1. 1 COMM 301: Empirical Research in Communication Kwan M Lee Lect3_1

2. 2 Announcements Office hours Initial survey with email  hand in to TAs Class Activities –Unresolved problems  Ask TA or contact Professor

3. 3 Building good measures: Levels of measurement Things to know by the end of this lecture: –What is measurement? –What are the levels of measurement? –What is the Likert scale? –Why do we care about levels of measurement? –How levels of measurement affects statistical tests?

4. 4 Measurement; Numerals; Numbers Measurement: assignment of numerals to objects according to rules Numerals: symbols (label; category) Numbers: symbols with quantitative meaning. Levels of measurement –Nominal –Ordinal –Interval –Ratio

5. 5 Nominal level of measurement Nominal level of measurement assign a numeral to a variable, without quantifying the numeral –i.e., name Example: biological sex female = 0 male = 1 Note, no sense of quantity: “1” here does not mean more than “0”

6. 6 Nominal level of measurement Criteria for nominal level of measurement –Mutually exclusive Every observation will fit into one and only one of the categories –Exhaustive Every observation have one category in which it may be mapped What pet do you like –Dog; Cat; Fish ……………… Others –Equivalent All categories should be about the same attribute of variable being mapped. –News; Drama; Comedy……….; 30 min; 1 hour

7. 7 Nominal level of measurement: Examples Party affiliation –Dummy variable –Rep 1; Dem 2; Green 3; Independent 4; Other 5 –No rank order above!

8. 8 Ordinal level of measurement Ordinal level of measurement retains the criteria for nominal level –mutually exclusive, exhaustive, equivalent Plus, the numerals are rank ordered from low to high But, there is no assumption of equal spacing between the ranks

9. 9 Ordinal level of measurement Example (ordinal level) –Socio-economic status Low; Middle; High –Top 3 social issues in the USA Note unequal distances in above examples

10. 10 Interval level of measurement Interval level of measurement retains attributes of the ordinal level: –mutually exclusive, exhaustive, equivalent –rank ordered Plus, that the spacing between the ranks are equal And, there is an arbitrary zero point

11. 11 Building good measures: Interval level of measurement Example (interval level) –Fahrenheit scale equal spacing: a change from 60 to 70 is the same as the change from 90 to 100 arbitrary zero: temperature can fall below 0, 0 does not indicate lack of temperature –IQ Note: the distinction b/w ordinal and interval scales are fuzzy in real life

12. 12 Interval level of measurement Implications: –we use numbers at the interval level –we can use mathematics to work with the numbers to get some meaningful results Scales using interval level Likert scale Semantic differential scale In Social Science Research, ordinal scales are usually used as if they were interval. –So in real research, the distinction b/w ordinal and interval is not that important.

13. 13 Interval level of measurement; Examples Likert scale –set of statements assessing (dis)agreement with a 5- or 7-point scale (usually) –Example: Kwan is a good teacher. –Strongly disagree 1 –Disagree 2 –Neutral 3 –Agree4 –Strongly agree5 Kwan is well organized Kwan is not funny –Note: reverse coding –Sum up the response values for all statements, to get a measure of Kwan’s teaching ability

14. 14 Ratio level of measurement Ratio level of measurement retains attributes of the nominal level: –mutually exclusive, exhaustive, equivalent –rank ordered –equal spacing But, instead of an arbitrary 0, it has a true 0 True 0 indicates absence of the phenomenon in question

15. 15 Building good measures: Ratio level of measurement Example (ratio level) –income –age –News recall measure based on 10 items –…

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17. 17 Building good measures: Why do we care about the levels? Levels of measurement determine what sort of statistical analysis you can do later. –Nominal, ordinal levels use a type of statistics –Interval, ratio levels use another type An important point in the research process, need to think through.

18. 18 Building good measures: Why do we care about the levels? Generally, try to use higher level of measurement if possible. –Example: Age Age measured in ratio (number of years): 21, 33, 46, 87 Age measured as ordinal categories –Teenager –Young adult –Middle age adult –Elderly senior

19. 19 Levels of measurement and an introduction to statistical tests Statistical tests determines more precisely if there are relationships between variables. In this course, we will focus on three statistical tests: –Non-parametric statistics: for nominal and ordinal chi-square test –Parametric statistics: for interval and ratio t-test Correlation –Don’t worry about the next slides on the details of each stat tests. We will learn them one by one later in the semester.

20. 20 Introduction to statistical tests Chi-square test Chi-square test –There are several variations of the chi-square test. –In this course, we will focus on one particular variation: comparing frequencies of two or more distinct groups. E.g., composition of a class (Sophomores; Juniors; Seniors) –Independent variable is group membership, measured at the nominal or ordinal level. –Dependent variables are observed frequencies of each nominal or ordinal category. Examples...

21. 21 Introduction to statistical tests T-test T-test –There are three forms of t-tests. –The one we will use the most is the independent samples t-test. –The independent samples t-test compares average (mean) score of one group with the average score of another group. For example, the average GPA of communication majors is compared to the average GPA of engineering majors.

22. 22 Introduction to statistical tests Independent samples t-test Independent samples t-test –Independent variable is group membership, measured at the nominal or ordinal level. Examples … –Dependent variable is measured at the interval or ratio level. Examples … –Particularly appropriate for experimental designs, usable for surveys also. Examples …

23. 23 Introduction to statistical tests Correlation Correlation –Correlation tests if there is a linear relationship between two variables. Examples... –Independent variable is measured at the ordinal, interval, or ratio level. –Dependent variable is measured at the interval, or ratio level.