Research Terminology for The Social Sciences

 Data is a collection of observations  Observations have associated attributes  These attributes are variables  A collection of data is often called a “data set”  What are variables?  A measure that takes different values for different observations  Across a population (cross-sectional)  Across time (cross-temporal)  Both! (Panel data)  Independent/explanatory variables are variables we think have an effect on other variables  Control variables are a special category  Dependent/outcome variables are the variables we are trying to explain or predict What is Data?

 Features of variables  Take on some set of values  Different values have different meanings  Could be numerical, meaning they have number values attached  Continuous  Discrete or Limited  Could be categorical, meaning they have descriptive terms attached  Ordinal (the categories have numerical ranks associated with them)  Typological (the categories are descriptive and do not represent some ordering/ranking/values) Unpacking Variables

 Determine what kind of data will be needed based upon your research question  Quantitative?  Large-N  Measurable in a clear and consistent way  Qualitative?  Case studies  Not easily quantifiable  The Holy Grail of Social Science Research: Turning Quantitative Data into Qualitative Measures Research Design

 Libraries have a large collection of data sets that are ready to be used, in common software formats  Digital Centers have software suites for all steps of data collection process  Bibliographic packages  Data management software  Data analysis software  Reference librarians are useful resources for discovery  Sometimes, you may need to collect original data  Field work: going out and gathering data from observations  Archival work: finding the data in other information sources and aggregating it into a data set Collecting Data

 Operationalization is the process of turning theoretical concepts into measurements  Matching theory with variables  Ideological framework  The type of problem should suggest an appropriate measure  Matching levels  Macro vs. micro, and everything in between  Matching observations  Individuals? Pairs? Groups?  Matching meanings  This is the hardest Operationalization

 Models are statements about the way variables related to one another  Two basic types in social science: analytical and formal  Analytical Models  Describe the causal relationships between variables  Rely upon probability and statistics  Formal Models  Describe a simplified version of reality  Variables become elements of this simplified reality  Rely upon theoretical frameworks  Both types of models can be tested with data Using Variables

 Mixed methods analysis is the “gold standard”  Combination of quantitative and qualitative data  Formal models  Mathematical representations of decisions  Game theory  Matching the research design to the hypothesis under investigation is critical  How questions are asked and answered  What counts as evidence? Research Methods

 Descriptive Statistics  These are measures designed to help you “picture” your data  Means, Medians, Modes  Standard Deviations, Variances  Exploratory Visualization  These are graphs that depict visually information contained in descriptive statistics  Distribution plots  Histograms  Density plots  Simple correlation plots  Graphing two variables, one on each axis (i.e., X & Y)  You can get more complicated later! Discovering the Data

 Simple inferences  Correlations/covariances  These measures show the relationships between and among variables  Commonly referred to as ANOVA – ANalysis of VAriance  ANOVA is about comparing two (or more) samples, groups, populations  Basic Linear Models  These models explore  Simple regression: one dependent variable, one independent variable  This is really just a correlation  Multivariate regression: one dependent variable, many independent variables  This technique looks at simultaneous correlations among several variables Analyzing the Data

 Models for non-continuous/limited/discrete variables  Logit and probit models: the dependent variable can take two values  Tobit models: the dependent variable can take a set of values  Ordered logit, ordered probit, and multinomial logit models: the dependent variable can take a small and discrete set of values  Models for complex data  Simultaneous equations models (SEMs): the dependent variable can also effect the independent variable  Instrumental variables are a technique used to deal with this issue  Time-series and panel data models  The data cover multiple years and may have serial correlations (i.e., the values for one year are highly correlated with values from the previous year)  Non-linear models  The relationships between the variables are not of the form Y= mX + B Advanced Data Analysis