Encyclopedia of Measurement in Social Sciences

Professional Fields & Applications This section will provide an overview of traditional social science disciplines, multi-disciplinary fields such as criminology, urban studies, marketing, and spatial economics, as well as examples of transdisciplinary research approaches to study social phenomena. Basic and applied research will be distinguished. There also will be entries to convey some of the important advances to knowledge that social science has made in history. Examples of entries include the following: Anthropology Archaeology Behavioral economics Behavioral psychology Business Clinical psychology Cognitive neuroscience Cognitive psychology Communication Criminology Cultural anthropology Demography Digital humanities Economics Epidemiology Eugenics Geography History History of social science History of social experiments Knowledge creation Law Linguistics Marketing Operations research Organizational psychology Political Science Polling industry Public health Science and the social world Social Work Sociology Spatial econometrics Urban Studies Research Designs The structure and capabilities of various overall study designs will be explained in this section. Entries will cover the full range of experimental and quasi-experimental designs, and the goals and the limitations associated with each. Examples from many fields of study will be provided, including to help illustrate ways in which to overcome difficulties through multiple methods. Entries will likely include the following: Case study Cross-sectional designs Explore, explain, evaluate, & predict: Purposeful research Experimental designs Falsification of theory Field research Goal-based vs. goal-free evaluation Hypothesis tests and proofs Impact and outcome evaluation Laboratory studies Longitudinal cohort designs Longitudinal panel designs Pilot study Process evaluation Quasi-experimental designs Rapid assessment process Research designs, overview Risk and needs assessment Treatment effects Sampling Strategies & Human Subjects This section will address how cases are chosen to be studied, and the ethical, legal, and humanitarian considerations that must be given when “cases” are human beings. In many ways this is what distinguishes social science research. Issues of reliability and validity will be highlighted throughout. Entries will include the following: Access and objectivity Census undercount and adjustment Clustering Confidence intervals Confidentiality & disclosure limitations Ethical issues, overview Field relations Margin for error Non-probability sampling Population vs. sample Randomization Rare events research Research ethics committees and approval process Sample designs Small area estimation Snowball sampling Sample size Spatial sampling Stratified sampling methods Time sampling Units of analysis Weighting Data Collection & Sources Entries will cover the techniques of collecting data from human subjects directly through surveys, observation, and participation, and about subjects through indirect, unobtrusive methods. The extent to which methods of data collection can enhance or diminish the strength of specific research designs will be included. Merits and limitations associated with each method of collecting data will be addressed. Examples of traditional and innovative data collection across fields of social science will be provided. Entries likely will include: Archival and administrative records Social media sources Computer-based testing Computer simulation Computerized record linkage & statistical matching Content source collection Data distribution and cataloguing Ethnography Experimenter effects Expert witness testimony Focus groups Information management Interviews Locational decision making Meta-analysis Neutrality in data collection Non-response bias Observational studies Participant observation Phrase completion scales Polling Primary vs. secondary data Questionnaire construction Recall issues Response bias Survey administration strategies Telephone surveys Unobtrusive methods Several widely used national and international data sets of various types will be profiled in this section. Entries will include hyperlinks and open access designations when available, which will be a major improvement over the initial edition. Potential topics for which data will be described, and access provided when possible include: Agricultural production Built environments Campaign finance Corporate firm value Crime and victimization Educational achievement Environmental discrimination Quality of life Religious affiliation and commitment Safe neighborhoods Tourism Constructs & Variables Entries in this section will explain the ways in which social behavior and values are operationalized in social science research. Entries will cover the various methods and measurement issues associated with all types of quantitative and qualitative variables. Capabilities of handling complex, multi-dimensional constructs through indices and typologies will be identified. The section will include several examples representing various fields of study, prominent indicators in common use, and unique efforts that might provide inspiration for future research. Strengths and limitations will be addressed. Entries will likely include the following topics: Coding variables Correspondence analysis & dual scaling Equivalence Guttman scaling Judgement and choice Likert scale construction & analysis Manifest & latent coding Role of theory Ranking Remote sensing Scales and indexes Spatial scale Thurstone’s scales of primary abilities Typology construction Utility Measuring examples may include: Consumption & saving Discrimination Educational achievement Extremism vs. terrorism Insurance mortality tables Innovative computerized test items Migration Performance prism Psychometrics of intelligence Quality of life Residential segregation Taxation Transportation Models & Methodologies This section will include entries describing many ways in which data are examined. Several advances have occurred in this research phase since publication of the first edition. Once the final list of entries is determined, this section will be divided into subsections progressing from initial to advanced stages of analysis, and distinguishing techniques appropriate for qualitative and qualitative data. Entries will include the following: Aggregation Artificial Intelligence, Data Mining, Machine Learning, and Deep Learning Bayesian statistical analysis Categorial modelling & automatic interaction detection Cognitive mapping Correlation and elaboration Cost-benefit analysis Contingency tables and log-linear models Conversation analysis Cultural consensus model Descriptive & inferential statistics Digital terrain modelling Dynamic terrain modelling Economic forecasting Event history analysis Factor analysis Fixed-effects models Geographic information systems Hazards measurement Heuristics Hierarchical linear models Incidence & prevalence Item response theory Land use mapping Linear logistics test models Linear models Location analysis Log linear models Maximum likelihood estimation Meta-analysis Modelling diffusion processes Multilevel analysis Network analysis Nominal categories models Nonparametric item response theory Ordinary least squares (OLS) Path analysis Probability models for unfolding responses Probit, logit & other binary models Problem-solving methodologies Q methodology Qualitative analyses Regional input-output analysis Spatial pattern analysis Spatial regression models Structural equation models Social interaction models Statistical disclosure control Test equating Time series analysis Time-space modelling Data Interpretation, Quality, & Potential Limitations This section will convey many important considerations for investigators as they conduct research, assess the quality of their data and inference, and determine what their findings truly mean. Although every section will have entries that address potential challenges relevant to the research phase, this section will cover topics relevant to research throughout. Entries will include: Age, period, and cohort effects Alpha reliability Autocorrelation Causal inference Content validity Cross-cultural comparisons Deduction and induction Ecological fallacy Ethnocentrism Generalizability theory Implicit bias Intangible assets Inter-rater reliability Item and test bias Measurement error Missing data: problems and solutions Multicollinearity Objectivity Omitted variable bias Phenomenology Reliability assessment Selection bias Spatial discounting Spatial externalities Split-half reliability Statistical and substantive interpretations Test-retest reliability Theta reliability Type I and Type II error Validity assessment