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.

• 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.

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.

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.

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?

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)

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

Coding IV

• What is your favorite NFL team? – Packers– Vikings – Bears– Lions– Other (Specify): ___________

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)

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

Code Books

• List of all variables in the “data set” – Variable labels: what exactly does this variable

measure– attributes/codes: list of attibutes and codes for

each (ONLY IF NEEDED) – level of measurement, and so forth.

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

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