COMM 250 Agenda - Week 10

1. COMM 250 Agenda - Week 10 Housekeeping C2 - Due Today (Put in Folders) RAT 5 – Next Wed. RP2 – Nov. 12 (the day before my b-day! :) Lecture Experiments ITE 10

2. Review:Exercise in Coding Open-ended Responses A Review of Issues with Open-ended Items Advantages: • Avoids “Framing” an Issue, Eliciting Particular Responses • Reveals Issues/Repsonses the Researcher Would Have Missed Disadvantages: • Time Consuming to Code • Difficult to Categorize Some Responses Typically Used: • To Get a Preliminary Look at an Issue • To Ensure Unprompted Responses

3. Review:The Research Process Conceptualization • Start with / Develop a Theory and Hypotheses Planning & Designing Research • Selecting Variables of Interest (IV, DV, Control vars) • Operationalize all Variables (i.e., How to measure the vars?) • Design a Study to Test Hypotheses Methods for Conducting Research • Plan the Study and Collect the Data Analyzing & Interpreting Data • Run Statistics and Interpret Results Re-Conceptualization • Back to the Drawing Board

4. Experimental Research Purpose • To Control Variables (in order) • To Attribute the Effects to the IV; that is, • To Infer Causality Types of Experiments • Pre-Exp. - Typically no Comparison Group • Quasi-Exp. - IV is manipulated OR Observed, NO Random Assignment of Subjects • Full Experiments - IV is “manipulated,” Random Assignment of Subjects

5. Experimental Research (continued) Experimenters Create Situations . . . • to Control Variables (in order to . . .) • to Attribute Observable Effects to the IV; that is . . . • to Infer Causality Control by Exposing Subjects to an IV • Manipulating (exposure to) an IV (the “Active Var.”) • Observing (exposure to) an IV (the “Attribute Var.”) Control by “Ruling Out" Initial Differences • Random Assignment • Pretests

6. Review:Correlation & Causality Correlation • Two variables are related (as one varies, the other varies predictably) Causation 3 “Necessary & Sufficient” Conditions: • Two variables must be shown to be related • The IV must precede the DV in Time • The relationship cannot be due to another “extraneous” variable

7. Experimental Designs Pre-Experiments (“Pseudo-Experiments”) 1-Group, Posttest Only • Produces a Single Score • E.g.: Exam in School 1-Group, Pretest-Posttest • Produces a Difference Score • E.g.: Evaluation of Corporate Training Non-Equivalent Groups, Posttest Only • Also Called “Static Group Comparison” • No Random Assignment to Groups • E.g.: Comparing Test Scores for a Training Class to a Group Who Did Not Take the Training

8. Experimental Designs Quasi-Experiments (“Field Experiments”) 1-Group, Time Series Design • Series of Pretests (Baseline) Treatment  Series of Posttests • E.g.: Monitoring the Effects of Blood Pressure Medicine • Problems: Sensitization, Sleeper Effect, No Comparison Group Quasi-Equivalent Groups, Pretest-Posttest • Non-Random Assignment to (Treatment, Control) Groups • Produces a Difference Score • E.g.: Study of College Classes • Problems: Equivalence (History, etc.) Quasi-Equivalent Groups, (Multiple) Time Series Design • Combines the Two Designs Above • Problems: Sensitization, Equivalence, Sleeper Effect

9. Experimental Designs Full Experiments Equivalent Groups, Pretest-Posttest • Equivalence = Random Assignment of Subjects to Groups • Experiments Provide Control; Reveal Causality (in the Lab) • E.g.: Testing a New Chemotherapy Drug Equivalent Groups, Posttest Only • Relies on the Random Assignment • Initial Differences COULD Cause Any Observed Effect • E.g.: Lab Study of New Messaging System Solomon Four-Group • Combines the Two Designs Above • Checks for Pretest (Sensitization) Effects • Checks Whether Random Assignment “Worked”

10. Experimental Designs Factorial Designs • Multiple IVs (“Factors”); Typically One DV • Can Be Pre-, Quasi-, or Full Experiments • Most Common: Quasi- and Full • Most Common: Posttest Only Examples – H1: The more competent at comm, the higher income one earns. 2x2 Factorial Design • IVs: Comm Competence (Lo, Hi); Gender (F, M) • DV: Income 3x2x2 Factorial Design • IVs: Competence (L, M, H); Gender (F, M); Occup (BC, WC) • DV: Income

11. (Possible) 2 x 2 Factorial Design Hypotheses • The higher one’s CC, the better liked one is. • Women are better liked than men. Independent Variables (IVs) • Comm Competence (“CC”) (measured as Hi / Lo) • Gender (M / F) Dependent Variable (DV) • Likability Score (could have others) Control Variable • (Positive/Negative) Attitude

12. 2 x 2 Factorial Design - Example • IVs: Comm Competence, Gender • DV: Income • Subjects: 20 per cell • Control for: Age, Education, Location

13. 2 x 2 x 2 Factorial Design - Example • IVs: CC, Gender of Sender, Observer Gender • DV: Income • Subjects: 10 per cell • Control for: Age, Education, Location

14. Experimental Research (Review) Experimenters Create Situations . . . • to Control Variables (in order to . . .) • to Attribute Observable Effects to the IV; that is . . . • to Infer Causality Control by Exposing Subjects to an IV • Manipulating (exposure to) an IV (the “Active Var.”) • Observing (exposure to) an IV (the “Attribute Var.”) Control by “Ruling Out" Initial Differences • Random Assignment • Pretests

15. In-Class Team Exercise # 10 - Part I: Design a 3 x 2 Factorial Experiment (draw a Table) You Must Use These IVs: • Group Size (Use 3 Levels, S, M, L, but choose the # in each) • Type of Conferencing (Pick 2: Audio, Video, Text, Chat, FtF) Write out 2 Hypotheses (H1, H2): H1: One Predicting the Effect of Group Size on Group Consensus H2: One Predicting the Effect of Type of Conferencing on Group Consensus Declare the DV (You Choose – They Are in Your H1, H2) • E.g., User Satisfaction, Quality of Solution, Time Efficiency Label the 2 IVs and Label Their Levels List (at least) 2 Variables you Should “Control for”

16. Review:Hypotheses Two-Tailed Hypotheses • Non-directional – researcher predicts a relationship, but does not specify the nature • “Comm Competence is related to Annual Income.” One-Tailed Hypotheses • Directional – researcher predicts both a relationship AND the direction of it • “The more Competent one’s Comm, the higher one’s Annual Income.”

17. Review:Variables of Interest Independent – influences another variable • IV = “Predictor” variable Dependent – variable influenced by another • DV = “Outcome” variable Control – variable one tries to control for • Could “keep constant,” balance across groups, or extract in the statistical analysis • Control Var = “Concomitant” variable

18. Extraneous Variables Intervening Var – explains relation bet IV, DV • “The  a Person’s Comm Competence (CC) (the IV), the  the Salary (the DV).” • Since Competence, per se, doesn’t get you $, “Job Function” is an Intervening Var.

19. Extraneous Variables (continued) Confounding Var – obscure effects • “Surpressor” Var. reduces the effect of an IV • CC could  # of Friends, but also  difficulty of chosen job, which in turn  time for friends. • “Reinforcer” Var. increases the effect of an IV • CC could  # of Friends, but also  # of events one attends, which in turn would further  # of friends. Lurking Var – explains both IV and DV • Perhaps the var “Extroversion” affects both CC and # of Friends.

20. Statistics Descriptive Statistics: • a way to summarize data Inferential Statistics: • strategies for estimating population characteristics from data gathered on a sample

21. Descriptive Statistics Measures of Central Tendency • Used to describe similarities among scores • What number best describes the entire distribution? Measures of Dispersion • Used to describe differences among scores • How much do scores vary?

22. Descriptive Statistics Measures of Central Tendency • Mean The Average • Medium The Middle Score • Mode The Most Common Score

23. Measures of Dispersion • Range • The Highest & Lowest Scores • Variance • A Measure Of Dispersion Equal To The Average Distance Of The Scores, Squared, From The Mean Of All Scores, Divided By N • Standard Deviation • The Square Root Of The Variance (Dispersion About The Mean, Based In The Original Units)