Research Terminology forThe 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