Practice Exercises Using Data Set #3
Data Set #3 Description
This study is a factorial experiment investigating the impact of
(1) the amount of distraction and (2) the type of interaction on
several social ratings of research partners.
The research partners are actually confederates who behave
exactly the same in each of the interactions. The amount of
distraction is manipulated by adjusting the workload on the
participants. Low workload participants are engaged in tasks that
require their concentration for about 20% of the total time they are
in the experiment. Moderate workload participants are engaged in
demanding tasks for 50% of the time, and high workload participants
are engaged in demanding tasks for 80% of the time.
There are three types of tasks that participants may be engaged
in. One involves competing with the partner; one involves
cooperating as a teammate with the partner; one involves working on
separate and unrelated tasks.
The ratings are (1) how much they liked their partner, (2) how
much they trusted their partner, (3) their estimate of their
partner's intelligence, and (4) their estimate of their partner's
height.
Both factors were between-subjects factors, and 10 participants
were randomly assigned to each of the nine groups of the study.
- Participant ID number (subject)
- Factor 1-Level of Distraction [1=20% activity; 2=50%
activity; 3=80% activity] (distract)
- Factor 2-Type of Interaction [1=competitive; 2=cooperative;
3=parallel activity] (interact)
- sex of participant (sex)
- age of participant (age)
- Rating of how much participants liked their partner
[1=strongly disliked to 10=strongly liked] (like)
- Rating of how much participants trusted their partner
[1=strongly distrusted to 10=strongly trusted] (trust)
- Rating of their partner's intelligence [1=very unintelligent
to 10=extremely intelligent] (iq)
- Estimate of the height of their partner to the nearest inch
[actual height of confederate was 69 inches] (height)
Questions
- Start by using descriptive statistics to find data entry
errors. (There are four of them.) Copy the practice #3 file to
your hard drive and correct the data entry errors with the most
likely numbers before addressing the rest of the questions.
- Verify that there are 10 participants in each of the nine
cells.
- Verify that there are an equal number of males and females
in each of the cells.
- What is the average age of the participants in this study?
- What is the range and standard deviation of age in this
study?
- What is the average liking rating in this study?
- Is there a main effect for distraction level for the
dependent variable of liking?
- Is there an interaction for the dependent variable of
liking?
- Produce the matrix of cell means for the dependent variable
of liking.
- Graph the cell means for the dependent variable of liking?
- Is there a relationship between the variables of liking and
trusting in the entire sample?
- Conduct a two-way ANOVA for the dependent variable of
trusting.
- Conduct a two-way ANOVA for the dependent variable of
intelligence.
- Conduct a two-way ANOVA for the dependent variable of
height.
- The height of the confederate in this study was 69 inches.
Is there a significant difference between this actual height and
the estimated heights from the participants in this study?
- Construct a scatter plot for the variables of liking and
trusting.
- Produce the matrix of cell means for the dependent variable
of height.
- Is there a correlation between trusting and the intelligence
estimate in the entire sample?
- Is there a difference between the male and female
participant's estimates of the height of the confederate?
- Is there a sex difference on any of the other dependent
variables in the study?
- Are the groups equivalent on the variable of age?
- Are there sex differences on the variable of age?
- Conduct a three-way ANOVA using sex as the third independent
variable.
- Imagine that there was only one factor (the type of
interaction), with thirty participants assigned to each
condition. Conduct a one-way ANOVA of the data for this factor.
- Construct a frequency distribution for height estimates.
- Compute an index of skewness for the variable of age.