation
• Multiple Imputation
• Noncausal Covariation
• Null Hypothesis
• Panel Data Analysis
• Percentage Frequency Distribution
• Point Estimate
• Population Parameter
• Post-Survey Adjustments
• Probability
• Random Assignment
• Random Error
• Recoded Variable
• Regression Analysis
• Relative Frequency
• Replicate Methods for Variance Estimation
• Research Hypothesis
• Research Question
• Sampling Bias
• Sampling Error
• Sampling Variance
• Seam Effect
• Significance Level
• Solomon Four-Group Design
• Standard Error
• Standard Error of the Mean
• Statistical Package for the Social Sciences (SPSS)
• Statistical Power
• Systematic Error
• Taylor Series Linearization
• Test-Retest Reliability
• Total Survey Error (TSE)
• Type I Error
• Type II Error
• Unbiased Statistic
• Variance Estimation

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