Module 2 - SLP The data are obtainable in this SPSS Data set . Here is the full codebook . Please note


Module 2 - SLP

The data are obtainable in this SPSS Data set . Here is the full codebook .

Please note that these are RAW data in the classical sense of "raw".  There are assorted bad codes for some variables, probably missing data codes of various types that were not documented (e.g., sex = -5).  You'll have to use your good judgment in removing bad codes, at least from anything you report.  We will be VERY annoyed if in addition to calculating the mean of "sex", you don't take out the "-5" or "99" codes and thus wind up reporting the "average sex" to be about 36.4.

To help you in this process, you should consult this very excellent guide on Data Cleaning prepared by Tulane University.  These are very important skills to learn and practice for your future research.

If you have problems downloading the large data set you can work on a reduced set which only contains 48 variables by getting into the following SPSS data set .

Please note that there is an available "gloss" file that will walk you through the analysis if you need help beyond that made available by the PowerPoint Guide presentations linked below. Don’t hesitate to use the Gloss if you need it, but the more of the work you can do without recourse to it, the more you’ll be achieving personal mastery of this material, which is important.

  1. Look over the data, and try to bond and form a personal relationship with it.  The first steps in such bonding usually involve description of the data.   Be sure to remove incorrect values before proceeding further -- the Guide on Reliability Analysis covers this as part of its discussion of reliability.
  • Create appropriate descriptive statistics for the 4-5 of the main demographic descriptors of the sample that interest you the most.  Present them suitably, and comment on anything that looks interesting to you about the survey sample.
  • Create appropriate descriptive statistics for the Internet Use variables (EMAIL to BANK).  Which are the most frequent uses of the Internet reported by the respondents?  The least frequent?  Anything about this ordering that you didn't expect to find?
  • Create appropriate descriptive statistics for the Online Purchase variables (CLOTHES to VIDEOS).  Which are the most frequent Internet purchases?  The least frequent?  Anything about this ordering that you didn't expect to find?
  • Let's now try some scaling ourselves.  You should review this brief write-up on Cronbach's alpha statistic , and then try the following steps:

    1. Using ANALYZE : SCALE : RELIABILITY ANALYSIS in SPSS (or the equivalent -- see the Guide to Reliability Analysis ) calculate Cronbach's alpha for a scale made up of the Internet Use variables.  If its reliability could be improved by removing any of the variables, what are they?  What is your final list of variables for this scale, and the corresponding alpha?
  • Using ANALYZE : SCALE : RELIABILITY ANALYSIS in SPSS (or the equivalent), calculate Cronbach's alpha for a scale made up of the Online Purchase variables.  If it could be improved by removing any of the variables, what are they?  What is your final list of variables for this scale, and the corresponding alpha?
  • Now let's use the scales:

    1. The reliability analysis procedure may have tested what such a scale might look like, but it didn’t actually create the new scale values for you. You have to do that yourself by actually adding up the values of all the items comprising the scale. So, using COMPUTE, create the two summated scales you have defined for Internet Use and Online Purchase by summing up the values of the included variables.  ( Be sure to remove incorrect values before proceeding further, if you didn't do so earlier -- The Guide to Reliability Analysis covers this aspect as well. ) Create and interpret appropriate descriptive statistics for these two scales.
  • Scale reliability is important, but so is validity. A commonly used test for validity involves seeing how well the scale values correspond to other specific indicators of the same general thing. Here, we have single-item indicators of the same general constructs -- Hours of internet usage/week (INTUSE) and Money spent online in past 1 month (GN020701) – that would allow such a test. Using CORRELATION or REGRESSION , calculate coefficients for the relationships between each of the two scales and its corresponding single-item indicator. Interpret the results.
  • Formulate a hypothesis about the relationship between one or the other of the two new scales that you just put together – Amount of Internet Use or Amount of Internet Purchasing (dependent) and one of the categorical demographic variables (independent).  Using T-TEST or ANOVA (depending on whether the independent variable has just two or more than two levels), test the hypothesis.  Is it supported?  What have you learned?
  • Transfer your findings to a Word document, add suitable text, and finalize your report.
  • Price: $30.67
    Solution: The downloadable solution consists of 26 pages, 467 words.
    Deliverable: Word Document


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