The participants will also learn how to use the STATA manual and help menu for writing commands that they need for conducting various statistical analyses. This assumes that the participants have some basic knowledge of statistics, although the interpretation of the results will be formulated in terms that appeal to the basic intuition behind these concepts. For example, when showing the STATA commands for conducting linear regression analysis and their results we will not cover the assumptions of the linear regression or how coefficients are calculated, although we will briefly discuss how to read the main elements of the output generated. The course gives the participants practical, hands-on training in the use of STATA for conducting statistical analysis through a mix of examples presented by the instructor and a set of applications and exercises that the participants will solve and discuss in the class.Īlthough the course will present the STATA commands for conducting different types of statistical analyses and will briefly discuss their results, it will not teach the statistical theory behind these statistical methods.
The course will walk you through the typical stages of a process of empirical data analysis, from getting the data, through arranging the data in the needed formats and visualizing them, to conducting different statistical analyses and reporting the results in formats required by professional journals. In this course you will learn how to use STATA for conducting basic types of statistical analysis.
Specifically, the course includes the following components:
In this course you will learn how to use Stata for conducting basic types of statistical analysis. No prior experience with STATA is required. If you are unsure of your level of knowledge please get in touch with the instructor. The participants to this course need to have an understanding of elementary statistics and basic knowledge of empirical research design. This means that there is no statistically significant relationship between the variables sex and language in this dataset.Building: N9 Room: Orange lab Building: N9 Room: Orange lab Building: N9 Room: Orange lab Building: N9 Room: Orange lab Building: N9 Room: Orange lab Building: N9 Room: Orange lab Since we are using 0.05 or below as our cutoff point for the significance level, we can see that 0.885 is very much above 0.05 and we then conclude there is no statistical significance of the Chi-squared test. We can see that the Chi-squared value is 0.244, the degrees of freedom is 2 and the significance level is 0.885.
We can see Stata uses the Pearson Chi-squared test ( Pearson chi2) which includes the degrees of freedom in parentheses, the calculated Chi-squared value, and the Pearson r coefficient ( Pr) which is the two tailed significance level. On the bottom of the crosstabulation chart Stata gives us the results from the Chi-squared test. In the code, we also specified the cells to include row and col which are the percentages of the observations of the total sample size for this analysis.
We can see that sex is first in the code and appears in rows while language is written second and appears in the columns. In the output chart Stata shows the crosstabulation of sex by language.