A step by step guide to recoding AGE variables into generational groups in SPSS. Screenshots of every step is provided in an easy to follow tutorial of how to change or transform a list of ages into generational categories in SPSS
2. Precautionary Message:
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After re-coding, please remember to choose the right measurement for
your new variable!
NOTE: Check the codes for your missing values. You may have to recode
those if you have the Silent Generation in the 90 range, for example.
You will have to decide if you will include them, or treat them as outliers
and possibly select them out.
The generational groups are:
GenY = 18 – 27
GENX = 28 (NOT 27) – 43
Boomers = 44 – 60
Traditional (Silent) = 61 – * (89 was actually chosen as a cut-off date for this group see
notes below)
4. •You will see this box
•Choose the variable that you want to recode. In this case, AGE
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5. 1) Type a new name for the variable, e.g. “age1”
2) Under label type “Age Recoded to Generational Groups”
3) Click on “Change”
4) Click on “Old and New Values”
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6. This window will open. The two value buttons will be highlighted. Leave as
is. The old value one will disappear after the subsequent choice.
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7. a) Click on “Range”. Type in “18”
b) and “27” as shown. This is the age range for Generation Y
c) In “Value” box, type in 1
d) Click on “Add” button. This will bring you to the next screen.
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8. • The age range of 18 thru 27 has now been transferred to the Old -> New window
• Add another range (e.g. 28 through 43, which is Generation X). Click “ADD”
• Type “2” in Value box, under “NEW Value”
• Repeat for each range of years. Number each group consecutively
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9. The third age range of 44 – 60 is shown.
This range will be added when you click “Add”
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Follow the previous procedures and you will get the following result.
Click on “Continue”. You will return to the original “Recode into New Variables”
window (next slide)
12. You will get this syntax read out that tells you what you just did.
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13. • Click on the “Variable View” at the bottom of your screen and scroll all the way down
• You will see the new variable “age1”
• NOTE!! Save your data under a new name! (e.g. GSS1-2006_Recoded)
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14. Look at the highlighted area under “Values”.
Click on it. A blue button will appear.
Click on the blue button. A new window will appear.
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• In “Value” box type “2”. In “Label” box type “genX”
• NEXT: In “Value” box type “3”. In “label box” type “genBoom”
• NEXT: In “Value” box type “4”. In “label box” type “genTrad/Slnt”
• Click on “OK”
• NOTE: If you need to change or remove something, click of the item and make
choice
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• All labels have now been entered
•Click “OK”. You will return to the “Variable View” window
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• At the “Variable View”
• Click on blue button that will appear in the highlighted “Label” box
• You will see the labels for each of the values
• You will now run a Frequency analysis…
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• Go to “Analyze”
• Choose “Descriptive” > “Frequencies”
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Scroll down to the bottom of the variables list.
Choose “Age Recoded…”
Transfer to “Variable(s)” window
You will come to the next screen
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Click on “OK”
(This is just a quick check, do not choose any frequencies to run)
NOTE: Measurement level on Age Recoded has not been changed.
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Congratulations!! You have now learned how to recode variables into
groups with specified ranges!
(Please read the cautionary message in the next slide)
NOTE: Remember to choose the appropriate measurement level for
age1 before actually running any analyses!
24. CAUTIONARY MESSAGE!
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Please remember to choose the right measurement for
your new “age1” variable!
The generational groups are:
GenY = 18 – 27
GENX = 28 – 43
Boomers = 44 – 60
Traditional (Silent) = 61 – *
NOTE: You may have to choose an end range for the
Traditional/Silent Generation grouping.
NOTE: Check the codes for missing values in your dataset.
You may have to recode those so that they will not conflict
with ages that are 90+ (see NOTES section in this slide).
Notes de l'éditeur
The age range for Traditional/Silent generation is 61+If you have Silent Generation members in the 90s+ age range, you will have to pay attention to how other variables such as “missing” are coded. Often values such as 90, 99, etc are used to indicate these values. You may have to either (1) choose to define the Traditional/Silent generation withinIn this case you will have to treat age ranges that fall outside of 89 as outliers.You will have to decide if you will include them, or treat them as outliers and possibly select them out.If you choose to include them in your analysis, you will have to recode missing values so that they do not fall into that generation’s age range, thus confusing your data and analysis
The age range for Traditional/Silent generation is 61+ If you have Silent Generation members in the 90s+ age range, you will have to pay attention to how other variables such as “missing” are coded. Often values such as 90, 99, etc are used to indicate missing values. You may have to either (1) choose to define the Traditional/Silent generation within a range with a cut-off age (e.g. “89” is shown as the cut-off age)In this case you will have to treat age ranges that fall outside of 89 as outliers. If so, you will have to decide if you will select them out, for example OR: (2) You will have to recode missing values. Why? Often missing values are coded with values in the 90+ range. If you choose to include age values in your analysis that are in the 90+ range, you will have to recode those missing values in your data so that they do not fall into that generation’s age range, thus confusing your data and subsequent analysis
The age range for Traditional/Silent generation is 61+If you have Silent Generation members in the 90s+ age range, you will have to pay attention to how other variables such as “missing” are coded. Often values such as 90, 99, etc are used to indicate these values. You may have to either (1) choose to define the Traditional/Silent generation withinIn this case you will have to treat age ranges that fall outside of 89 as outliers.You will have to decide if you will include them, or treat them as outliers and possibly select them out.If you choose to include them in your analysis, you will have to recode missing values so that they do not fall into that generation’s age range, thus confusing your data and analysis