HENRY

Lab 4: Association and Regression

In this lab, we will investigate how total time spent on homework is associated with a student’s grades among the national sample of 10th grade students given in the NELS data collected in 1990. Follow the directions below to complete the analysis with SPSS.

Print out the outputs and submit in print. Also submit this worksheet with your answers to the questions.

1. Open the SPSS dataset of “short NELS” that you created in Lab 1. If you don’t have the SPSS dataset ready to use, download the original data file “short_nels.csv” from Blackboard and follow Lab 1 Problem Set 2 to import it into SPSS.

2. Download “nels_coding_system.pdf” from Blackboard. Follow the prompts below to locate the two variables of interest (i.e., time on homework, grades) in the dataset and learn about their coding systems.

1. Open “nels_coding_system.pdf”. Search the keyword “homework” to learn about all variables related to homework available in this dataset.

2. Find out the variable that is total time spent on homework out of school in 10th grade. Write down its variable name here.

   (Note: NELS is a longitudinal study that followed the sampled students over 12 years and collected data in 4 waves. The first wave was done in 1988 when students were in their 8th grade, which is also called the “base year”. The variables from this base year have their names starting with “by”. The 10th grade is the first follow-up wave and the variable names start with “f1”.)

3. The dependent variable, student grades, is named ffugrad in the SPSS dataset. This is a composite score created by averaging each student’s math (f1s39a), English (f1s39b), history (f1s39c), and science (f1s39d) grades at the 10th grade.

4. Use the variable names mentioned above as the keywords for searching the coding system PDF. This will lead you to coding systems of these variables, that is, what are the possible values this variable can assume, and what does each value represent. Write down the coding systems for the two variables of interest in the space below.

   (Note: ffugrad does not have its own coding system explained in the file. But because ffugrad is a composite score created using subject-specific grade variables, we can use one of the original variables, such as f1s39a, to find the coding system.

3. Go to the SPSS. Follow the directions below to create a new, simpler dataset that includes only the variables of interest above.

4. 1. Open a new blank dataset: FileàNewàData

   2. Follow the directions below two times to copy and paste each of the two variables above

      from the short NELS dataset to the new dataset.

      Go to the short NELS SPSS data, Data ViewàEditàGo to VariableàEnter the variable name in the text boxàGoàSelect the entire column, right-click and Copyà Go to the new blank dataset, Data ViewàSelect the first (blank) column, right-click and Paste.

   3. After both variables have been pasted into the new data file, save the new dataset.

   3. As the coding systems found above show, neither of these two variables of interest is a perfect continuous measure. For this lab’s purposes, we will treat ffugrad as if it’s a continuous measure. But for time on homework, follow the directions below to convert it into a more reasonably continuous measure --- hours.

      Open the new (simpler) SPSS dataset, Data ViewàTransformàRecode into Different VariablesàSelect the homework variable (f1s36a2) as the Input VariableàEnter homework as the Output Variable Name, click ChangeàClick Old and New Valuesà For each pair of old (current) and new values below, enter the old and the corresponding new values in the text boxes then click AddàAfter all pairs are defined, click ContinueàOK. 

   
   

5. 5. Follow the directions below to create a scatter plot of the two variables. On the side of the printed graph, interpret the relation between the two variables.

      GraphsàLegacy DialogsàScatter/DotàSimple ScatteràSelect ffugrad for the Y Axis, select homework for the X Axis, create a title for the graphàOK.

   6. Follow the directions below to find the Pearson’s correlation coefficient between the two variables. On the printed output, highlight/circle the Pearson’s correlation coefficient value, and then interpret this quantity on the side in terms of the direction and strength of the linear association between time on homework out of school and grades.

      AnalyzeàCorrelateàBivariateàSelect ffugrad and homework as the Variablesà OK.

   7. Follow the directions below to build a simple linear regression model to predict grades using

   the total time spent on homework out of school.

   1. Add a fitted linear regression line on the scatter plot.
      Go to the output window, double-click the scatter plotàElementsàFit Line at Total

      à Apply.

   2. Run the linear regression model analysis.

      AnalyzeàRegressionàLinearàDependent: ffugrad; Independent: homeworkà OK.

   3. Write out the fitted model using the coefficients given in the outputs.

   4. Interpret the intercept.

   5. Interpret the slope.

   6. Find out the R2 value from the outputs and interpret it. 

Lab 3: Descriptive Statistics

Complete the following tasks using SPSS. Print out the outputs and submit in print. For questions you need to answer, write down the answers on the printed outputs by the table or graph.

Problem Set 1

Download the dataset “heights.sav”.

1. Follow the directions below to create value labels for variable gender: 0 = male, 1 = female.

   Variable ViewàValuesàFor each different value, enter the value and label and then click Add, after all pairs are defined, click OK.

2. Follow the directions below to create a frequency table for gender. How many males and females are there in the sample and what are the corresponding proportions?

   AnalyzeàDescriptive StatisticsàFrequenciesàVariable(s): GENDERà Make sure below the boxes “Display frequency tables” is checkedàOK.

3. Follow the directions below to select all of the male cases.

   DataàSelect CasesàIf condition is satisfiedàIf...àSelect GENDER into the box on topàclick “=” and then click “0”àContinueàOK

4. Follow the directions below to find the following for the height of males in the dataset: mean, standard deviation, variance, max, min, range. What is the relationship between standard deviation and variance?

   AnalyzeàDescriptive StatisticsàDescriptivesàVariable(s): HEIGHTà Options...àCheck all items required aboveàContinueàOK.

5. Create a histogram for the heights of males. Describe the shape of the distribution.

6. Select all cases. Then follow the directions below to create a boxplot graph to compare the distribution of heights for the two gender.

   GraphsàLegacy DialogsàBoxplotàChoose Simple and Summaries for groups of cases à Variable: HEIGHT; Category Axis: GENDER à OK.

7.  Follow the directions below to explore the dataset. Produce the following output for height for each gender: means, medians, standard deviations, min, max, range, quartiles (Q1, Q2, Q3), interquartile range, extreme values, boxplots, stem-and-leaf plots, and histograms. Interpret the results.

   AnalyzeàDescriptive StatisticsàExploreàDependent List: HEIGHT; Factor List: GENDERàPlay with the specification dialogue boxes to get all items required aboveàOK.