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

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

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

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

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

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

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

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

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”

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

(Possible) 2 x 2 Factorial Design Hypotheses 1. The higher one’s CC, the better liked one is. 2. 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

2 x 2 Factorial Design - Example IVs: Comm Competence, Gender DV: Income Subjects: 20 per cell Control for: Age, Education, Location FemaleMale Low Comm Competence 20 High Comm Competence 20

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 FemaleMale Low Comm Competence WOMEN10 High Comm Competence WOMEN10 Low Comm Competence MEN10 High Comm Competence MEN10

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

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”

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.”

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

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.

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.

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

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?

Descriptive Statistics Measures of Central Tendency  MeanThe Average  MediumThe Middle Score  ModeThe Most Common Score

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)