1) The document describes steps to prepare data in Microsoft Excel for analysis in IBM SPSS. It involves changing sex variables from text to numeric, exporting the Excel file to a text file for SPSS, and opening the file in SPSS. 2) Descriptive statistics are calculated in SPSS, including measures of central tendency, variability, and normality. Percentiles and z-scores are also computed. 3) Further computations include p-values using z-scores and percentile ranks. The outputs are examined and files are saved.

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In these exercises, you will be using the Microsoft Excel and IBM SPSS softwares. In the
first few steps, you will be preparing the data for SPSS using Microsoft Excel.
A. Opening the Spreadsheets containing the Data
1. Open the  WORKSHOP_DATA folder at the Desktop or with the use of the
Windows Explorer look for the  WORKSHOP_DATA folder in the Drive D: and
open it.
2. Double – click SAT16WO2.XLS in the  WORKSHOP_DATA folder to open it.
3. Save the spreadsheet file with another name  File – Save – As “EXER02.XLS”.
The purpose of doing this is to reserve the original file. In case, you make mistakes in
the process, you can reopen the original file.
The file SAT16W02.XLS contains 961 examinees who took the Scholastic
Achievement Test last school year 2010 – 2011. The file contains the actual data and
the names of these examinees are replaced with labels STDN0001 to STDN0961
B. In this part, you will be changing the SEX variables for male and female to 1 and 2
respectively.
4. Insert two new columns after SEX. At column header E, Right–Click and then Insert.
Right–Click column header F and then Insert again to insert the second blank column.
5. At the cell E3, type =IF(D3="m",1,2) and press <Enter>. Click E3 and at the lower
right corner of this cell, double – click the small square to apply the same formula to
the rest of the items.

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6. Highlight the range E3:E963, and then Right–click  Copy. Click F3, and then Right
–Click  Paste Special, choose Values under the Paste section. Click OK.
The numbers pasted on column F are simply values that do not include a formula.

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7. Transfer the title “SEX” from D2 to F2. Delete the columns D and E. Click the
column headers D and E, Right – Click and choose Delete. Save your file.
The letters M and F are now replaced with 1 and 2, respectively.
C. In this part, you will be exporting the data from Microsoft Excel to IBM SPSS
8. Click File  Save As. The File – Save As Dialog Appears. In the ‘Save as Type’
section, select ‘Text (Tab delimited)’. Filename is still ‘EXER02’. Then click Save.
9. Click the Yes button.

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10. Click File  Exit and then click No button.
11.Open IBM SPSS 16.0 or different version. If the opening window appears, just click
the Cancel button.
12.Click File  Read Text Data.

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13. The Open Data dialog box appears. Go the  WORKSHOP_DATA folder and
look for ‘EXER02.TXT’ and then click the Open button.
14.The Text Import Wizard appears. Click Next in ‘Step 1 of 6’ window.
15.Click ‘Yes’ to answer the question “Are the variable names included at the top of
your file?. Then, click next.

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16. Click Next on ‘Step 3 of 6’ to ‘Step 5 of 6’. Finally, click ‘Finish’ on ‘Step 6 of 6’.
17. Save your file as ‘EXER02.SAV.
D. In this part, you will obtain the Central Tendency and Variability of the raw scores of
the 961 examinees using IBM SPSS. This part will summarize the given data set. The
measures of central tendency and variability are critical components of other statistical
procedures.
18. Currently, the file ‘EXER02.SAV’ is open in IBM SPSS, from the ANALYZE menu
choose DESCRIPTIVE STATISTICS then choose FREQUENCIES.
19. Select the RSCORE variable and move it to the VARIABLE(S) box by clicking the
right arrow . Uncheck the box that says DISPLAY FREQUENCY TABLES.

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Click on the STATISTICS button. Under CENTRAL TENDENCY check MODE,
MEDIAN, and MEAN. Under DISPERSION check RANGE, STANDARD
DEVIATION. Check SKEWNESS and KURTOSIS under DISTRIBUTION section,
and then click CONTINUE button.
20. Click CHARTS…, select HISTOGRAM and WITH NORMAL CURVE. Click the
CONTINUE button. Then click OK button.
21. View the output, and then save the viewer file as ‘EXER0201”. These are the
information you can see in the output.

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Percentile Scores
Percentiles of the raw scores in
the “Cumulative Percent” column.
The SPSS output shows that Skewness (=
0.327) and Kurtosis (= –0.488) is less than + / –
1.0
This implies that RSCORE is normally
distributed, meaning Z–Scores can be used to
estimate the probability value.
Statistics data also shows:
Standard Deviation of 12.264
Mean of 42.91
E. In this part, you will obtain the Percentile scores of the 961 raw scores. This is the
second way of obtaining the percentile scores.

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22. Sort the data set. Click DATA and SORT CASES. Move the RSCORE variable into
the SORT BY box. The SORT ORDER is ASCENDING. Then click OK.
Then you can see, the RSCORE values are sorted from lowest down to highest value.
23. Next, rank the RSCORE values by clicking TRANSFORM > RANK CASES. Move
the RSCORE into the VARIABLE box and then click OK. An additional column
“RRSCORE” is added showing the rank of the values in RSCORE.
You can set the decimal places to 0 of the values in column RRSCORE, by clicking
the VARIABLE VIEW tab. Save your file.
24. Then, compute the percentile by clicking TRANSFORM > COMPUTE VARIABLE.
Type “PRCNTL_SC” in the TARGET VARIABLE box. “PRCNTL_SC” will be the
label of the additional column. Select RRSCORE in the box and then click RIGHT
ARROW to move the variable RRSCORE into the NUMERIC EXPRESSION box.

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Complete the whole expression by typing the remaining items. In this expression, we
subtract 0.5 from RRSCORE divided by the total cases of 961 and multiplied the
whole computation by 100. Then click OK.
You can adjust the number of decimal
places again. Just go to the
VARIABLE VIEW tab and edit the
PRCNTL_SC properties.
F. In this part, you will compute the Z–Scores which can be used to estimate the
probability value (p–value). You will also use the Z–Scores to compute the percentile
ranks.
((RRSCORE – 0.5)/961)* 100

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Z–Scores are a transformation of individual raw scores into a standard form, where
the transformation is based on knowledge about the standardization sample’s mean
and standard deviation.
The formula for computing Z–scores is the individual raw score (X) minus the mean of
the scores obtained by the standardization sample (M), divided by the standard
deviation of scores obtained by the standardization sample (sd). Z–scores have a mean
of 0 and a standard deviation of 1. (BROCK, 2012).
Standard scores tell you how many standard deviation units above or below the mean
a value falls. A score that is one standard deviation below the mean has a Z–score of –
1. A score that is at the mean would have a Z–score of 0 and a score above the mean
will have a Z–score of +1.
25. To derive the Z–scores of RSCORE, click ANALYZE > DESCRIPTIVE
STATISTICS > DESCRIPTIVES. Click  SAVE STANDARDIZED VALUES AS
VARIABLES. Move RSCORE into the VARIABLE box and click OK.
An additional column labeled “ZRSCORE” is shown.
The Histogram Section
26.Next, we can generate the HISTOGRAM of the Z–scores. Click GRAPHS >
LEGACY DIALOGS > HISTOGRAM. Select ZRSCORE and move it to
VARIABLE box at the right. Click  DISPLAY NORMAL CURVE. Click OK.

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The histogram of the raw scores using Z–scores.
The p – value Section.
27. To compute the p–value using the Z–scores. Click TRANSFORM > COMPUTE
VARIABLE. Type “P_ZSCORE” in the TARGET VARIABLE box. Select “CDF &
NONCENTRAL CDF” from the FUNCTION GROUP box. Select “CDFNORM”
function from the FUNCTIONS AND SPECIAL VARIABLES box. Click the up
arrow , select “ZSCORE (RSCORE) [ZRS..” from the variable box and click the
right arrow  to replace the ? inside the function CDFNORM(?). Click OK.
Follow what is shown in the diagram.

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The column labeled “P_ZSCORE” is added. This column contains the p–values for
the RSCORES.
The Percentile Rank Section
28. Click TRANSFORM > COMPUTE. Type “PRCTLRANK” in the TARGET
VARIABLE box. Select “CDF & NONCENTRAL CDF” from the FUNCTION
GROUP box. Select “CDF.NORMAL” function from the FUNCTIONS AND
SPECIAL VARIABLES box. Click the up arrow , select “ZSCORE (RSCORE)
[ZRS..” from the variable box and click the right arrow  to replace the first “?”
inside the function CDF.NORMAL(?,?,?). Type “0” for the second “?” represents the
mean and type “1” for the third “?” representing standard deviation. Click OK.
CDF.NORMAL(ZSCORE,0,1)

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The column labeled “PRCTLRANK” is added. This column contains the percentile
ranks of RSCORE.
Then SAVE your work.
References:
Brock, S. E. Descriptive Statistics and Psychological Testing. California State
University, Sacramento. Retrieved April 05, 2012.
Cohen, R. J., & Swerdlik, M. E. (2005). Psychological Testing and Assessment:
An Introduction to Tests and Measurement. (6th
Edition.) McGraw–Hill.
Zucker, S. (2003, December). Fundamentals of Standardized Testing. Retrieved
April 06, 2012 from http://www.hemweb.com.

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Contents of SAT16WO2.XLS:

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