1 1 Research Methods in AD/PR COMM 420 Section 8 Tuesday / Thursday 3:35 pm -5:30 pm 143 Stuckeman Nan Yu 2007 Fall_COMM 420_Week NY

2 A review Operational Definitions Variables

3 Operational Definition Answers the question: How do we know? It translates the concept into simplified terms that can be measured.

4 Operational Definition (cont.) How can an abstract concept be transformed into a concrete variable that can be studied (measured)? How can it be empirically defined?

5 Operational Definition (cont.) Academic Performance Final grades Term papers Class participation

6 Operational definition (cont.) attention eye movement response speed recall

7 Operationalization Links concepts to variables Abstract Concepts Concrete Variables

8 Relationships Between Variables One way to operationalize each concept: Intelligence (Concept X) Academic performance (Concept Y) IQ score (Variable X) GPA (Variable Y) Theory Hypothesis

9 IV and DV independent variable predictor (cause) variable antecedent variable dependent variable criterion (effect) variable IQ score (Variable X) GPA (Variable Y) Hypothesis

10 Intervening Variable Comes in between the IV and DV Usually offers an explanation of why or how the IV affects the DV E.g., Exposure to TV commercials leads to changes in attitude towards the advertised product, which in turn lead to product sales. Exposure to TV commercials Changes in attitude towards advertised product Product sales IV DV Intervening Variable

11 Rules IV can be manipulated or measured. DV are always measured.

12 Example IV: Brand image (manipulated) DV: Purchase intention (measured)

13 Example (cont.) IV: Body type (manipulated) DV: Perceived body type (measured)

14 How do we measure variables?

15 Measurements 2007 Fall_COMM 420_Week NY length weight Amount of fluid temperature

Fall_COMM 420_Week NY How do we measure human behaviors?

17 What are measurements? 2007 Fall_COMM 420_Week NY The process of assigning numbers to observations according to specified rules.

18 Levels of Measurement: Nominal (Categorical) Nominal (Categorical) Differentiated based on type or category Very commonly seen in demographic data: Gender (1=male, 2=female) Political party affiliation (1=democrat, 2=republican, 3=independent).

19 Nominal (cont.)

20 Nominal (Categorical) Categories are mutually exclusive Categories are exhaustive, otherwise they will not represent the variable fully E.g., One individual case should fit in at least one category

21 Levels of Measurement: Ordinal Ordinal: values can be rank ordered. Social status (1=low, 2=middle, 3=high) Household incomes 1. Less than $ 20, $20,001 to $30, $30,001 to $45, $45,001 to $60, $60,001 to $80, $80,001 to $100, More than $100,000 Ordinal measures provide orders (2 is more than 1), but do not tell how much apart the values in different categories are.

22 Levels of Measurement: Interval If distances between categories are equal, we can say it interval variable. We assume that the distance between a 1 and 2 is the same as the distance between 2 and 3.

23 Levels of Measurement: Ratio Same attributes as interval variables, plus a meaningful zero point. Height: 6-foot man is twice as tall as a 3-foot boy. Age: 20-year-old boy is twice as old as a 10-year-old boy. Temperature: 100 degree is twice as hot as 50 degree Time: 30 minutes is three times as long as 10 minutes. Ration measures mean that a 2 is twice as much of something.

24 Levels of Measurement (cont.)

25 Level of Measurements (cont.) Interval and ratio variables continuous variables. more precise more powerful statistical analysis could be applied

26 Scales of Measurement: Likert-type Scale (p ) Indicate on the scale below, how strongly you agree or disagree with the statement: Listening to heavy metal music makes one prone to violent acts. __Strongly agree __Agree __Neutral __Disagree __Strongly disagree OR Listening to heavy metal music makes one prone to violent acts. Strongly disagree Neutral Strongly agree

27 Likert-type scale (cont.)

28 Likert-type scale (cont.)

29 Semantic Differential (Bipolar) Similar to Likert-type scales, but involves pairs of attributes based on which the respondent will judge something. E.g., Indicate on the scale below, circling only one dot, how you feel about the web site you have just seen: Organized * * * * * * * Unorganized Confusing * * * * * * * Not Confusing

30 Semantic Differential Scales (cont.)

31 Thurstone Scales Generating Potential Scale Items.

32 Thurstone Scales (cont.) Rating the Scale Items.

33 Thurstone Scale Now, you have to select the final statements for your scale. You should select statements that are at equal intervals across the range of medians.

34 Open-ended questions What is your age? How long have you been using Internet? Provide ideas about how to develop measurements. Often used during interviews or focus groups.

35 Other scales Guttman Scales Thermometer Scaling Multidimensional Scaling Unobtrusive measures

36 Unit of Measurement/Observation unit of measurement (observation) Smallest unit used for purposes of observation (e.g., individual, group, news story, etc.).

37 Unite of Analysis unit of analysis = Smallest unit used for data analysis.

38 Example E.g., In a survey, we ask (measure) the annual income of each individual in a household, but in the analysis we might be interested in the average income per household. What is the unit of observation/analysis in this case?

39 Decisions in the Measurement Process Any existing measures of the variables of interests? What is the unit of observation (individuals, social groups, stories, etc.)? Who measures (self-report, other-report, researcher, etc.) whom (sampling)? What levels/types of measurements do you use (e.g. interval? Likert-type)?

40 Measurement Error All measurements are subject to error Two types of measurement error: Systematic error (due to instrument calibration) Constant and predictable Random error Erratic and unpredictable

41 Pilot test (pre-test) A test before the formal research is conducted Test the stimulus Test the measurement instruments Eliminate potential problems Reduce predictable errors It ’ s like a mini test to make sure everything goes well as you expected. The sample could be a lot smaller than the actual sample. But you can not include the same person in both.

42 In-class Demo Please go to this website survey1/survey1.html survey1/survey1.html Down load the file “ in-class demo ”

43 In-class Demo (cont.) Click and answer all questions that are on the questionnaire. Identify the level of measurement of each PART Submit your answers to ANGEL (week 4, drop box for in-class Demo)