COMM 250 Agenda - Week 10 Housekeeping C2 – Returned to You Today RP1 – Due Today (IM Surveys) TP3a – Due Tomorrow Lecture RAT4 Review RP1 Experiments

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

Correlation & Causality (Review) 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 Independent Variables (IVs) Comm Competence (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

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

Hypotheses (Review) 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.”

In-Class Team Exercise # 8 - Part I First Do as Individuals, then produce a Team Version: 1) Design a Factorial Experiment to answer these questions: Which can be read faster on a web site - plain text (plain black letters on a white background, no links) or text supplemented in some way? What other variables might affect a user’s ability to read text? (Name 3 and then Choose one for Step 2) 2) Draw a table of the design - at least 3 levels of one variable, 2 of another (you choose the second IV) Label the 2 IVs and Label Their Levels 3) Write out 2 Hypotheses (H1, H2): One Predicting Effects of IV 1, the other the Effects of IV 2 4) Declare the DV (It is in your H1, H2) 5) List Two (“People”) Variable you Should “Control for”

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.

In-Class Team Exercise # 8 - Part II Produce a Team Version only: How does talking on a cell phone affect driving? Design a 3 x 2 Factorial Experiment (draw a Table) You Must Use These IVs: Level of Driving Experience (Pick 3 Levels) Type of Distraction (Pick 3: Cell Phone, Changing CDs, You choose #3) Write out 2 Hypotheses (H1, H2): Your DV should be: MPH deviation from the average speed on the road One Predicting the Effects of Driving Experience One Predicting Differences Due to Type of Distraction Label the 2 IVs and Label Their Levels List Two Other Variables you Should “Control for”