1. Preparing to collect data

2. Make sure you have your materials Surveys –All surveys should have a unique numerical identifier on each page –You can write them on the corners of each page –If pages get separated you can reconnect them Consent forms –Make sure they are signed by me Other materials –Extra pens or pencils –Debriefing forms –Plates, foods

3. Data cleaning Enter the data –I will not ask you to do double entry –But it is common At the end of every entry batch –Choose 10% randomly and check entire entry At the end of entering all data –Choose 10% randomly and check entire entries –So if you entered 80, you should choose 8 and check

4. Data Cleaning Run frequencies on all variables Print out…yes, print out. –You will need to turn it in to me –Reading data on the screen is not a good way to catch errors Read carefully with a red marker –This is the first screening for data entry errors –Your frequencies shouldn’t have anything out of range Keep the syntax

5. Data cleaning What about frequency histograms? This goes to the issue of “skewness” That is second stage cleaning Right now we are concerned with finding errors, not seeing if our data are skewed

6. Data cleaning-missing data Check variables for large amounts of missing data If a variable has a lot of missing, go back and look at the raw data –Was there something strange about the printing or wording of the question? If a variable has more than 25% missing you may have to leave it out of analyses –Because of the way these data were collected we would not expect high rates of missing items

7. Nuts and Bolts Running frequencies –Analyses/frequencies Saving output –File/save as Saving syntax! –Paste! –I recommend making notes in syntax –File/save as

8. Recoding reverse scored data You will create a new variable –Keep the old variable intact –Name the new variable the same name with the suffix “rv” –Label the new variable as “X, rescored in the reverse direction” –Make sure the value labels reflect the new coding! –Keep the syntax! When you enter new data you will have to reverse code newly entered variable scores –So having the syntax will really help!

9. Steps for creating a new variable using reverse coding Keep the syntax! Paste! Transform –Recode into different variables –Choose new name (and add rv suffix) –Hit “change” –Go to old and new values –Input new values –Don’t forget to input missing data marker

10. Creating summary variables Items that are totaled to create a single scale score This can be done two ways –Make sure that reverse coded items are handled first You can SUM items to get a total Preferable is to AVERAGE them –So you don’t lose strings of items with missing data –Numerically the same in terms of analyses

11. Steps for creating a summary (average) score from items Keep the syntax! Don’t forget to paste! Transform/compute variable Choose name mean(var1, var2, var3) Be sure to assign missing data values to new variable

12. After creating summary scores Check frequencies This will show you any errors right away It will also remind you if you haven’t labeled your missing=999 Don’t wait to do this…

13. More cleaning Now you can look at frequencies to actually examine the variables Choose single item variables (those that aren’t only used to form a scale) –E.g. gender, race, single questions Choose summary scores Examine frequencies and histograms

14. Categorical Variables Gender, race, yes/no, good, better best Use frequencies to examine these Examine and note the rate of occurrence of each type of score Save your syntax Save your output Turn it in

15. Continuous (or semi continuous) variables Summary scores are sufficient Anything you would use in an analysis –Not individual items, if you wouldn’t analyze them Use Descriptive Statistics Mean, SD, Range, Skewness, Kurtosis

16. Looking for skew and outliers For total scale scores only Skew and Kurtosis are actually largely visual decisions –You can check the numbers –But you’ll need to examine frequency histograms Outliers are based on numerical guidelines –Any values more than 3 standard deviations from the mean

17. Explore command Analyze/explore Put variables in dependent box Label by IDS Choose output under statistics and plots Now you can examine your data for outliers, and visually for skew and kurtosis Save the syntax and output!