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SPSS Day

SPSS Day. Review Measurement SPSS Introduction / Coding . IC 4 . Professor Plum believes that laziness is the primary reason that students perform poorly on her exam.

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SPSS Day

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  1. SPSS Day Review Measurement SPSS Introduction / Coding

  2. IC 4 • Professor Plum believes that laziness is the primary reason that students perform poorly on her exam. • Jane Q. Graduate student is researching the idea that a person’s level of sweat has an impact on whether they are able to attract a sexual partner. • Larry the telephone guy believes when he is late to fix someone’s phone line, there is a higher likelihood that the customer will scream obscenities at him.

  3. Common Mistakes to avoid on exam • Variable versus an attribute • Lazy vs. Laziness • Screaming obscenities vs. number of obscenities screamed • Always treat “dummy” variables as nominal • Yes/no, have/not have, etc. • Interval/Ratio data • Avoid categories (0-1, 1-2…) • Stick with “number of times…” or “age in years,” “volume in ounces,” etc.

  4. Level of Measurement • A variable must have mutually exclusive and exhaustive attributes • Nominal, Ordinal, Interval, Ratio levels of measurement • Examples: • Income in dollars of overpaid quarterbacks • Whether or not a quarterback is a drama queen • The number of times a quarterback throws the ball after running past the line of scrimmage • Agreement (Strongly agree, agree, disagree) that a T-shirt which says, “We’ll never forget you BRENT” is funny.

  5. Coding • Coding data typically involves assigning numbers to the attributes of a variable. • For some variable, the “codes” are self evident, as the attributes are already numbers. • Variables that have numbers for attributes? • Many variables have attributes that are non-numerical • We must therefore assign numbers to each attribute so that we can utilize SPSS or other programs (in order to analyze the data) • Variables with non-numerical attibutes?

  6. Coding II • Coding data = assinging numbers to attriubtes • This is arbitrary—but others must be able to tell how we coded variables • “CODE BOOKS” • Labeling values in SPSS • What are the attributes of the variable “Sex?” • How would you “code” this variable? • This is a “dummy” variable (always nominal)

  7. Coding III • Which of the following best matches your feeling towards ice cream? • I hate it • I dislike it • I could take it or leave it • I like it • I would kill you to obtain it

  8. Coding IV • What is your favorite NFL team? • Packers • Vikings • Bears • Lions • Other (Specify): ___________

  9. Recoding Data • Researchers can (and do) manipulate variable attributes based on their interest. • If I was only interested in whether or not a person was a Packer fan, what could I do with the “original” data? Packers (1) Vikings (2) Bears (3) Lions (4) Other (Specify): _____ (5) Packers (1) Vikings (2) Bears (2) Lions (2) Other (Specify): ________ (2)

  10. Missing Data • Unfortunate feature in almost all research • Why is data missing? • Coding missing data • Code missing data a something unusual • Convention is negative numbers (-99 or -999) • You must indicate (to SPSS or others using data) that it is missing and not valid

  11. Code Books • List of all variables in the “data set” • Variable labels: what exactly does this variable measure • attributes/codes: list of attibutesand codes for each (ONLY IF NEEDED) • level of measurement, and so forth.

  12. Code book example variable • Variable = NFL_team • Label = “Respondent’s favorite NFL team” • Codes: • Packers = 1 • Vikings = 2 • Bears = 3 • Lions = 4 • Other (Specify): ________ = 5 • Missing = -99 • Refuse to answer = -999 • Measurement: Nominal

  13. In class exercise • Create the variable “nobrett” based on the following survey item: • “Please indicate your level of agreement with the following statement: I would rather lose than have Brett Favre be the quarterback of our football team.” (Strongly Agree) (Agree) (Disagree) (Strongly Disagree) • Input codes and variable label • Add a category/code for “missing data” • Enter data such that 3 individuals are in each category, with one person skipping the question (and coded as missing) • Recode the variable to create a new variable “nobrett_dum” that indicates whether or not a respondent agreed with the statement. • Run a frequency on both variables • Analyze  descriptive stats  frequencies

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