Requirements:• The Step 1&2 are finished.
• Only need to do the step 3. Which are explain the results of step 1&2, especially
step 2 from the perspective of induction and abduction. (Step 2 stars from the
page 8)
• Details are showing below
• Use the Comment tool of Adobe Acrobat to add explanations.
Step 1. Conduct an inferential analysis, such as… › testing a statistically significant difference-in-means
between two or more subsets of your data across an interval- or ratio-level variable (do this by testing
whether confidence interval for the difference in means includes zero)
› Testing a statistically significant increase or decrease in a quantitative variable over time
› Estimating a population parameter from a sample (provide a confidence interval)
› Estimating the minimum sample size to measure a statistic—or a difference in two statistics—at a given
level of confidence (e.g., 95%)
Step 2. Then, thinking carefully about your understanding of the causal relationships among the data
you’ve processed (and/or any other outside forces), identify dependent and independent variable(s) in
your data. Estimate and interpret a regression model based on these variables.
Step 3. Each step above should be followed by an explanation that succinctly explains your analysis, why
you set it up the way you did, and how you interpret its results. This interpretation should include…
› Induction: interpret your regression coefficients numerically in terms of their sign, magnitude, level of
significance
› Abduction: inference to the best explanation; How would you explain what’s happening numerically in
your findings in the context of theory and your own professional experience? For your regression model,
also explain how you specified it and why, based your understanding of the theory behind your research
topic. Did your account for potential endogeneity in your data, collinearity in your independent variables,
and/or confounding variables?
Tell us a story about the real world using your results. Your analysis should convey a compelling data-driven story
about your research topic.
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