Final exam Flashcards

1
Q

Threats to internal validity: History

A

an external event occurs that
affects the results of the study

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2
Q

Threats to internal validity: Maturation

A

respondents change over
time, which affects the DV/outcome

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3
Q

Threats to internal validity: Experimenter bias

A

attitudes or behavior
of researcher affect results

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4
Q

Threats to internal validity: Testing/sensitization

A

participant is
familiar with the measure (increase their
scores)

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5
Q

Threats to internal validity: Regression to the mean

A

people may score higher or
lower on a measure, then move toward the mean when
measured again

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6
Q

Threats to internal validity: Experimental mortality

A

people dropping out of the
study

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7
Q

Threats to internal validity: Contamination

A

people who have been part of the study
tell others what the study is about

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8
Q

Threats to internal validity: Sample bias/non-equivalent groups-

A

groups in conditions
are not equivalent before starting study

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9
Q

Key Elements of an Experiment: Manipulation of independent variable(s)

A

▪ Create different conditions/groups that
receive different treatments
▪ Control group- no treatment
▪ Treatment group- treatment
▪ Measure the effect (DV) after exposure to the
cause (IV)

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10
Q

Random assignment of participants

A

In theory, this creates equivalent (similar)
samples in different conditions

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11
Q

Problems with Post-Test Only
Control Group: sample bias

A

No way to assess if these
groups were different to begin
with

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12
Q

Factorial Design

A

 Experimental studies with two or more
independent variables
 To understand whether the combination of
two or more variables increases/decreases
effects (i.e., look at interactions)
 Interpreting 2X2 Factorial Design:
Two IVs, each IV has two levels
So we end up with four conditions/groups

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13
Q

Within-Subjects Designs

A

 Advantages
* Don’t have to worry about individual differences
* Fewer participants are required
 Disadvantages
* Order effects
* Fatigue
o People could get sick/tired of experiment
o People get worse at game because they don’t want
to play anymore
* Solution: Counter-balanced design
o Participant 1: X1O1 X2O2 X3O3 X4O4
o Participant 2: X2O2 X3O3 X4O4 X1O1

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14
Q

Uses of Content Analysis

A

Describe how much or what kind of
certain messages exist
❖E.g., How frequently are “dogs” and
“trucks” mentioned in top country songs?
❖What type of messages do Fortune 500
CEOs communicate on Twitter? (e.g.,
company vision, personal life etc.)
Compare media content to the “real
world”
❖E.g., How many criminals in prime‐time
dramas are minorities vs. actual U.S.
convicts that are minorities?

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15
Q

Uses of Content Analysis 2

A

 Assess the “image” of a particular
group
* E.g., How is Nike portrayed by its
competitors in their annual report?
* How are Syrian immigrants portrayed
in French TV media?
Discussion Question: What kind of
content analysis do we do in
PR/COM/Marketing? Examples?

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16
Q

Content Analysis Steps

A
  1. Formulate a research question or
    hypothesis
  2. Define the population
  3. Select a sample
  4. Define the unit of analysis
  5. Construct the categories of interest
  6. Train the coders
  7. Assess reliability
  8. Analyze the data
17
Q

Formulate a research question &
Defining the Population

A

 RQ: How is drunk driving portrayed
in primetime television shows?
 Need to define:
 Drunk driving
 Implied or explicit
 Primetime
 What hours?
 Television shows
 Cable, broadcast, streaming services?

18
Q

Code data

A

 Typically need 2-3 people
 They must understand and agree on
coding rules.
 So, we need to create a codebook to
provide these rules…
 Individuals must be trained to use
codebook.

19
Q

Compute Reliability

A

Coders should agree in what they see. If
not, that’s a problem.
 To ensure that the coding scheme is
reliable we have to test it:
 Coders analyze identical content
 Results are compared using statistical
tests for reliability
 Intercoder reliability: 70% low bottom

20
Q

Intercoder Reliability

A

Agreement
between coders

21
Q

Select a Sample: Example

A

 Often multi-stage cluster sampling
* Selecting Fortune 500 CEO communication
channels
* All media -> Social media -> Twitter
* Picking a sample of content of a workable
size
* Randomly pick 5 years’ content
* Sampling tweets
* Every 3rd tweet