ELEMENTARY STATISTICS EXAM 4 TEST
PAPER COMPLETE SOLUTIONS AND
ACCURATE RESPONSES
●● The difference between ANOVA and the t tests is
Answer: that ANOVA can be used in situations where there are two or
more means being compared, whereas the t tests are limited to situations
where only two means are involved.
●● If we are comparing two means from independent samples....
Answer: ANOVA will produce the same results as the t-test for two
independent samples.
●● If we are comparing two means from dependent (related) samples...
Answer: ANOVA will produce the same results as the t-test for two
related samples.
●● If we already have t-test, why is ANOVA necessary?
Answer: ANOVA is necessary to protect researchers from excessive risk
of a Type I error in situations where a study is comparing more than two
means.
●● ANOVA allows researcher to evaluate all of the mean differences
,Answer: in a single hypothesis test using a single α-level and, thereby,
keeps the risk of a Type I error under control no matter how many
different means are being compared.
●● The purpose of ANOVA is to test significant differences on variances
among groups (True, False).
Answer: False (testing significant differences on MEANS).
●● Performing several t-tests rather than one ANOVA involves a greater
risk of committing Type II error (True, False).
Answer: False (greater risk of committing Type I error)
●● Independent variable (IV)
Answer: The independent variable is the variable that is manipulated by
the researcher. The independent variable usually consists of the two (or
more) treatment conditions to which subjects are exposed.
o Treatment conditions or
o Type of TV program
This study has only one IV
●● Quasi-independent variable (Quasi-IV)
Answer: The quasi-independent variable is the variable that is NOT
manipulated by the researcher. Sex (male vs. female), marital status
(married, divorced, single, separated, others) are the examples.
,●● Dependent variable (DV)
Answer: The dependent variable is the one that is observed for changes
in order to assess the effect of the treatment.
- aggressive behaviors
●● factor
Answer: Factor A factor is the variable (independent or quasi-
independent) that designates the group
o Treatment conditions or
o TV programs
This study has only one factor
●● Levels
Answer: The levels of the factor are the individual conditions or values
that make up a factor
- Three types (condition/levels) of treatment
●● A researcher measures job satisfaction among managers, secretaries,
skilled workers, and laborers to determine whether the mean job
satisfaction is significantly different among four occupation groups.
, a. IV (or quasi-IV):
b. DV:
c. Factor:
d. Level:
Answer: a. Occupation (it is quasi-IV because occupation variable
cannot be manipulated)
b. Job satisfaction
c. Occupation
d. 4 Types of occupation
●● What statistics is associated with ANOVA?
Answer: f- test
●● What is the f test
Answer: F= variance (differences) between treatments/ variances
(differences) expected with no treatment effect
Mean differences between groups who received different treatments/
Individual differences for people who received the same treatments
●● Why use variance?
PAPER COMPLETE SOLUTIONS AND
ACCURATE RESPONSES
●● The difference between ANOVA and the t tests is
Answer: that ANOVA can be used in situations where there are two or
more means being compared, whereas the t tests are limited to situations
where only two means are involved.
●● If we are comparing two means from independent samples....
Answer: ANOVA will produce the same results as the t-test for two
independent samples.
●● If we are comparing two means from dependent (related) samples...
Answer: ANOVA will produce the same results as the t-test for two
related samples.
●● If we already have t-test, why is ANOVA necessary?
Answer: ANOVA is necessary to protect researchers from excessive risk
of a Type I error in situations where a study is comparing more than two
means.
●● ANOVA allows researcher to evaluate all of the mean differences
,Answer: in a single hypothesis test using a single α-level and, thereby,
keeps the risk of a Type I error under control no matter how many
different means are being compared.
●● The purpose of ANOVA is to test significant differences on variances
among groups (True, False).
Answer: False (testing significant differences on MEANS).
●● Performing several t-tests rather than one ANOVA involves a greater
risk of committing Type II error (True, False).
Answer: False (greater risk of committing Type I error)
●● Independent variable (IV)
Answer: The independent variable is the variable that is manipulated by
the researcher. The independent variable usually consists of the two (or
more) treatment conditions to which subjects are exposed.
o Treatment conditions or
o Type of TV program
This study has only one IV
●● Quasi-independent variable (Quasi-IV)
Answer: The quasi-independent variable is the variable that is NOT
manipulated by the researcher. Sex (male vs. female), marital status
(married, divorced, single, separated, others) are the examples.
,●● Dependent variable (DV)
Answer: The dependent variable is the one that is observed for changes
in order to assess the effect of the treatment.
- aggressive behaviors
●● factor
Answer: Factor A factor is the variable (independent or quasi-
independent) that designates the group
o Treatment conditions or
o TV programs
This study has only one factor
●● Levels
Answer: The levels of the factor are the individual conditions or values
that make up a factor
- Three types (condition/levels) of treatment
●● A researcher measures job satisfaction among managers, secretaries,
skilled workers, and laborers to determine whether the mean job
satisfaction is significantly different among four occupation groups.
, a. IV (or quasi-IV):
b. DV:
c. Factor:
d. Level:
Answer: a. Occupation (it is quasi-IV because occupation variable
cannot be manipulated)
b. Job satisfaction
c. Occupation
d. 4 Types of occupation
●● What statistics is associated with ANOVA?
Answer: f- test
●● What is the f test
Answer: F= variance (differences) between treatments/ variances
(differences) expected with no treatment effect
Mean differences between groups who received different treatments/
Individual differences for people who received the same treatments
●● Why use variance?
