Assignment 1 social performance

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Assignment 1 social performance

For instance, we use inferential statistics to try to infer from the sample data what the population might think.

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Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

Here, I concentrate on inferential statistics that are useful in experimental and quasi-experimental research design or in program outcome evaluation. Perhaps one of the simplest inferential test is used when you want to compare the average performance of two groups on a single measure to see if there is a difference.

You might want to know whether eighth-grade boys and girls differ in math test scores or whether a program group differs on the outcome measure from a control group. Whenever you wish to compare the average performance between two groups you should consider the t-test for differences between groups.

Most of the major inferential statistics come from a general family of statistical models known as the General Linear Model.

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Given the importance of the General Linear Model, it's a good idea for any serious social researcher to become familiar with its workings.

The discussion of the General Linear Model here is very elementary and only considers the simplest straight-line model.

However, it will get you familiar with the idea of the linear model and help prepare you for the more complex analyses described below. One of the keys to understanding how groups are compared is embodied in the notion of the "dummy" variable. The name doesn't suggest that we are using variables that aren't very smart or, even worse, that the analyst who uses them is a "dummy"!

Perhaps these variables would be better described as "proxy" variables. Essentially a dummy variable is one that uses discrete numbers, usually 0 and 1, to represent different groups in your study. Dummy variables are a simple idea that enable some pretty complicated things to happen.

For instance, by including a simple dummy variable in an model, I can model two separate lines one for each treatment group with a single equation.

To see how this works, check out the discussion on dummy variables. One of the most important analyses in program outcome evaluations involves comparing the program and non-program group on the outcome variable or variables.

How we do this depends on the research design we use. Because the analyses differ for each, they are presented separately. The simple two-group posttest-only randomized experiment is usually analyzed with the simple t-test or one-way ANOVA.

The quasi-experimental designs differ from the experimental ones in that they don't use random assignment to assign units e.

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The lack of random assignment in these designs tends to complicate their analysis considerably. In the Regression-Discontinuity Designwe need to be especially concerned about curvilinearity and model misspecification. Consequently, we tend to use a conservative analysis approach that is based on polynomial regression that starts by overfitting the likely true function and then reducing the model based on the results.

Assignment 1 social performance

The Regression Point Displacement Design has only a single treated unit. When you've investigated these various analytic models, you'll see that they all come from the same family -- the General Linear Model.

An understanding of that model will go a long way to introducing you to the intricacies of data analysis in applied and social research contexts.Assignment 1: Social Performance of Organizations Debbie Shimhue Business and Society Ms.

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