1. 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 1)Developing Concepts 2)Operationalization 3)Selection of Research Method(s) 4)Sampling Strategy 5)Data Collection ‘Plan’ 6)Analyses 7)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  Concept: 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