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The Dummy's Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April,2001 ©Amy Gamble4/30/01 All Rights Rerserved
© Amy Gamble 4/30/01 All Rights Rerserved The Dummy’s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001
TABLE OF CONTENTS PAGE Helpful Hints for All Tests...... .1 Tests for Numeric Data 1.Z-Scores 1 2.Helpful Hints for All T-Tests........... 2 3. One Group T-Tests....2 4.Independent Groups T-Test..... .3 5.Repeated Measures (Correlated Groups or Paired Samples)T-Test...........3 6.Independent Groups ANOVA....................... 4 7.Repeated Measures (Correlated Groups or Paired Samples)ANOVA..................4 8.Correlation Coefficient.4 9.Linear Regression. Tests for Ordinal Data 1.Helpful Hints for All Ordinal Tests. 7 2.Kruskal-Wallis H. > 3.Friedman's 4.Spearman's............ 7 Tests for Nominal Data 1.Helpful Hints for All Nominal Tests 8 2.Chi-Square Goodness-of-Fit....... P 3.ChiSquare Independence8 4.Cochran'sQ.… .8 5.Phi or Cramer's V(Correlations for Nominal Data).9 父
ii TABLE OF CONTENTS PAGE Helpful Hints for All Tests................................................................................................1 Tests for Numeric Data 1. Z-Scores.................................................................................................................1 2. Helpful Hints for All T-Tests.................................................................................2 3. One Group T-Tests.................................................................................................2 4. Independent Groups T-Test ...................................................................................3 5. Repeated Measures (Correlated Groups or Paired Samples) T-Test .....................3 6. Independent Groups ANOVA................................................................................4 7. Repeated Measures (Correlated Groups or Paired Samples) ANOVA .................4 8. Correlation Coefficient ..........................................................................................4 9. Linear Regression ..................................................................................................5 Tests for Ordinal Data 1. Helpful Hints for All Ordinal Tests .......................................................................7 2. Kruskal – Wallis H.................................................................................................7 3. Friedman’s .............................................................................................................7 4. Spearman’s.............................................................................................................7 Tests for Nominal Data 1. Helpful Hints for All Nominal Tests.....................................................................8 2. Chi-Square Goodness-of-Fit ..................................................................................8 3. Chi-Square Independence ......................................................................................8 4. Cochran’s Q ...........................................................................................................8 5. Phi or Cramer’s V (Correlations for Nominal Data) .............................................9
SPSS Guide to Data Analysis Page 1 of8 For All Tests Remember that the Significance (or Asymp.Sig.in some cases)needs to be less than 0.05 to be significant. The Independent Variable is always the variable that you are predicting something about (i.e.what your Ha predicts differences between,as long as your Ha is correct).The Dependent Variable is what you are measuring in order to tell if the groups (or conditions for repeated measures tests)are different.For correlations and for Chi-Square,it does not matter which one is the Independent or Dependent variable. Ha always predicts a difference (for correlations,it predicts that r is different from zero,but another way of saying this is that there is a significant correlation)and Ho always predicts no difference.If your Ha was directional,and you find that it was predicted in the wrong direction(i.e.you predicted A was greater than B and it turns out that B is significantly greater than A)you should still accept H,even though H predicts no difference,and you found a difference in the opposite direction. If there is a WARNING box on your Output File,it is usually because you used the wrong test,or the wrong variables.Go back and double check. Tests For Numeric Data Z-Scores (Compared to Data) Analyze Descriptive Statistics>Descriptives Click over the variable you would like zscores for Click on the box that says Save Standardized Values as Variables.This is located right below the box that displays all of the variables. If means and standard deviations are needed,click on Options and click on the boxes that will give you the means and standard deviations. .The zscores will not be on the Output File!!! They are saved as variables on the Data File.They should be saved in the variable that is to the far right of the data screen.Normally it is called z,and then the name of the variable (e.g.ZSLEEP) Compare the zscores to the critical value to determine which zscores are significant.Remember,if your hypothesis is directional(i.e.one-tailed),the critical value is or-1.645.If your hypothesis is non-directional (i.e.two- tailed),the critical value is or-1.96
SPSS Guide to Data Analysis Page 1 of 8 For All Tests · Remember that the Significance (or Asymp. Sig. in some cases) needs to be less than 0.05 to be significant. · The Independent Variable is always the variable that you are predicting something about (i.e. what your Ha predicts differences between, as long as your Ha is correct). The Dependent Variable is what you are measuring in order to tell if the groups (or conditions for repeated measures tests) are different. For correlations and for Chi-Square, it does not matter which one is the Independent or Dependent variable. · Ha always predicts a difference (for correlations, it predicts that r is different from zero, but another way of saying this is that there is a significant correlation) and Ho always predicts no difference. If your Ha was directional, and you find that it was predicted in the wrong direction (i.e. you predicted A was greater than B and it turns out that B is significantly greater than A) you should still accept Ho, even though Ho predicts no difference, and you found a difference in the opposite direction. · If there is a WARNING box on your Output File, it is usually because you used the wrong test, or the wrong variables. Go back and double check. Tests For Numeric Data Z-Scores (Compared to Data ) Analyze ‡ Descriptive Statistics ‡ Descriptives · Click over the variable you would like z-scores for · Click on the box that says Save Standardi zed Values as Variables. This is located right below the box that displays all of the variables. · If means and standard deviations are needed, click on Options and click on the boxes that will give you the means and standard deviations. · The z-scores will not be on the Output File!!! · They are saved as variables on the Data File. They should be saved in the variable that is to the far right of the data screen. Normally it is called z, and then the name of the variable (e.g. ZSLEEP) · Compare the z-scores to the critical value to determine which z-scores are significant. Remember, if your hypothesis is directiona l (i.e. one-tailed), the critical value is + or – 1.645. If your hypothesis is non-directional (i.e. twotailed), the critical value is + or – 1.96
SPSS Guide to Data Analysis Page 2 of9 Z-Scores Compared to a Population Mean and Standard Deviation: The methodology is the same except you need to tell SPSS what the population mean and standard deviation is(In the previous test,SPSS calculated it for you from the data it was given.Since SPSS cannot calculate the population mean and standard deviation from the class data,you need to plug these numbers into a formula). Remember the formula for a zscore is: X-4 2= You are going to transform the data you got into a zscore that is compared to the population by telling SPSS to minus the population mean from each piece of data, and then dividing that number by the population standard deviation.To do so,go to the DATA screen,then: Transform→Compute Name the new variable you are creating in the Target Variable box (ZUSPOP is a good one if you can't think of anything). Click the variable you want zscores for into the Numeric Expression box. Now type in the zscore formula so that SPSS will transform the data to a US population zscore.For example,if I am working with a variable called Sleep, and I am told the US population mean is 8.25 and that the US population standard deviation is.50,then my Numeric Expression box should look like this: (SLEEP-8.25)/.50 Compare for significance in the same way as above. For All T-Tests The significance that is given in the Output File is a two-tailed significance. Remember to divide the significance by 2 ifyou only have a one-tailed test! For One Group T-Tests Analyze→Compare Means→One-Sample T Test The Dependent variable goes into the Test Variables box. The hypothetical mean or population mean goes into the Test Value box.Be Careful!!!The test value should be written in the same way the data was entered for the dependent variable.For example,my dependent variable is "Percent Correct on a Test"and my population mean is 78%.If the data for
SPSS Guide to Data Analysis Page 2 of 9 Z-Scores Compared to a Population Mean and Standard Deviation: · The methodology is the same except you need to tell SPSS what the population mean and standard deviation is (In the previous test, SPSS calculated it for you from the data it was given. Since SPSS cannot calculate the population mean and standard deviation from the class data, you need to plug these numbers into a formula). · Remember the formula for a z-score is: s - m = X z · You are going to transform the data you got into a z-score that is compared to the population by telling SPSS to minus the population mean from each piece of data, and then dividing that number by the population standard deviation. To do so, go to the DATA screen, then: Transform ‡ Compute · Name the new variable you are creating in the Target Variable box (ZUSPOP is a good one if you can’t think of anything). · Click the variable you want z-scores for into the Numeric Expression box. Now type in the z-score formula so that SPSS will transform the data to a US population z-score. For example, if I am working with a variable called Sleep, and I am told the US population mean is 8.25 and that the US population standard deviation is .50, then my Numeric Expression box should look like this: (SLEEP – 8.25)/.50 · Compare for significance in the same way as above. For All T-Tests · The significance that is given in the Output File is a two-tailed significance. Remember to divide the significance by 2 if you only have a one-tailed test! For One Group T-Tests Analyze ‡ Compare Means ‡ One-Sample T Test · The Dependent variable goes into the Test Variables box. · The hypothetical mean or population mean goes into the Test Value box. Be Careful!!! The test value should be written in the same way the data was entered for the dependent variable. For example, my dependent variable is “Percent Correct on a Test” and my population mean is 78%. If the data for