WorksheetsPSYC3980 FInal
Total questions: 61
Worksheet time: 31mins
Which of the following describes an experiment?
the manipulated condition
manipulating at least one variable and measured others
outcome variable, measured.
How many IV and DV must an experiment have?
(a)
Which describes covariance?
linear relationship between two variables
principle establishing cause-effect relationships between two variables.
Which describes temporal precedence?
linear relationship between two variables
principle establishing cause-effect relationships between two variables.
The cause variable precedes the effect variable.
True
False
What's the difference between the confound and design confound?
There is no difference between the two
one describes an alternative explanation for experiment, the other is an experimenter's mistake in designing the IV.
one is just random or haphazard
What is an independent groups design?
each participant is presented with all levels of IV
separate groups of participants are placed into different levels of IV
What is a within-groups design?
Each participant is presented with all levels of IV
separate groups of participants are placed into different levels of IV
Posttest-Only Design:
The DV is measured/tested once after the IV
The DV is measured before and after IV
Participants measure on DV more than once
Pretest/Posttest Design:
DV is measured only once, after IV
DV is measured before and after the IV
DV measured more than once, after exposure to each level of the IV
Concurrent-Measures Design:
Each Participant is presented with all levels of IV
DV measured only once after IV
Participants measured on DV more than once.
Participant exposed to all levels of IV at roughly the same time.
How is full counterbalancing different from partial counterbalancing?
full has all possible orders used, and partial has some orders
full has some possible orders and partial has all orders
there's no difference between the two
Which isn't a disadvantage of Within-Group Designs?
The Latin Square
demand characteristics: cue leading participants guess the hypothesis
potential order effects
Might not be practical/possible
Which described construct validity?
To whom or what can the causal claim generalize?
How well were the variables measure/manipulated?
How much? How precise?
Are there alternative explanations for the results?
Which describes external validity?
To whom or what can the claim generalize?
How well were the variables measures/manipulated?
How much? How precise?
Are there alternative explanations for the results?
Which describes Statistical Validity?
To whom or what can the claim generalize to?
How well the variables are measured/manipulated?
How much? How precise?
Are there alternative explanations for the results?
Which describes internal validity?
To whom or what can the claim generalize to?
How well were the variables measured/manipulated?
How much? How precise?
Are there alternative explanations for the results?
Pilot Study:
generalized to other people
experiments are designed to test theories
simple study using different group of participants before /after formal study
Interaction effect:
Design that can test limits
whether effect of original IV depends on the level of another IV
Interpreting main effects and interactions
Which design studies two IV, tests limits, test theories, yields main effects and interactions?
Independent Groups Design
Comparison Group Design
Within-Groups Design
Factorial Design
What is a Participant Variable?
levels manipulated but not selected
levels selected but not manipulated
Main effect?
Difference in Differences
Overall Difference
Unforeseen Difference
Interactions?
Difference in Differences
Overall Differences
Unforeseen Differences
The interaction is ALWAYS more important than the main effect.
True
False
Independent-Groups factorial design is also known as between-subjects factorial design.
True
False
The within-groups factorial design is also known as repeated-measures design
True
False
What is a mixed factorial design?
four different groups
one group participate in all four conditions
1 IV independent group and 1IV within-groups
How can you describe the three-way interaction?
interaction b/w 2 IV depends on the 3rd IV
Differences in differences in differences
all of the above
What do researchers gain through quasi experiments, with a risk of internal validity?
real-world opportunities to study
External Validity
all of the above
What occurs within a Quasi-experiment?
You can't randomly assign participants to the IVs
You study a few individuals
You study thousands
In a small-N design you only study a few individuals.
True
False
Which describes a waitlist design?
some participants receive treatment others on the waitlist don't.
I don't know.
all participants receive treatment but at different times.
An external, historical event that happens for everyone at the same time as the treatment is:
Attrition Threat
History Threat
Maturation Threat
Which validities are at a disadvantage with small-N studies?
External
Statistical
Construct
Internal
Stable-baseline design?
stagger the intervention across various individuals, times, or situations.
observe a behavior over an extended baseline period before intervention.
observe a problem behavior with vs. w/o treatment
Multiple-baseline Design:
observe a problem behavior with vs. w/o treatment
stagger the intervention across various, individuals, times, etc
observe behavior for an extended baseline period before intervention
Reversal Design?
Observe a problem behavior with vs. w/o treatment
stagger the intervention across various individuals, times, or situations
observe a behavior for an extended baseline period of time before intervention
Larger samples collect a little info from each participant, while in small-N designs they collect a lot of info from a few case.
False
True
Direct Replication?
repeat as closely and compare results from original & new
same research question, different procedures
Neither
Conceptual replication?
repeat as closely and compare results from original and new
same research question, different procedures
Both
In Replication-plus-extension you replicate original and add variables to test additional questions.
True
False
HARKing:
hypothesizing after results are shown
manage & analyze data in a wide variety of ways, p-value under .05
Preregistration:
publish a transparent research practice
publish methos, hypotheses, or statistical analyses before data collection
trying many ways of analyzing data so results are more likely to be fluke
Instead of recording what happened in a study, researchers invent data
data fabrication
data falsification
debriefing
Researchers influence a study's results, slectively deleteing observations
Data fabrication
Data falsification
Debriefing
Assess potential harm to participants and potential benefits to society.
Principle of Justice
Principle of Beneficence
Calls for a fair balance between the kinds of people who participate in research and the kinds of people who benefit from it.
Principle of Justice
Principle of Beneficence
Repeated measures at different times of the same people.
Cross-Sectional Study
Autocorrelations
Longitudinal Study
Correlations between 2 variables measured at the same time.
Cross-Sectional correlation
Longitudinal Design
Autocorrelations
Correlation of each variable with itself across time.
Longitudinal design
Auto-correlation
Cross-sectional design
Correlation between one variable at earlier time and another variable at a later time
Cross-sectional
Cross-lag
A statistical technique for examining the linear relationship between a continuous DV & a set of 2 or more IV
Cross-sectional
Regression
Multiple Regression
DV, usually specifies in top row of title of a regression table
Predictor variable
Criterion variable
Predictor variables are also known as:
DVs
IVs
Beta: pure effect of one variable to criterion variable when controlling for other variables, strength and direction
True
False
If 95% CI doesn't contain zero then it is statistically significant
True
False
the degree to which a scientific theory provides the simplest explanation of some phenomenon
pattern
parsimony
pattern & parsimony
Investigate causality by using a variety of correlational studies that all point in a single direction
pattern
parsimony
pattern & parsimony
an intermediary or intervening variable that accounts for an observed relation between two variables
Predictor variable
Mediating variable
Mediators: why? how?
Moderators" For whom? when?
True
False
Null effect: no difference in DV
True
False
