Second Exam-Review Sheet

Overview of quantitative research methods
Primary goal of quantitative research (dependent variables vs. independent variables, relationships -- causal (three necessary features, causal vs. correlational)
Variables are measured
Deterministic/probabilistic model
Steps involved in quantitative research
Come up with question needs to be empirically answered (hypothesis vs. research questions)
Determine unit of analysis
Select a research method (survey, content analysis, experiment)
Operationalization
Sampling and populations
Make your observations
Analyze observations
Interpret results.
Levels of measurement
Properties and differences between (nominal, ordinal, interval, ratio measures)
Reliability (test/retest method, split-half method, inter-coder method)
Validity (face validity, content validity, criterion validity - concurrent and predictive)
Describing Data
Distributions (frequency)
Distributions (population, sample, sampling, differences)
Measures of central tendency (mode, median, mean)
Measures of dispersion (range, standard deviation)
z-scores
Content Analysis
goal
steps involved
advantages and disadvantages
Statistics used with content analysis
chi-square analysis (2)
goal
one sample 2 vs. multiple sample 2
theoretical values, degrees of freedom
Logic and purpose of experiments
Hypothesis testing
Logic behind experiments (manipulation and control)
manipulation checks, random assignment, matching
Why experiments can identify causal relationships
Basic steps in experiments
Experimental design
Threats to internal validity (history, maturation, testing, statistical regression, mortality, selection, demand characteristics)
Threats to external validity (non-representative samples, specific conditions are required)
Eliminating these problems
Types of experimental designs (pre-experimental designs -- one-shot case study, one group pretest-posttest design, static group comparison, true experimental designs - pretest-posttest control group design, posttest only control group design)
Factorial designs
Basic terms (factors, number of levels)
Types of effects (main and interaction effects)
Strengths and weaknesses of experimental research
Overview of statistics used with experimental research
Goal
Significant difference
Hypothesis testing (null hypothesis vs. research hypothesis;
directional vs. non-directional)
Logic underlying hypothesis testing
Probably theory
Distribution of differences (mean and standard deviation - standard error of the difference)
Significance level
Tests of Significance
z-test
goal, steps involved, critical values
t-test
Steps involved in using t-tests
F-test
steps involved
when each test should be used