Module 4 SLP

First , use CTBS Language NCE Score (interval) as DV, and gender (categorical; coded 0=female, 1=male) as IV; try some predictive models, in each case requesting as many optional pieces of output as seem helpful to you:

ANALYZE : COMPARE MEANS : INDEPENDENT SAMPLES T-TEST (remember to specify the two categories of gender )

ANALYZE : COMPARE MEANS : ONE-WAY ANOVA

REGRESSION : LINEAR

Then compare the output from these three analyses.  Note which numbers are the same, and what they represent.  Interpret the output from each test specifically, and identify what each flavor of analysis tells you and what it doesn't.

Second , use CTBS Language NCE Score (interval) as DV, and gender (categorical; coded 0=female, 1=male) and engprof2 (categorical, recoded from engprof as 0=no non-English proficiency [ engprof =0 or 4], 1=some non-English proficiency [ engprof =1, 2, 3], as IV; try as predictive models:

GENERAL LINEAR MODEL : UNIVARIATE [ gender and engprof2 are both fixed factors]

[Multiple] REGRESSION : LINEAR [include interac [interaction (product) of gender and engprof2 ]  along with gender and engprof2 as IVs]

Again, compare the output, and interpret the results.  In this case, all three IV's are "dummy variables", 0/1 codings reflecting the presence or absence of a particular effect.    In each case, the variable is coded as 1 if the characteristic is true, and 0 if it is not.  Thus, a coefficient for gender would actually be interpreted as the effect of being male (the category coded as "1"), a coefficient for engprof2 as the effect of having some non-English proficiency, and a coefficient for interac as the effect of being a male with some non-English proficiency.

Third , let's try out some coefficient interpretation. Start by reviewing the sections of the UNESCO IDAMS Guide on multiple regression ; it's all useful, but for the purposes of this assignment it's OK to scroll down to the part on standardized regression coefficients.  Trochim also has useful material here, as do several of the other regression sources.  They'll help you with this interpretive exercise.

Then set up a multiple regression model to predict algebra scores (DV) from four rather different variables (IV's): spring math grades (i.e., previous course performance), CTBS Math scores (presumably a measure of basic talent/ability), days absent (a measure of "showing up"), and school (a control variable representing environmental forces).  One can make a reasonable case as to why each of these factors might be a predictor of algebra performance.

Start with basic descriptive statistics and frequencies on each of these variables.  What do you learn about their distributions from this?  What about the different scales on which they are measured?

Run the above regression model and generate the appropriate coefficients.  Interpret the results in text.  What's significant?  Not significant?  Summarize your results in a sentence or two.

Finally, comment on the differences between the unstandardized coefficients ("b's) and the standardized coefficients ("betas").  Interpret your results by showing what can you learn from each set of coefficients that you can't learn from the other.  You can refer to the presentation on interpreting regression coefficients if you need to do so.

FOURTH, try at least one more analysis along these lines of your own choosing.  Please say why it's interesting and you chose it, how you set it up, what you found, and your interpretation of the results.

Then, as usual, transfer your output to Word® and format it in an appropriate manner.  Conclude your report with a couple of paragraphs or so of summary analysis, in which you provide an overall interpretation of your data analysis activities and draw conclusions of appropriate scope about their value and applicability.

Price: $26.75

Solution: The downloadable solution consists of 13 pages, 1375 words and 11 charts.
Deliverable: Word Document