The following information should assist you with your preparations for the final examination for the course. The final examination will be a “take-home” style test, meaning that you will have access to the exam on Monday, November 16, 2020 at 12:00am ET and the last day to upload your responses will be Friday, November 20, 2020 at 4:00pm ET. Please note that this document is simply a guide, and exam questions may touch on material that was covered during the semester but not indicated on this guide.
Topics and Areas of Study for the Examination:
· Conceptualization of variables
· Operationalization of variables
· Units of analysis
· The differences between (advantages and disadvantages) probability and nonprobability sampling.
· The key elements of probability sampling.
· The differences between samples and populations and what these differences mean for sampling design and data analysis
· Details about the design and construction of survey questionnaires (scales, indexes)
· Strengths and weaknesses of survey research and the advantages and disadvantages of various survey techniques
· Differences between independent and dependent variables
· Steps for developing a proposal, as outlined by Babbie
· The differences between qualitative and quantitative data
· Types of sampling designs
· How to construct research questions, hypotheses and null hypotheses
· The role of research literature on variable definitions and variable selection
· Levels of measurement
· Descriptive statistics (mean, mode, median) and variability (range, standard deviation, and variance)
· Understanding the conditions and assumptions for when and why we would use the Pearson product-moment correlation coefficient. Also, how to interpret the SPSS output for Pearson’s r.
· The differences between tests of significance, Type I, and Type II errors
· Understand the conditions and assumptions for when to use a one-sample z test. Also, how to interpret the SPSS output.
· Understand the conditions and assumptions for when to use t-test for independent groups and related groups. Also, how to interpret the SPSS output.
· Understand the conditions and assumptions for when to use a simple ANOVA (also known as an F test). Also, how to interpret the SPSS output.
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