1. Designing Research Concepts, Hypotheses, and Measurement
Chapter 3
Designing Research Concepts, Hypotheses, and Measurement

2. Research Design Must create a Research Design
Questions are composed of concepts
Must start with a research question

3. Stages of Research Developing Concepts Operationalization
Selection of Research Method(s)
Sampling Strategy
Data Collection ‘Plan’
Analyses
Results and Writing
Also need to consider budget issues

4. Operationalization
It is critical to survey research to understand how to go from ideas to concepts to variables – operationalization.

5. Concepts
Concept (p.35): an idea, a general mental formulation summarizing specific occurrences
A label we put on a phenomenon, a matter, a “thing” that enables us to link separate observations, make generalizations, communicate and inherit ideas.
Concepts can be concrete, abstract, tangible or intangible. 
Concrete: Height, Major
Abstract: Happiness, Love

6. Transferring Concepts into something Measurable
Variable:
A representation of concept in its variation of degree, varieties or occurrence.
A characteristic of a thing that can assume varying degrees or values.
Fixed meaning = constant
Most variables are truly variable = multiple categories or variables

7. Example: Concept and Variable
Political participation
Variables:
Voted or not
How many times a person has voted
What party a person votes for

8. How to be measured?
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.

9. Conceptualization
As you flush out the pieces or aspects of a concept, you begin to see the dimensions; the 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.

10. Operationalizing Choices
You must operationalize: process of converting concepts into measurable terms
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
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

11. 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
Is it better to include too much or too little?

12. Variables
The dependent variable is the variable that the researcher measures; it is called a dependent variable because it depends upon (is caused by) the independent variable.
The independent variable is the one that the researcher manipulates.
Example: If you are studying the effects of a new educational program on student achievement, the program is the independent variable and your measures of achievement are the dependent ones.

13. Variables
Qualitative Variable: Composed of categories which are not comparable in terms of magnitude
Quantitative Variable: Can be ordered with respect to magnitude on some dimension  
Continuous Variable: A quantitative variable, which can be measured with an arbitrary degree of precision. Any two points on a scale of a continuous variable have an infinite number of values in between. It is generally measured.
Discrete Variable: A quantitative variable where values can differ only by well-defined steps with no intermediate values possible. It is generally counted.

14. Level of Measurement
Nominal
Ordinal
Interval
Ratio

15. Nominal Measures Only offer a name or a label for a variable
There is not ranking
They are not numerically related
Gender; Race

16. 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
lower class, middle and upper class
Note: Scales and indexes are ordinal measures, but conventions for analysis allow us to assume equidistance between attributes (if it makes logical sense); treat them like “interval” measures; and subject them to statistical tests

17. Interval Measures
Distance separating attributes has meaning and is standardized (equidistant)
“0” value does not mean a variable is not present
Score on an ACT test 50 vs. 100
does not mean person is twice as smart

18. Ratio Measures
Attributes of a variable have a “true zero point” that means something
Waist measures and Biceps measures
Allows one to create ratios

19. Hypotheses
Hypotheses: (pg. 36) Untested statements that specify a relationship between 2 or more variables.
Example: Milk Drinkers Make Better Lovers

20. Characteristics of a Hypothesis
States a relationship between two or more variables
Is stated affirmatively (not as a question)
Can be tested with empirical evidence
Most useful when it makes a comparison
States how multiple variables are related
Theory or underlying logic of the relationship makes sense

21. Hypotheses should be clearly stated at the beginning of a study.
Do not have to have a hypothesis to conduct research, general research questions.

22. Positive and Negative (Inverse) Relationships
Positive: as values of independent variable increase, the values of the dependent variable increase
Negative: as values of independent variable increase, the values of the dependent variable decrease (or vice versa)

23. Two-directional Hypotheses
More general expression of a hypothesis
Usually default in stat packages
Suggests that groups are different or concepts related, but without specifying the exact direction of the difference
Example: Men and women trust UK security differently.

24. One-directional hypotheses
More specific expression of a hypothesis
Specifies the precise direction of the relationship between the dependent and independent variables.
Example: Women have greater trust in UK security compared to men.

25. Determining Quality of Measurement
Accuracy and Consistency in Measurement
Validity is accuracy
Reliability is consistency

26. Reliability
Definition -- 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

27. Validity
Definition -- The extent to which our measure reflects what we think or want them to be measuring

28. Face Validity
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
concept = intellectual capacity
measure = grades (high face validity)
measure = # of close friends (low face validity)

29. Criterion Validity
Criterion validity (predictive validity): the measure is predictive of some external criterion
Criterion = Success in College
Measure = ACT scores (high criterion validity?)

30. Construct Validity
Construct Validity: the measure is logically related to another variable as conceptualized it to be
construct = happiness
measure = financial stability
if not related to happiness, low construct validity

31. Content Validity
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 are measuring prejudice, but you only ask questions about race
what about sex, religious etc.?

32. Methodological Approaches, Reliability and Validity
Qualitative research methods lend themselves to high validity and lower reliability.
Quantitative research methods lend themselves to lower validity and higher reliability