COMM 250 Agenda - Week 4

1
COMM 250 Agenda - Week 4

• Housekeeping
• Team Pictures
• Team Issues
• Team Rosters (?)
• TP1, TP2
• Lecture
• RAT 1
• The Craft of Research
• Variables
• Research Questions Hypotheses
• Operationalizing Variables

2
In-Class Team Exercise 1 - Part I

• Paradigms Paradigm Shifts
• Deliverable list 6 major paradigm shifts in
  society
• (List Both the Old and New Paradigms)
• Additional Team Work
• Choose a team name
• TP1 Meet as a Team
• TP2 Peer Evaluation PC

3
The Research Process

• Conceptualization
• Start with / Develop a Theory and Develop
  Hypotheses
• Planning Designing Research
• Operationalize all Variables
• i.e., How will you measure each variable? (must
  be precise!)
• 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
4 Types of Variables

• Independent influences another variable
• IV Predictor variable
• Dependent variable influenced by another
• DV Outcome variable
• Control variable one tries to control for
• Either keep constant, balance across groups,
  or extract in the statistical analysis (aka a
  concomitant variable)
• Extraneous variable not studied/interested in
• But it has some impact on the IVDV relationship

5
4 Levels of Variables

• Nominal simply categories for classification
  the numbers assigned are meaningless
• Political Party, Gender, County
• Ordinal levels are in a rank order
• US News Top 100 Grad Schools
• Lettermans Top Ten List
• Interval numbers are meaningful, no true zero
• a 7 Point Scale, Fahrenheit or Celsius
  Temperature
• Ratio numbers are meaningful, a true zero
• Height, Weight, Kelvin Temperature

6
RQs and Hypotheses

• RQs
• Open-ended, general
• When researcher is unsure or new to the area
• E.g. How does education level affect income?
• Hypotheses
• Predict a relationship
• When researcher knows an area, or has a theory
• E.g. The more education a person has, the
  higher his/her annual income.

7
RQs use Variables Hs use IV, DV

• Independent influences another variable
• IV Predictor variable
• Dependent influenced by another
• DV Outcome variable
• Sample RQ
• What is the relationship between education level
  and income?
• Sample H1
• The more education a person has, the higher
  his/her annual income.

8
RQs and Hypotheses

• RQs
• Open-ended, general
• When researcher is unsure or new to the area
• How does education level affect income?
• Hypotheses
• Predict a relationship
• When researcher knows an area of has a theory
• The more education a person has, the higher
  their annual income.

9
Hypotheses

• Two-Tailed Hypotheses
• Non-directional researcher predicts a
  relationship, but does not specify the nature
• Education level is related to income.

10
.

• One-Tailed Hypotheses
• Directional predicts a relationship AND the
  direction of that relationship
• The more education a person has, the higher
  their annual income.

11
Operationalization

• Operational Definition
• Defines a concept in observable / measurable
  terms
• A scientist can propose/claim/offer virtually ANY
  operational definition of a concept all he/she
  has to do is be able to defend it
• So operational definitions must be
• Plausible (must make sense to most in the field)
• Measurable (must be specified in detail)
• Replicable (must be complete - so others can
  repeat)

12
Examples of Operational Definitions

• Good (Defensible)
• IQ score achieved on the Wechsler Adult
  Intelligence Scale
• Poor (Indefensible)
• IQ how smart someone is
• Good (Defensible)
• Educ Level highest grade completed
• Poor (Indefensible)
• Educ Level total years in school

13
In-Class Team Exercise 1 - Part II

• First Do as Individuals, then produce a Team
  Version
• 1) Create 2 Hypotheses (One 1-Tailed, One
  2-Tailed)
• Relate the concepts regular exercise and
  health
• 2) Create a specific, measurable Operational
  Definition of each concept
• 3) Which is the IV, which the DV?
• 4) Propose 2 (likely/possible) Intervening
  Variables
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• Deliverable a written version of the above

14
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
  variable (an Intervening or Confounding
  variable)