INFO 271B LECTURE 2 COYE CHESHIRE Foundations of Research

Administrative Stuff Info 271B 2 Software  Intercooled vs. small stata  Stata MP Readings Data and Assignments  Course Datasets  Your own data  Easily obtainable data (ICPSR/Roper/GSS/etc)

Info 271B 3 Brief Background: Epistemology and Strategies of Inquiry

Positivism and Humanism Info 271B 4 Positivism  “The truth is out there, we can find it”  If we systematically study humans using a scientific approach, we can learn patterns and commonalities.  Human behavior can be explained in terms of causes and effects. Humanism  Humans create meaning, thus science is inappropriate for studying humans  Deals with moral questions– right and wrong.  Often embraces subjectivity and unique human experiences

Different Strategies of Inquiry Info 271B 5 Quantitative  Instrument-based questions  Statistical analysis  Surveys, Experiments Qualitative  Emergent methods  Open-ended questions  Interviews, Case Studies, Ethnographies Mixed-Methods Approaches  Both quantitative and qualitative methods used

Why quantitative research? Info 271B 6 Standardized methodologies  Statistical techniques are public  Like any science, the methods of research can (and should be) disclosed so that anyone can duplicate your findings Forces the investigator to think about the measurement of key factors (i.e., variables)

Foundations of Quantitative Research: Variables and Measurement Info 271B 7

Constructs and Variables Info 271B 8 Variables  Something we can measure  Concrete measured expressions to which we can assign numeric values Constructs  Concepts, often complex  Not directly measurable  Also called ‘theoretical variables’

Linking Constructs and Variables Info 271B 9 Being NiceLife Happiness ????

Conceptual and Operational Definitions Info 271B 10 Conceptual Definitions  Abstractions that facilitate understanding Operational Definitions  How to measure a conceptual variable

Operationalization Info 271B 11 Concept: “Emotional State”

Measurement Info 271B 12 How could we operationalize…  Age?  “Intelligence”?  How efficient is interface X? Status AGEINCOME

Qualitative/Quantitative Measures and Operationalization Info 271B 13 Note Bernard’s example (p ) of parental aspirations and children's career aspirations  What does this kind of example tell us about research design?

Operationalization Info 271B 14 For any operational definition, there are a few important things to keep in mind:  What is the unit of analysis?  Be able to justify your operational definition (i.e., don’t make arbitrary decisions)

Measurement: Variables Info 271B 15 Independent Variable (X)  Also called predictor variables, or right- hand side variables (RHS)  Those that the researcher manipulates  Attributes or potential causes under investigation in a given study Dependent Variable (Y)  Also called outcome variable, or left- hand side variables (LHS) X Y y = mx + b

Info 271B 16 Time spent playing Game X Observed ‘violent acts’ Over time Y

Types of Variables Info 271B 17 Nominal  Categorical  Dichotomous, Binary, Dummy Variables  Qualitative Variables Ordinal  Rank Variables Metric  Interval Variables  Ratio Variables

Nominal Variables Info 271B 18  Binary/dichotomous  Example: Gender, event occurred or did not occur, etc.  When coded as 0/1, also called ‘dummy variables’  Nominal/non-ordered polytomous  Example: Employment Status 1= Employed 2= Unemployed 3= Retired Three New Dummy Variables: Employed (0,1) Unemployed (0,1) Retired (0,1)

Ordinal Variables Info 271B 19  Ordered polytomous  Example: Likert scales  Any ordered, categorical variable where the distance between categories may not be equal and meaningful

Metric Variables Info 271B 20  Interval  Distance between attributes has meaning  Example: Celsius temperature, “likert-scale” questions  Ratio  Distance between attributes has meaning, and there can be a meaningful zero.  Example: Kelvin temperature, Count variables

21 Info 271B Time spent Exercising between Time 1 and Time 2 Difference in Weight Scores between Time1 And Time 2 Gender (Male =1, Female =2) Scale 1-5 of attitude About Presidential Candidate Ethnic Identity (10 Racial Types) Owns and iPod or not

Next Week: Info 271B 22 Preparing for Research Defining Problems for Research