PSYCH 100 RESEARCH METHODS EXAM QUESTIONS ANSWERED CORRECTLY LATEST UPDATE 2026
Single factorial design - Answers single independent variable. The IV can still have multiple levels. Ex.
Highlighting (single factor designs are limited in what they can tell you. To test interactions you need a
multi factor design) Single factor designs are septiable to the third variable problem.
Multi-factorial design - Answers assesses the interaction between the IV and another variable Ex.
Highlighting and exposure to highlighted text. This type of design controls for the third variable
problem
Main effects: - Answers Used to describe the overall effect of a single independent variable. The
difference between the means of the levels of any one independent variable Ex. Highlighter vs.
Baseline and Once vs. Twice
Simple effects - Answers the comparison between each variable at each level. Highlighter (once) vs
baseline (twice) etc.
Interactions: The comparison of each of the simple effects. Ex. Highlighter (once) - Baseline (once) vs.
Highlighter (twice) - Baseline (twice)
Within-subject factorial design - Answers a person experiences all conditions. All conditions will have
the same participants. This allows for an experiment to have a smaller sample size while still holding
its internal validity though is susceptible to order effects, needs counterbalancing to reduce.
Between subjects factorial design - Answers different people test each condition. Therefore a group
of 60 ppl will be divided into 4 groups. Each condition will have 15 ppl.
mixed factorial design - Answers A design that includes both independent groups (between-subjects)
and repeated measures (within-subjects) variables.
Correlation - Answers a measure of the relationship between two variables. High correlation = the
level of one variable strongly depicts the level of the other variable
third variable problem - Answers The problem of drawing causal conclusions in correlation research;
third variables are uncontrolled factors that could underline a correlation between variables X and Y.
Directionality Problem: - Answers In correlation research, the fact that for a correlation between
variables X and Y, it is possible that X is causing Y but it is also possible that Y is causing X; the
correlation alone provides no basis for deciding between the two alternatives.
correlational research - Answers the study of the naturally occurring relationships among variables.
Does not equal causation.
Correlation coefficient (Pearson's r) - Answers Proportion of the variance in one variable explained by
the variance in the other variable. Just square Pearson's r.
The effect of outliers on correlations - Answers Scores that are dramatically different from the
remaining scores in a sample (don't seem to belong to the same distribution)
nonlinearity - Answers the degree to which multiple measurements do not approximate a straight
line on a graph
Restriction of range - Answers Measurements of one variable may not span a large enough range of
values. Sometimes it is a good thing to limit your range to focus on interpreting results from a specific
subset of data. However, the interpretation of your data is limited to the data in your range.
Heterogeneous subsets - Answers sample consists of subgroups that show different patterns
Statistical/practical significance of correlation coefficient: - Answers provides information about the
strength, direction and magnitude of a correlation between two variables.
Partial Correlations - Answers Measures the correlation between two variables while controlling for a
third variable. Often used as a way to try to control for confounding variables in correlational
research. E.g., What is the relationship between mindfulness and happiness? -Many potential third
variables (money, free time, anxiety, etc.)
Value of comparing correlations in different conditions: - Answers Differences in correlations with
different DVs or in different situations/conditions can be informative. Example: looking at a
correlation from one perspective can show a weak correlation but looking at it from another
perspective/ condition can make it a stronger correlation with a different direction.
Quasi-experimental designs - Answers research designs involving the manipulation of the
independent variable but lacking either random assignment to groups or a control group.
(•Participants not randomly assigned to conditions of an IV
•Quasi-Independent Variables (Q-IVs) formed whenever groups are selected based on some measure
or pre-existing characteristic
Single factorial design - Answers single independent variable. The IV can still have multiple levels. Ex.
Highlighting (single factor designs are limited in what they can tell you. To test interactions you need a
multi factor design) Single factor designs are septiable to the third variable problem.
Multi-factorial design - Answers assesses the interaction between the IV and another variable Ex.
Highlighting and exposure to highlighted text. This type of design controls for the third variable
problem
Main effects: - Answers Used to describe the overall effect of a single independent variable. The
difference between the means of the levels of any one independent variable Ex. Highlighter vs.
Baseline and Once vs. Twice
Simple effects - Answers the comparison between each variable at each level. Highlighter (once) vs
baseline (twice) etc.
Interactions: The comparison of each of the simple effects. Ex. Highlighter (once) - Baseline (once) vs.
Highlighter (twice) - Baseline (twice)
Within-subject factorial design - Answers a person experiences all conditions. All conditions will have
the same participants. This allows for an experiment to have a smaller sample size while still holding
its internal validity though is susceptible to order effects, needs counterbalancing to reduce.
Between subjects factorial design - Answers different people test each condition. Therefore a group
of 60 ppl will be divided into 4 groups. Each condition will have 15 ppl.
mixed factorial design - Answers A design that includes both independent groups (between-subjects)
and repeated measures (within-subjects) variables.
Correlation - Answers a measure of the relationship between two variables. High correlation = the
level of one variable strongly depicts the level of the other variable
third variable problem - Answers The problem of drawing causal conclusions in correlation research;
third variables are uncontrolled factors that could underline a correlation between variables X and Y.
Directionality Problem: - Answers In correlation research, the fact that for a correlation between
variables X and Y, it is possible that X is causing Y but it is also possible that Y is causing X; the
correlation alone provides no basis for deciding between the two alternatives.
correlational research - Answers the study of the naturally occurring relationships among variables.
Does not equal causation.
Correlation coefficient (Pearson's r) - Answers Proportion of the variance in one variable explained by
the variance in the other variable. Just square Pearson's r.
The effect of outliers on correlations - Answers Scores that are dramatically different from the
remaining scores in a sample (don't seem to belong to the same distribution)
nonlinearity - Answers the degree to which multiple measurements do not approximate a straight
line on a graph
Restriction of range - Answers Measurements of one variable may not span a large enough range of
values. Sometimes it is a good thing to limit your range to focus on interpreting results from a specific
subset of data. However, the interpretation of your data is limited to the data in your range.
Heterogeneous subsets - Answers sample consists of subgroups that show different patterns
Statistical/practical significance of correlation coefficient: - Answers provides information about the
strength, direction and magnitude of a correlation between two variables.
Partial Correlations - Answers Measures the correlation between two variables while controlling for a
third variable. Often used as a way to try to control for confounding variables in correlational
research. E.g., What is the relationship between mindfulness and happiness? -Many potential third
variables (money, free time, anxiety, etc.)
Value of comparing correlations in different conditions: - Answers Differences in correlations with
different DVs or in different situations/conditions can be informative. Example: looking at a
correlation from one perspective can show a weak correlation but looking at it from another
perspective/ condition can make it a stronger correlation with a different direction.
Quasi-experimental designs - Answers research designs involving the manipulation of the
independent variable but lacking either random assignment to groups or a control group.
(•Participants not randomly assigned to conditions of an IV
•Quasi-Independent Variables (Q-IVs) formed whenever groups are selected based on some measure
or pre-existing characteristic
