CJ 526 Statistical Analysis Research methods and statistics

Assumptions 1. there is order in nature 2. every event has an explanation 3. we will never know everything

Terminology Variable: any trait or characteristic which can take on a range of values Hypothesis: Question or statement about the relationship between two or more variables i.e., is there a relationship between number of police on the streets and the crime rate? Between taking medication and improvement?

Terms Independent variable (IV): a variable thought to have an effect Dependent variable (DV): affected variable

Terms Value: Individual characteristics within the set of the variable Constant: Characteristic that does not change in value Extraneous (EV): Variable that can affect DV, but one that the researcher is not interested in

Examples Gender of people in treatment Variable: Gender Values: Female, male Social class of patients Variable: Social class Values: Lower, lower middle, middle, upper middle, upper

Examples of values of variables Intelligence tests scores Variable: Intelligence Values: Range from 50 to 160 Number of prior felony convictions Variable: Prior felony convictions Range from 0 to...

Constants--example Study of serial killers Gender (Gender would apply to some medical conditions) Race i.e., sickle cell anemia among African Americans—hold race constant

Independent Variable: Home health visits for at risk mothers Constant: at-risk Dependent Variables: Cognitive development, rate of health problems, abuse, school performance, criminal history Extraneous Variables: State of the economy, other available services

Terms Units of analysis: units observed and described to create summary descriptions of all units and to explain differences among them Individuals Groups (i.e., families, gangs) Organizations (hospitals) Social artifacts (traffic accidents, court cases, prison riots, patient cases)

Terms Theory: an explanation that systematically organizes observations and hypotheses Basic vs. Applied Research Basic--why questions; Applied--solve problems Cross-sectional vs. Longitudinal Research Experimental vs. Ex Post Facto Research

Types of research methods Experiments (manipulation and control) Surveys (written and interviews) Field or observational research Record or archival research (content analysis, secondary analysis) Case study Evaluation research

Steps in research Choosing a research problem Reviewing the literature: abstracts and journals, books, collected readings, computer searches (NCJRS), CD ROMS, and the internet Conceptualization of variables, hypotheses, questions

Steps in research Selecting how to measure variables (operationalization) Selecting subjects for the study: population and sample Method: making observations and measurements Data processing and analysis

Steps in research (continued) Interpreting the results and their applications

Why study statistics? 1.Enhanced Informed Decision Making 2.More Knowledgeable Consumers of Research 3.Better Producers of Research Two Types of Statistical Techniques Descriptive and Inferential

Statistical techniques to organize and summarize data Types of Descriptive Statistics: Average score Variability Tables and graphs Strength of relationship (correlation)

Inferential Statistics Statistical techniques that allow one to draw conclusions about a larger group based on results from some portion of it Typical Uses for Inferential Statistical Techniques

Examples of inferential statistics 1.Program Effectiveness 1.Short-term and long-term effectiveness of prison boot camp programs 2.Group differences Do males and females differ in terms of response to a drug? 3.Theory testing 4.Prediction 1.Bail decision making