3) Data, measures, and methods: Describe what data you use and how you operationalize your variables (i.e., what they intend to measure, and how each measure was constructed). What year were the data collected? Give the sample size and the sample characteristics. Is it a sample of males, black Americans, or some other subgroup? That is, if you are not using the full sample, but focusing on a subset, describe what restrictions you have imposed. What is the response rate of your sample? Justify the inclusion of the variables you are using. Describe your variables, saying precisely what the response categories are, how you handled missing data, etc. Describe what statistical models you utilize.
4) Results: Present and describe results. Present a table of means, standard deviations, and possibly correlations among your variables or, if you have a set of categories, the percentage distribution (i.e., present descriptive statistics). Lead your reader through these tables and figures, saying what they show. If you are making comparisons between groups, e.g., males vs. females, present the statistics for each group as well as for the entire population. Present your regression results in tables, including: the coefficients and their standard errors or t-ratios or probability values, the constant, and the R2 . If you have a categorical dependent variable, use an appropriate logistic regression model. Justify the inclusion of the variables in your model. Why is it there? What sign and, if possible, what magnitude do you expect for each coefficient? Only present more than one model if you have a reason to do so. If not, just present the most complete model. The condition under which you want to present alternative (or nested) models is when you are interested in whether the effect of one variable is explained by another variable – that is, when you are interested in the effect of introducing one or more control variables. If you do present nested models, you need to justify the order in which you build up your model. This should not be arbitrary but should have a clear logic and should depend upon which hypotheses about spurious effects. Make your tables and figures aesthetically appealing. Look at the course examples of well-presented tables and model after these. Include what model you are running, what the data are and the sample size (n=XXX) in your table 3 heading, and what the numbers represent in your table notes (e.g., “Standard errors in parentheses”). Make your tables easy to understand for the reader. Name dichotomous variables with the “positive” category. For example, better to call a variable “male” than “sex” since the reader cannot tell from “sex” whether males or females have the higher value. Also, you may need to reverse the coding of variables to better represent your theoretical objectives.
5) Summary and discussion: Remember that you are social scientists in your discussion and conclusions. Describe the social significance of your findings. In this section you tie your findings back to your originating question, explaining how your specific findings address your research question. Also include the limitations of your research (all research has limitations) and what future work might accomplish. Finally, describe the implications of your study for the state of knowledge in this area, future research, and polices. 6) References: Include references at the end of the paper (see format in ASR).
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