Chapter 2 Flashcards
The Abstract in a Research Article
comprehensive summary of the article describing what was done, to whom, and what was found.
The Introduction
background on the research problem.
Independent Variable (s)
the variable that is manipulated by the researcher.
Dependent Variable
response measure that we think will be influenced by the IV
Moderating Variables
influence the relationship between the IV and the DV
Mediating Variables
variables that intervene between the IV and DV in their cause-effect relationship.
The Methods Section
details about exactly how the variables were measured, manipulated, and controlled.
The Procedure
chronological sequence of what happened to the participants.
The Results
statistical information about whether or not the data support the research hypothesis.
In research statistics are used for two purposes:
To summarize data
To test research hypotheses
Descriptive Statistics
includes measures of central tendency, variability, and the strength of the relationship between variables. Includes mean, median, mode, range, variance, and standard deviation.
Pearson Product-Moment Correlation
most common measure of association (symbolized as r). Describes how strongly variables are related to one another.
Inferential Statistics
used to generalize the findings of a study to a whole population.
Common Tests of Significance
used in hypothesis testing to determine whether results are statistically significant. Effect size – provides some indication of the strength of the effect.
Effect Size
provides some indication of the strength of the effect.
The Discussion
where the author describes how the results fit into the literature. Includes suggestions for future research
t-test
used to compare means of two groups.
F-test
used to compare means of more than two groups
Chi-Squared
used to compare frequencies.
Pearson’s r-Test
used to investigate whether there is a linear relationship between two continuous variables
Regression
It’s related to correlation, but we are interested in using a predictor variable to predict a criterion variable.
Multiple Regression
using more than one predictor variable to predict a criterion variable.
Partial Correlation
used to partial out the effects of a third variable that is influencing the relationship between two variables.
Semi-Partial Correlation
used to partial out the effects of a variable that is influencing only one of the other variables.
Logistic Regression
used when you want to use predictor variables but you don’t have a discrete criterion variable.
Factor Analysis
used to find simpler patterns of relationships among many variables.
Cluster Analysis
used to group similar objects into categories or clusters.
Structural Equation Modeling
can involve various techniques including factor analysis, regression models, path analysis, etc.
Discriminant Function Analysis
allows us to determine the predictive ability of each variable alone and in combination with other variables.
