INFO 271B LECTURE 2

COYE CHESHIRE

Foundations of Research

Administrative Stuff

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Software Intercooled vs. small stata Stata MP

Readings

Data and Assignments Course Datasets Your own data Easily obtainable data (ICPSR/Roper/GSS/etc)

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Brief Background: Epistemology and Strategies of Inquiry

Positivism and Humanism

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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

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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?

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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

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Constructs and Variables

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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

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Being Nice Life Happiness

? ? ? ?

Conceptual and Operational Definitions

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Conceptual Definitions Abstractions that

facilitate understanding

Operational Definitions How to measure a

conceptual variable

Operationalization

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Concept: “Emotional State”

Measurement

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How could we operationalize…

Age?

“Intelligence”?

How efficient is interface X?

Status

AGE INCOME

Qualitative/Quantitative Measures and Operationalization

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Note Bernard’s example (p. 39-40) of parental aspirations and children's career aspirations

What does this kind of example tell us about research design?

Operationalization

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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

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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

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Time spent playing Game X

Observed ‘violent acts’Over time Y

Types of Variables

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Nominal Categorical Dichotomous, Binary,

Dummy Variables Qualitative Variables

Ordinal Rank Variables

Metric Interval Variables Ratio Variables

Nominal Variables

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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

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Ordered polytomous

Example: Likert scales

Any ordered, categorical variable where the distance between categories may not be equal and meaningful

Metric Variables

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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

21Info

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Time spentExercising betweenTime 1 and Time 2

Difference in Weight Scores between Time1

And Time 2

Gender(Male =1,

Female =2)

Scale 1-5 of attitudeAbout Presidential

Candidate

Ethnic Identity(10 Racial Types)

Owns and iPod or not

Next Week:

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Preparing for ResearchDefining Problems for Research