Experiments (cont.)

Content Analysis

COMM 420.8Fall 2007

Nan Yu

Factorial Design (Multiple IVs Design)

more than one independent variable (IV).

IV’s are called “factors”

Factorial Design (Multiple IVs Design)

E.g., Imagine that you would like to study the effect of advertising (IV1) and provision of product sample (IV2) on purchase intention (DV)

Both IVs have to be manipulated and DV has to be measured

Advertising

Provision of Product Sample

Purchase intention

IV 1

IV 2

DV

Factorial Design (Multiple IVs Design)

Advertising

Provision of Product Sample

Purchase intention

• 2 (advertising) x 2 (provision of product sample) factorial design 2 (advertising) = Exposed to ad, Not exposed to ad 2 (provision of product sample) = Product sample available, Product sample not available Purchase intentionI don’t want to buy it I really want to buy it.

1 2 3 4 5 6 7

• What level measurement are the IVs and DV?• Nominal/ Ordinal/ Interval/ Ratio

Group 1 No advertising, no

sample (control group) Group 2

Sample only, no advertising

Group 3Advertising only, no

sample Group 4

Both sample and advertising

Groups in Factorial Design

(Watt and van der Berg, 2002)

Main effects and interaction effects

Main effectsThe effect of one IV is not depending on the

levels of the other IV.

Interaction effectsThe effect of one IV is depending on the levels

of the other IV.

Example

Factorial Design

NormalDilated

FemaleMale

Design Diagram

Main effects for pupil dilation

Main effects for gender

Interactions

Demo 1 Answer

Demo 1 (cont.)

Demo 1 (cont.)

Demo 1 (cont.)

Interaction Demo

Please go to folder week 7 on ANGEL

Download the file “interaction demo”

Demo 2

Computer Anthropomorphism(Koh & Tsay, 2006)

IV1: Anthropomorphizing a computer (named

computer vs. unnamed computer)

IV2: Physical proximity between the user and

computer (far vs. near)

IV3: Reciprocity of the computer (good score vs.

bad score)

DV: Politeness towards the computer

How many factors are in this study?

What kind of factorial design is it?

How many experimental groups do we need?

If each group needs to have 20 people, how many of participants should the study recruit?

2X2X2 factorial design, 160 people

Computer Anthropomorphism(Koh & Tsay, 2006)

2 (Anthropomorphism) x 2 (Physical Proximity) x 2 (Reciprocity) Experimental Design

1Named Comp.,Near Comp.,Good Score

2Named Comp.,

Far from Comp.,Good Score

3Named Comp.,Near Comp.,Bad Score

4Named Comp,

Far from Comp.,Bad Score

5Unnamed

Comp.,Near Comp.,Good Score

6Unnamed

Comp.,Far from Comp.,

Good Score

7Unnamed

Comp.,Near Comp.,Bad Score

8Unnamed

Comp,Far from Comp.,

Bad Score

Good Score

ANTHROPOMORPHISM

Named Computer

Unnamed Computer

Near Computer

RECIPROCITY

Far from Computer Near Computer Far from Computer

Bad Score

PHYSICAL PROXMITYCells

Computer Anthropomorphism(Koh & Tsay, 2006)

2 (Anthropomorphism) x 2 (Physical Proximity) DV: Politeness toward the computer

Interaction effects

NamedComputer

UnnamedComputer

Near the computer

Away from the computerPolitenesstowards the

computer

More Examples

If a factorial experiment is:

2 x 2 Number of factors? Number of levels per factor? Number of groups?

2 x 3 x 2 Number of factors? Number of levels per factor? Number of groups?

If you have only 1 factor with 3 levels, you can call it 1X3 experimental design.

Adv & Disadv of Factorial Designs

Advantages – Combined effect of multiple variables

Disadvantages – Increases number of participants/subjects

Increases time needed to conduct the experiment

Field Experiment

Researcher retains control over IVs, but conducts the research in a natural setting, without any control over environmental influences.

E.g., Imagine that you are a researcher who is employed by a large corporation. You are interested in the ability of a communication training program to reduce communication anxiety in people who must make speeches.

HypothesisThose who receive communication training will have reduced levels of communication anxiety compared to those who did not receive communication training.

Field Experiment Example

Sampling Frame:

List of all employees of

the organization

GROUP 1

GROUP 2

Randomly assigned to one of two groups

Anxiety

Both groups fill out a questionnaire assessing communication anxiety (operationalized as apprehension immediately before giving his or her most recent presentation)

RECEIVES TRAINING PROGRAM

DOES NOT RECEIVE

TRAINING PROGRAM

Several months later…

GROUP 1

GROUP 2

Control Group

Each group fills out the same questionnaire

RECEIVES TRAINING PROGRAM

DOES NOT RECEIVE

TRAINING PROGRAM

Treatment Group

Anxiety

Comparing Posttest Measures

GROUP 1

GROUP 2

Control Group

QComparing the mean scores of communication anxiety, which group needs to be significantly

greater to support the hypothesis?

RECEIVES TRAINING PROGRAM

DOES NOT RECEIVE

TRAINING PROGRAM

Treatment Group

COMMUNICATION ANXIETY OF GROUP 1

COMMUNICATION ANXIETY OF GROUP 2

POSTTEST MEASURES

Field Experiment: Benefits vs. CostsBenefits

Increases external validity (due to natural setting) Nonreactivity (Little influence of a subject’s awareness

of being measured or observed on his/her behavior)Can examine complex social processes and situations

(more informative) Inexpensive (as compared to lab experiments) in most

cases (depending on size and scope)

CostsEthical considerations need to be taken into accountExternal hindrances in the environmentLittle control over extraneous variables (as compared to

lab experiments)

Observational Research

Sometimes, the researcher has no means to manipulate the IVs

There are instances in which s/he can control neither the IV nor the research setting. E.g.1 Retrospective studies: Researcher is interested in how past

events from childhood influence present behavior of adults

In this case, the researcher is limited to observing (variations in IV), i.e. measuring instead of manipulation.

Content Analysis

Content Analysis

Survey and experiments try to discover similar/different patterns among people.

Content analysis try to observe the messages in the media – the pattern, the trend and the problems.

Content Analysis

Systematic study of communication contents in an objective and quantitative manner.

The researcher uses objective and systematic counting and recording procedures to produce a quantitative description of the symbolic content in a text.

Why do we need content analysis?

Applications of content analysis

Describing communication content

Testing hypothesis of message characteristics

Assessing the image of particular groups in society.

Comparing media content to “real world”

Establishing starting point for media effects research, (e.g. cultivation and agenda setting)

Content Analysis

The content refers to words, meanings, pictures, symbols, ideas, themes, any message that can be communicated, etc.

The text refers to anything written, visual, or spoken that serves as a medium for communication (e.g., books, newspapers and newspaper articles, advertisements, speeches, official documents, movies, musical lyrics, photographs, etc.)

Content Analysis

Uses nonreactive measures and it is a type of unobtrusive research

Unobtrusive research is conducted in such a way that people being studied are not aware of it, and therefore they behave more “naturally”

Therefore, the measures are not reactive (i.e., participants are not reacting against the research procedures, settings, etc.)

Steps in Content Analysis

1. Research topic/set up parameter

2. Sampling

3. Codebook/Intercoder reliability

4. Coding

5. Analyze the pattern of the data

6. Results/Conclusions

Coding and measurement

Measurement (coding) in content analysis uses structured observation: systematic, careful observation based on written rules.

The rules explain how to categorize and classify the observations (i.e., units).

Written rules are important in content analysis, as they improve the reliability and make the replication possible.

Intercoder reliability

In order to improve the reliability and eliminate differences in judgments, researchers train and use more than one coder.

If they agree most of the time on what unit should be placed in which category, the reliability is high.

This type of reliability is called intercoder or interrater reliability.

Intercoder reliability

There are a number of ways to compute intercoder reliability (depend on the level of measurement of the content categories).

Generally, the percent agreement for a good set of content categories should be above 90%.

Poor reliability may indicate: Content categories are poorly defined or are too

general.

Content coders are not well trained.

An example of content analysis