Difference between descriptive and inferential analysis
Activity: Submit your note for the upcoming exam
• Submit your note (Microsoft Word) for the upcoming exam up to 6 pages and maximum words of 2000.
• You may prepare for the exam with your classmates. You can create notes together but your own may work better for you.
• Any discussion regarding the answers or connection to the Internet will be not allowed.
• See the review guide below.
Final Exam Review Guide
Each student is expected to take a final-term exam. This exam will involve statistical analyses that have been covered in the classes before this exam. It is designed to assess each student’s capability to understand statistical results conducted by others. Students are provided with statistical results from research articles and expected to answer questions on the interpretation of them. Real life examples may be different from the format that was practiced in the class because researchers use different formats when they report their results. Students who understand ‘which value means what’ should be able to know the right answers. This closed-book test allows students to use their note that is helpful for the exam.
You need to have following capabilities in an actual article: Understanding which statistical test should be used; understand the main concepts especially in the research context; How the results can be presented in an article (tables and narratives)
You need to understand following concepts and can apply them in the actual articles (Exercises should prepare you for this).
• Understand 3 distributions
• Difference between descriptive and inferential analysis
• Difference between population and sample
• Different distribution format based on level of measurement
• Know major symbols used in statistics
• Understand the nature of statistic: z-value, t-value, F-value, χ2-value
• Understand the concept of statistical significance using the sampling distribution
o df
o Understand the concepts: critical value vs. test statistic, alpha vs. p-value
o What is statistical significance? What should be bigger than what or what should be smaller than
o p-value < alpha level: what does it mean?
o What does it mean?
o Understand sampling error, margin of error, rejection region
o Understand research/null/alternative hypothesis
• Understand these basic concepts in the context of the analyses.
o Understand the analysis of variance
o Independent samples t test
o Analysis of covariance, ANCOVA
o Correlation vs. Regression
o Multiple Linear Regression vs. Multiple Binary Logistic Regression (understand protective factors vs. risk factors)
o Chi-square test of Independence
o Non-parametric models
• For each analysis
o Understanding which statistical test should be used given a research context
o How to use SPSS outputs to create tables for reporting
o understand the main concepts especially in the research context;
o How the results can be presented in an article (tables and narratives).
<Not required>
Additional resources for recap: click this
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