Statistics 101
Data Analysis and Statistical Inference
 

Instructions for lab 8

Lab Objective

The purpose of the lab is to analyze a data set from scratch, using methods that we have learned in class.

Lab Procedures

The television series Sesame Street is concerned mainly with teaching preschool skills to children age 3-5, with special emphasis on reaching economically disadvantaged children.  The show is designed to hold young childrens' attention through action oriented, short duration presentations teaching specific preschool cognitive skills and some social skills. Each show is one hour and involves much repetition of concepts within and across shows.  

Does Sesame Street help economically disadvantaged children 'catch-up' with economically advantaged children?  In the early 1970s, researchers at Educational Testing Service (the company that runs the SAT) ran a study to evaluate Sesame Street.   The researchers sampled children representative of economically advantaged and disadvantaged populations from five different sites in the United States.  To ensure the study contained a group of children that watched Sesame Street regularly, they randomly assigned children either to receive encouragement to watch Sesame Street or not to receive encouragement.  Those assigned to encouragement were given promotional materials, and received weekly visits and phone calls from ETS staff.  Those assigned not to receive encouragement did not get this attention.

The children were tested on a variety of cognitive variables, including knowledge of body parts, knowledge about letters, knowledge about numbers, etc., both before and after viewing the series.

Open the data set sesame.jmp by clicking on the link.  These data are part of a larger data set used to evaluate the impact of Sesame Street.  The names of variables are shown in the code book at the end of the lab instructions.  Note that all the variables are currently coded as continuous  (quantitative) variables.  You should recode any nominal (qualitative) variables by clicking on the blue Cs in the box to the left with the variable names and selecting "Nominal".

Questions:

1.  Did encouragement cause children to watch Sesame Street more frequently?  Did encouragement result in higher tests scores on average?

2.  What do the data suggest about whether watching Sesame Street helped children?   Compare within types of kids.

3.  What do the data suggest about whether Sesame Street helped economically disadvantaged children catch up?

Before you come to lab, think of an analysis plan to address the following issues:
1. The general question you will answer, and a hypothesized answer (i.e. what results will support your hypothesized answer?).
2. The comparison groups you will use.
3. The outcome (dependent, response, Y) and predictor (independent, X) variables you will use to answer the question.  You can look at one or two outcome variables, or more if you'd like.
4. The statistical method(s) that you will use to help answer the question.
5. What results from these specific statistical methods are needed to support your hypothesized answer?

Take this analysis plan with you to lab. You then can ask the TAs or other students about your plans, make adjustments, and use the remaining time to begin your analyses.  You'll have a week to complete your analyses.  You can ask the TAs or the professor for advice on your data analysis plans at any time during the week.

Turn in a typed summary of your analyses (not to exceed 1 type written page, single space and 12 point text).  In the write up, explain the analyses you did, and your conclusions.  Provide numerical evidence from the data to support your conclusions.  You don't have to tell me all the JMP commands you used.  Just tell me what you found.  For example, you might say "The values of test scores for the kids who were encouraged are typically higher than those who were not encouraged.  The means are ___and ___ respectively, with SDs of ___ and ___."  

This write up counts for 30 lab points.

Important Note

These data are challenging to analyze, particularly for Question #2 and #3.  There was a lot of controversy over the conclusions of ETS (who found it does help) because of concerns related to the study design and potential confounding.  Analyze Question #2 and #3 as best you can, thinking about potential confounding variables that could affect your conclusions.  Perform analyses for Question #2 assuming those confounding variables are not a problem.  But, explain in your last paragraph how they might be a problem.

Code book with variable names

id : subject identification number

site :   1 =Three to five year old disadvantaged children from inner city areas in various parts of the country.
           2 = Four year old advantaged suburban children.
           3 = Advantaged rural children.
           4 = Disadvantaged rural children.
           5 = Disadvantaged Spanish speaking children.

sex   male=1, female=2

age   age in months

viewcat  frequency of viewing
              1=rarely watched the show
              2=once or twice a week
              3=three to five times a week
              4=watched the show on average more than 5 times a week

setting:    setting in which Sesame Street was viewed, 1=home 2=school

viewenc :  treatment condition    1=child encouraged to watch,  2=child not encouraged to watch

prebody :  pretest on knowledge of body parts (scores range from 0-32)

prelet :  pretest on letters (scores range from 0-58)

preform : pretest on forms (scores range from 0-20)

prenumb : pretest on numbers (scores range from 0-54)

prerelat : pretest on relational terms (scores range from 0-17)

preclasf : pretest on classification skills

postbody : posttest on knowledge of body parts (0-32)

postlet :  posttest on letters (0-58)

postform :  posttest on forms (0-20)

postnumb : posttest on numbers (0-54)

postrelat : posttest on relational terms (0-17)

postclasf:  posttest on classification skills

peabody:  mental age score obtained from administration of the Peabody Picture Vocabulary test as a pretest measure of vocabulary maturity