Terms you should know well
You should be able to answer these questions:
What are the variables in this study?
Which method of analysis would you use to . . . (compare two means, determine the strength of the
relationship between two continuous variables, determine whether two categorical variables are
independent, etc.)?
Level of scaling: If you have the following variable (age, sex, education, nationality,... ), and you want
to compare two values, what kind of comparison can you do? Why that kind of comparison? What
determines the kind of comparison you can do?
Under what conditions or for what purposes is it appropriate to . . .(calculate a mean, perform a t-test, divide one value by another, reject a null hypothesis, etc.)?
What reduces sampling error?
In Crosstabulation, how do you calculate expecteds?
How do you calculate critical ratios?
What do you do with critical ratios?
Under what conditions is statistical significance an issue?
You should know what it means to:
• estimate a population parameter
• reject the null hypothesis
• test a theory empirically
• test the significance of a difference
• say a difference is not statistically significant
• say one variable is independent of another
• say "p < 0.05"
• operationalize a concept
What is the difference between:
- row and column percentages
- conceptualization and operationalization
- probability and non-probability sampling
- null and alternate hypotheses
- medians and means
- independent and dependent variables
- z-scores and deviation scores
- random and biased errors
- ambiguous and vague
- covariance and correlation
- computational form and regular form
- variables and constructs
- research questions and hypotheses
- reliability and validity
- sample statistics and population parameters
- one-dimensional vs multidimensional constructs
- direct vs. inverse; positive vs. negative relationship
- descriptive, exploratory, and explanatory research
- hypotheses and theories
- empirical and theoretical
- discrete and continuous variables
- inductive and deductive theory construction
- descriptive and inferential statistics
- row and column percentages
- parsimonious and perspicuous
- statistics and parameters
What is the relation between:
- variables and constructs
- hypotheses and theories
- level of scaling and measure of central tendency
- level of scaling and measure of dispersion
- level of scaling and test of significance
- level of scaling and continuous/discrete
- null and alternate hypotheses
- biased errors and validity
- variables, constructs, hypotheses, and theories
- sampling variability and statistical significance
- z-score and the mean
What is:
- reality isomorphism
- inverted u-shaped curve
- stratified sampling
- external validity
- dispersion
- variance
- research question
- conceptual definition
- sampling variability
- a standard error
- a critical ratio
- systematic sample with random start
- error variance
- standard error of the mean
- statistical significance
- sampling distribution
- quota sample
- stratified sampling
- simple random sample
- a circular definition
- percentage down compare across
- a statistically significant difference
- the assumption behind expected values in chi-square
What are the following:
- units of analysis
- deviation scores
- "the marginals"
- standard scores
- representative samples
- degrees of freedom
- probability sampling methods
- essential qualities
- estimates of population parameters
- failure to reject the null hypothesis
- "the expecteds"