1. Conceptualization, Operationalization, and Measurement

2. Independent and Dependent Variables
Independent variable is what is manipulated
a treatment or program or cause
‘Factor’
‘Explanatory Variable’
Dependent variable is what is affected by the independent variable
effects or outcomes
‘Measure’
‘Response Variable’

3. Concept versus Construct
Term (nominal definition) that represents an idea that you wish to study;
Represents collections of seemingly related observations and/or experiences   
Concepts as Constructs
We refer to concepts as constructs to recognize their social construction.

4. More on constructs
Three classes of things that social scientists measure:
Directly observable: # of people in a room
Indirectly observable: income
Constructs: creations based on observations; cannot themselves be directly or indirectly observed
Example; You can treat gender as
directly observable (gender presentation)
indirectly observable (check box)
a construct where you develop dimensions and indicators of gender (which then requires much more conceptualization)

5. Conceptualization
The process of conceptualization includes coming to some agreement about the meaning of the concept
In practice you often move back and forth between loose ideas of what you are trying to study and searching for a word that best describes it.
Sometimes you have to “make up” a name to encompass your concept. If you are interested in studying the extent to which people exhibit behaviors that bring together groups, you might come up with the nominal definition “bridge maker.”
As you form the aspects of a concept, you begin to see the dimensions; terms that define subgroups of a concept.
With each dimension, you must decide on indicators; signs of the presence or absence of that dimension. (Dimensions are usually concepts themselves).

6. Operationalizing Choices
The process of creating a definition(s) for a concept that can be observed and measured
The development of specific research procedures that will result in empirical observations
Examples
SES is defined as a combination of income and education and I will measure each by…
The development of questions (or characteristics of data in qualitative work) that will indicate a concept
See example about ‘looking for work’ on page 125

7. Variable Attribute Choices
Variable attributes need to be exhaustive and exclusive
Represent full range of possible variation
Degree of Precision
selection depends on your research interest, but if you’re not sure, it’s better to include more detail than too little
Level of Measurement

8. Level of Measurement Nominal measures
only offer a name or a label for a variable
there is not ranking; they are not numerically related
gender; race

9. Ordinal measures Variables with attributes that can be rank ordered
Can say one response is “more” or “less” than another
Distance between does not have meaning
Scales and indexes are ordinal measures, but conventions for analysis allow us to assume equidistance between attributes. Thus, they are often treated like “interval” measures.

10.  Interval Measures
Distance separating attributes has meaning and is standardized (equidistant)
“0” value does not mean that a variable is not present
For example, elevation and temperature

11. Ratio Measures
attributes of a variable have a “true zero point” that means something
Height and Weight
allows one to create ratios

12. Determining Quality of Measurement
Reliability: The extent to which the same research technique applied again to the same object/subject will give you the same result
Reliability does not ensure accuracy
a measure can be reliable but inaccurate
(invalid) because of bias in the measure or in data collector/coder

13. Example of Reliability Problems
Intercoder reliability
Coders make subjective decision about the presence of violence in a series of ads; “present” or “not present”
We have a reliability problem when more than one coder looks at the same ad and codes it differently.
Solution: Operationalize specifically what “counts” as violence

14. Techniques for Confirming Reliability or Discovering Problems
Test-Retest
Split-half Method
Divide indicators into 2 groups and use 2 surveys
If random selection of respondents and indicators are reliable, then there should be no significant differences between the 2 groups from which data was collected
Use Established Measures
Reliability of data collectors/coders: training, follow up checks, intercoder reliability checks

15. Validity: The extent to which our measure reflects what we think or want them to be measuring

16. Face Validity
The measure seems to be related to what we are interested in finding out even if it does not fully encompass the concept

17. Criterion-related Validity
Predictive Validity
The measure is predictive of some external criterion
Example:
Criterion = Success in College
measure = ACT scores (high criterion validity?) 

18. Construct Validity
The measure is logically related to another variable as you had conceptualized it to be
Example:
Construct = happiness
Measure = financial stability
(if not related to happiness, low construct validity)

19. Content Validity
How much a measure covers a range of meanings? Did you cover the full range of dimensions related to a concept?
Example:
You think that you’re measuring prejudice, but you only ask questions about race.
What about sex, religion, etc.?

20. Internal Validity
Internal validity addresses the "true" causes of the outcomes that you observed in your study.
Strong internal validity means that you not only have reliable measures of your independent and dependent variables BUT a strong justification that causally links your independent variables to your dependent variables.
At the same time, you are able to rule out extraneous variables, or alternative, often unanticipated, causes for your dependent variables.

21. External Validity
External validity addresses the ability to generalize your study to other people and other situations.
To have strong external validity (ideally), you need a probability sample of subjects or respondents drawn using "chance methods" from a clearly defined population.
Ideally, you will have a good sample of groups and a sample of measurements  and situations.
When you have strong external validity, you can generalize to other people and situations with confidence.