Essay On Descriptive Statistics In Spss

Descriptive Statistics

Descriptive statistics comprises the kind of analyses we use when we want to describe the population we are studying, and when we have a population that is small enough to permit our including every case.

For example, we might want to describe a physics class and compare it to a class of English literature. We might want to compare the gender composition of the classes, the math attitudes of the two classes, the familial demands and supports of the two classes. Descriptive statistics would allow us to do this.

  • The classes are small enough that we can include the whole class in our studies.
  • We would have to agree on how to measure gender, math attitude, and familial demands.
  • We could then interview the students in the classes, or survey them, or ask them to tell their stories about these issues and code them by content analysis.
  • Then we COULD DESCRIBE how these issues affect the members of the classes we studied, and how these variables are related in those classes.
  • We COULD NOT CONCLUDE that our results could be generalized to all physics and english literature classes because we have no idea whether the classes in our study were REPRESENTATIVE OF all physics and English literature classes.
Inferential Statistics

The important keys to the difference between descriptive and inferential statistics are the capitalized words in the description: COULD DESCRIBE, COULD NOT CONCLUDE, AND REPRESENTATIVE OF.

Descriptive statistics can describe the actual sample you study. But to extend your conclusions to a broader population, like all such classes, all workers, all women, you must be use inferential statistics, which means you have to be sure the sample you study is representative of the group you want to generalize to.

This means you can't do a study at the local mall and claim that what you find is valid for all shoppers and malls.

You can't do a study on college sophomores and claim that what you find is valid for the general population.

You can't give a women's movement that includes a majority of a single ethnic group and claim that what you find is valid for women of all ethnic groups.

As you can see, descriptive statistics are useful and serviceable if you don't need to extend your results to whole segments of the population. But the social sciences tend to esteem studies that give us more or less "universal" truths, or at least truths that apply to large segments of the population, like all teenagers, all parents, all women, all perpetrators, all victims, or a fairly large segment of such groups.

Leaving aside the philosophical and methodological soundness of such a search for some kind of general conclusion, different statistical approaches must be used if you aspire to generalize. And the primary difference is that of SAMPLING. You must choose a sample that is REPRESENTATIVE OF THE GROUP TO WHICH YOU PLAN TO GENERALIZE.

Tests of significance are about this problem of generalization. A Chi-Sqaure or a T-Test tells you the probablility that the results you found in the group you studied are representative of the population that group was chosen to represent. Put in other terms that you will hear frequently, Chi-Sqaure or a t-test gives you the probability that the results you found could have occurred by chance when there is really no relationship at all between the variables you studied in the population.

The Descriptives procedure can produce a select number of descriptive statistics on any variable. (Note, however, that the descriptive statistics generated are only suitable for numeric scale variables). The Descriptives procedure is best used when you want to compare the descriptive statistics of several numeric variables side-by-side.

To run the Descriptives procedure, select Analyze > Descriptive Statistics > Descriptives.

The Descriptives window lists all of the variables in your dataset in the left column. To select variables for analysis, click on the variable name to highlight it, then click on the arrow button to move the variable to the column on the right. Alternatively, you can double-click on the name of a variable to move it to the column on the right.

Selecting the Save standardized values as variables check box will compute new variables containing the standardized values (also known as Z scores) of each of the input variables. Recall that the standardized value of a variable is computed by subtracting its mean and then dividing that difference by the standard deviation:

$$ Z = \frac{X - \mu}{\sigma} $$

By default, the Descriptives procedure computes the mean, standard deviation, minimum, and maximum of the variable. Clicking Options will allow you to disable any of the aforementioned statistics, or enable sum, variance, range, standard error of the mean (S.E. mean), kurtosis, and skewness. You can also choose how you want the output to be organized:

  • Variable list will print the variables in the same order that they are specified in the Descriptives window.
  • Alphabetically will arrange the variables in alphabetical order.
  • Ascending means will order the output so that the variables with the smallest means are first and the variables with the largest means last.
  • Descending means will order the output so that the variables with the largest means are first and the variables with the smallest means are last.

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