SPSS Guide Howto, Tips, Tricks & Statistical Techniques


 Marcia McDowell
 4 years ago
 Views:
Transcription
1 SPSS Guide Howto, Tips, Tricks & Statistical Techniques Support for the course Research Methodology for IB Also useful for your BSc or MSc thesis March 2014 Dr. Marijke Leliveld Jacob Wiebenga, MSc
2 CONTENT Introduction... 3 Description of the dataset... 4 Step 1: Preparing the Dataset... 6 Goal: Label the variables... 6 Goal: Checking outliers... 6 Goal: Checking assumptions for regression analysis... 7 Step 2: Descriptive statistics... 9 Goal: Distribution and means... 9 Step 3: Correlations and reliability analysis Goal: Correlations between variables Goal: Test reliability Goal: Recoding variables Step 4: Making a new sum variable Goal: Making a sum variable Step 5: Technique Choice Goal: Choosing the analysis technique Step 6: The control variables Goal: TTest Independent Samples Goal: Linear Regression Goal: Oneway Anova Goal: Oneway Anova (2) Step 7: Testing the hypothesis Goal: Kruskal Wallis Test Goal: Linear Regression (2) Goal: Paired samples ttest Goal: Crosstable with Chisquare Goal: MannWhitney Utest Goal: Manova Goal: Twoway Anova Appendix Tables for Univariate and bivariate analyses
3 INTRODUCTION What to do before you start... When you start working in SPSS, it is useful to tick the box which makes sure the computer code of the analyses you make is also saved in the output. This might not immediately be clear to you. However, it is very insightful for your tutor. You can do this by going to: Edit>Options > tab Viewer > tick the box Display commands in the log in the bottom left. The techniques that are discussed in the chapter control variables are also usable for testing your hypotheses and the same goes for the analyses that are described in testing your hypotheses. The only reason these techniques are discussed in these chapters is due to the structure of the dataset used in this guide. Recommended literature Field, A. Discovering statistics using SPSS. Sage. ISBN: Huizingh, E. Applied statistics with SPSS. Sage. ISBN: Keller, G. Statistics for Management and Economics (7th International Student Edition), Thomson. ISBN: Recommended literature for more advanced and specialized techniques: Janssens, W., Wijnen, K., De Pelsmacker, P., & Van Kenhove, P. (2008) Marketing Research with SPSS. Pearson Education Limited. ISNB: > e.g. logistic regression, factor analysis, SEM, cluster analysis, Scaling techniques Tabachnick, B. G., and Fidell, L. S. (2007). Using Multivariate Statistics, 5th ed. Boston: Allyn and Bacon. ISBN10: ; ISBN13: > e.g. multiple regressions, experimental designs (ANOVAs), and time series analyses Lattin, J., Carroll, J.D., & Green, P.E. (2003). Analyzing Multivariate Data. Toronto: Thomson Learning. ISBN > e.g., Roles of third variables in the linear model, Hierarchical linear models, Principal components analysis, Factor analysis, Latent class analysis, Conjoint analysis, Choice models, Multidimensional scaling. 3
4 DESCRIPTION OF THE DATASET The case discussed here revolves around the company Solar Energy Ltd. and the study this company did concerning the relationship between autonomy and the job satisfaction of its employees. As it happens, the board of Solar Energy Ltd. expected that more autonomy leads to higher job satisfaction. They also wanted to know whether variables such as education level, sex, age and religion also affect job satisfaction. The conceptual model of their study can be found below. Autonomy Job Satisfaction Need for structure Education level Gender Age Religion In order to study this model Solar Energy Ltd. had their employees fill out the following questionnaire: Age: What is your age? (in years) Gender: What is your gender? (1 = man, 2 = women) Religion: What is your religion? (1 = reformed, 2 = catholic, 3 = hindoeïsm, 4 = muslim, 5 = jewish, 6 = atheïst, 7 = different) Education Level: What is your highest level of education? (1 = primary school, 2 = secondary school, 3 = postsecondary, 4 = Ph.D.) Autonomy: A1 A2 How often is it necessary to explain yourself beforehand to a superior about the tasks that have to be performed? (1 = never, 2 = sometimes, 3 = often, 4 = very often, 5 = always) How often is it possible to use your own ideas in your tasks? (1 never, 7 = always) A3 How often are you dependent on tasks your colleagues are responsible for when performing your job? (1 = always, 7 = never) 4
5 Job satisfaction: W1 W2 W3 How useful do you feel your job is to the company? (1= not at all, 7 = a lot) How much fun do you have when doing your job? (1= not at all, 7 = a lot) How satisfied are you with your job? (1= not at all, 7 = a lot) Need for structure S1 S2 To what degree do you need a very clear description of your job? (1= not at all, 7 = a lot) To what degree do you like working with clearly defined tasks in your job? (1= not at all, 7 = a lot) S3 To what degree do you experience stress when your tasks suddenly change? (1= not at all, 7 = a lot) In the followup study one year later: Job satisfaction: W1later To what degree do you feel your tasks are contributing to your company? (1= not at all, 7 = a lot) W2later To what degree do you experience fun when performing your tasks? (1= not at all, 7 = a lot) W3later T To what degree are you satisfied with your job? (1= not at all, 7 = a lot) Are you satisfied with your job? (1= yes, 2 = no) 5
6 STEP 1: PREPARING THE DATASET GOAL: LABEL THE VARIABLES The goal of this section is learning how you can label variables so you can understand what your variables mean later on. This can be done by clearly labeling your variables and naming the categories of each variable. TECHNIQUE Click the tab variable view in the bottom left corner of your SPSS file and type a description of your variables in the column labels. After this you click a cell in the column values which allows you to specify each category of your variable. Example: click in the column values on gender and type a 1 in the value box and man in label, then press add. Do the same for woman and press add again. Important! When discussing your analyses and findings, never mention terms like variable A1, W2, etc. (like we did in this guide), but mention their full description. This is because other people (clients for example) do not know what A1, W2 means. GOAL: CHECKING OUTLIERS Every statistical technique is based on several assumptions and in order to draw the right conclusion from the results these assumptions must be met. TECHNIQUE: OUTLIERS When using statistics it is important to always check your data for possible outliers (extreme values on the dependent or independent variable). It s possible that these 6
7 extreme values are the result of a mistake in the data input. However, these values might also be correct. These outliers violate the normality assumption and therefore a nonparametric test is has to be chosen in these cases. Outliers can be detected by plotting boxplots, histograms, probability plots or scatterplots. GOAL: CHECKING ASSUMPTIONS FOR REGRESSION ANALYSIS The assumptions that will be covered here are relevant for regression analysis: 1. The sample should be based on independent observations. 2. There is a linear relationship between the dependent and the independent variable. 3. The residuals are normally distributed. These assumptions also hold for the Oneway ANOVA and multiple regression analysis. The assumptions regarding independent observations and normality also apply to Ttests. Nonparametric tests are not restricted by these assumptions. TECHNIQUE: INDEPENDENT OBSERVATIONS The respondents in the dataset must be determined in dependent of each other, and there cannot be any relation between the observed scores between respondents. This also means the expected correlation between the residuals of a regression analysis are zero (independence assumption). In the case of dependence the estimated standard error will be smaller than the actual standard error which leads to inefficient estimates of the regression coefficients. TECHNIQUE: LINEAR RELATIONSHIP There should be a linear relationship between the dependent and the independent variable. For every set of values of the independent variable the expected mean residuals should be equal to zero. A systematic deviation of this assumption indicates the relationship is not linear. Detection of this problem can be done by making a scatterplot where the residuals (on the yaxis) are plotted against another variable (on the xaxis). If a linear relationship exists, the residuals will be spread randomly around their average (which is zero). However, when the scatterplot shows a nonlinear relationship another type of relationship might be applicable (e.g. logistic relationship). TECHNIQUE: NORMALITY ASSUMPTION There are several methods to test for nonnormality. Examples include graphical ways of examining normality and statistical tests. The easiest way is to plot a histogram of the residuals, which is particularly relevant for large samples. When a more accurate analysis is required one can use PP plots (normal probability plot). This is basically a scatterplot that shows the cumulative probability of a stand normal distribution plotted against the cumulative probabilities of the observed distribution. If the normality assumption is 7
8 satisfied, the dots on the PP plot will be in a straight line. The normality assumption is violated if there is a systematic deviation from the line which will lead to wrong estimates for the confidence intervals and pvalues. An important note here is that when sample size is sufficiently large (about 200) and the amount of independent variables is small (less than 5), the central limit theorem says that the estimates of confidence intervals and pvalues will be accurate again, even though the normality assumption is violated. 8
9 STEP 2: DESCRIPTIVE STATISTICS GOAL: DISTRIBUTION AND MEANS Use SPSS to calculate the number of males and females whom participated in the study and their average age. Furthermore, compute the distribution of education level and provide the medians of the ordinal variables education level and A1, and the averages for the quasi interval variables A2, A3, W1 and W2. TECHNIQUE Analyze > Descriptive Statistics > Frequencies. Put the variables gender, age, education level, A1, A2, W1,W2 in the right box and click Statistics. Next, tick the boxes mean, median, std. deviation, minimum and maximum and click continue. 9
10 Click Charts and tick the box Histograms (with normal curve) and click continue and OK. SPSS OUTPUT Amount of male and female participants: Gender Frequency Percent Valid Percent Cumulative Percent Valid man ,6 47,6 47,6 woman ,4 52,4 100,0 Total ,0 100,0 This table shows that 107 participants were male (47,6%) and 118 were female (52,4%). Furthermore, it shows that everyone answered what their gender was, since there is no row called missing. Descriptive statistics: Statistics Age Education Gender A1 A2 A3 W1 W2 N Valid Missing Mean 41,26 2,47 1,52 3,61 4,22 4,52 3,83 3,81 Median 42,00 2,00 2,00 3,00 4,00 4,00 4,00 4,00 Std. Deviation 13,203,987,501 1,475 1,627 1,593 1,839 1,651 Minimum Maximum
11 HOW TO REPORT DESCRIPTIVE STATISTICS The table above shows the descriptive statistics that were required. Keep in mind that when you report a mean, you should also report the standard deviation (SD). Distribution of education level: The sample consists of 107 (47,6%) men and 118 (52,4%) women, with an average age of 41 (Mage= 41,26, SD =13,20). Table 1 shows the means of variables A2, A3, W1 and W2. Furthermore, education level was approximately normally distributed (see figure 1) ranging from primary school to Ph.D.level. The median of education level was 2,00 (secondary school) and the median of A1 3,00 (often). Figure 1. Distribution of education level Table 1. Descriptive statistics for variables A2,A3,W1 and W2 A2 A3 W1 W2 Mean 4,22 4,52 3,83 3,81 Standard deviation 1,63 1,59 1,84 1,65 11
12 STEP 3: CORRELATIONS AND RELIABILITY ANALYSIS Two constructs in the model (autonomy and job satisfaction) are measured with multiple questions. Before proceeding with the analysis, it might be useful to check whether or not the questions correlate with each other and if they do, make a new sum variable that depicts the whole construct up in one score. GOAL: CORRELATIONS BETWEEN VARIABLES How are the questions measuring job satisfaction related? Can they be summed up? If yes, how would you do this? TECHNIQUE: CORRELATION First, you have to check whether or not the questions measuring job satisfaction (W1,W2 and W3) correlate with each other. Use Analyze>Correlate>Bivariate and put the three variables in the right box and press OK. SPSSOUTPUT Correlations W1 W2 W3 W1 Pearson Correlation 1,000 ,103 ,089 Sig. (2tailed),122,186 N 225, W2 Pearson Correlation ,103 1,000,897** Sig. (2tailed),122,000 N , W3 Pearson Correlation ,089,897** 1,000 Sig. (2tailed),186,000 N ,000 **. Correlation is significant at the 0.01 level (2tailed). NB. A correlation table actually provides the same information twice since the information is the same across the diagonal. Therefore, you only need half of the table to find your answer. The table shows that W2 does not significantly correlates with W1 (p is larger then 0,05; p=0,122). It also seems W3 does not correlate with W1 (p=0,186). However, W3 and W2 do correlate significantly (p is smaller than 0,05, namely p=0,000). 12
13 HOW TO REPORT THIS? A correlation analysis showed that W1 and W2 did not significantly correlate (r= 0,103, p=0,122). The variables W2 and W3 did correlate significantly (r= 0,897, p <.001). When people have more fun while working, they will be more satisfied with their work, and vice versa. Considering you can only sum up the variables that correlate significantly, it appears only W2 and W3 can be recoded into a sum variable. GOAL: TEST RELIABILITY The final test you have to perform to check whether or not a sum variable may be computed is Cronbach s alpha. This value should be as high as possible (ranging from 0 to 1), but a value of at least 0,6 is required. TECHNIQUE: CRONBACH S ALPHA Analyze > Scale > Reliability analysis. Move the three variables of job satisfaction to the right box and press Statistics. In the top right corner, tick the box under descriptives for : item, scale en scale if item deleted. Press continue and OK. 13
14 SPSSOUTPUT Reliability Statistics Cronbach's Alpha N of Items,442 3 SPSS shows a value for Cronbach s Alpha of 0,442 when combining all three questions for job satisfaction (W1, W2 & W3). However, the minimum to allow for summing up the variables is a Cronbach s Alpha of 0,6. When examining the rest of the output of SPSS you find the last column Cronbach s Alpha if item deleted which shows the value of Cronbach s Alpha if one of the questions is omitted. For example, if you don t consider W1 the Cronbach s Alpha shoots up to 0,945 which is significantly above the minimum of 0,6 which means W2 and W3 can be summed to one variable. However, when you delete W2 the Cronbach s Alpha goes down to 0,193. The same goes for deleting W3 (Cronbach s Alpha = 0,229). Both of these options only reduce the Cronbach s Alpha of 0,44 that we found before. We can conclude that W2 & W3 can be summed, but W1 cannot (the same conclusion we reached with correlations). ItemTotal Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item Total Correlation W1 7,99 10,174 ,099,945 W2 8,01 5,491,540 ,193 a W3 7,64 5,481,563 ,229 a Cronbach's Alpha if Item Deleted a. The value is negative due to a negative average covariance among items. This violates reliability model assumptions. You may want to check item codings. Important! A negative value for Cronbach s Alpha means something went wrong. One of the possibilities is that you included two variables with mirrored answer options (e.g. 1 means never with one variable, while 1 means always with the other variables). In order to calculate a valid Cronbach s Alpha in this case you should recode the variables in such a way that they all have the same scale (also see: recoding variables). 14
15 HOW TO REPORT THIS Reliability analysis on the three questions measuring job satisfaction showed that W2 and W3 together had a α = When W1 was also included, the α was reduced to 0,44. Therefore, a sum variable was computed using only W2 and W3. GOAL: RECODING VARIABLES How are the questions about autonomy related? Can they be summed to one variable? In this case several steps are of importance. The variables A2 and A3 are interval while A1 is ordinal. Therefore, only A2 and A3 can be summed since you can only sum interval variables. However, the categories for A2 and A3 are opposites (A2 starts with never, while A3 starts with always). This means one of these questions has to be recoded in such a way that they can be summed. TECHNIQUE: RECODE Recode variable A3 in such a way that 1=never and 7=always Transform > Recode into different variables. Important! NEVER choose Recode into same variable since if something goes wrong you will have lost your original data! Move the variable you want to recode to the right box and fill in the new name for your variable (RecA3) under output variable and press change. Next, press Old and New Values. 15
16 Since the old A3 had: 1=always and 7=never and the new A3 should be: 1=never and 7=always, you should fill in 1 at Old Value and 7 at New Value and press add. After that you fill in 2 at Old Value and 6 at New Value and press add. Continue until you recoded all seven levels, then press continue and OK. You have now recoded the variable in such a way that the new variable has: 1= never and 7=always. TECHNIQUE: CRONBACH S ALPHA In order to calculate the Cronbach s Alpha of A2 and A3 you can follow the procedure described in question 3a but instead of W1,W2 and W3 you use the variables A2 and RecA3. SPSS provides the following output: Reliability Statistics Cronbach's Alpha N of Items,871 2 The Cronbach s Alpha is α = 0,871 which is higher than the minimum of 0,6. This means we can conclude the variables A2 and RecA3 can be summed into one variable. Regarding the other variables in the dataset: you can use the same procedure described here. 16
17 STEP 4: MAKING A NEW SUM VARIABLE GOAL: MAKING A SUM VARIABLE After you found out which variables can be summed, you can use SPSS to calculate a new variable for you. First, this will be done for the questions about job satisfaction and after that for the questions about autonomy. TECHNIQUE: SUMMING THE VARIABLES In order to sum W2 and W3: Transform > Compute Variable. Fill in a new name for the variable you are about to make in the field Target Variable. For example: SumW2W3 (to clearly identify this as the sum of W2 and W3; which is also nice if someone else uses your dataset in the future). Next, you can indicate in the right field that you want to sum the two variables: (W2+W3) and click OK. After this you can see your new variable in the dataset (both in the Variable view and the Data view ). TECHNIQUE: AVERAGE Make a new variable for the combination of A2 and RecA3 by taking the average of the two variables. Instead of making a new variable by summing the two (which was done above), the average of the variables is taken instead. We can do this by first summing the variables and then dividing by the number of variables (2 here). An advantage of this method when comparing it to summing the variables is that the 7point scale is left intact this way which makes the result easier to interpret. Transform > Compute Variable. 17
18 Fill in the new name for the variable you are about to make in the field Target Variable. For example: AvA2A3 and fill the formula for averaging the scores in next (A2+RecA3)/2 and press OK. You can see your new variable in the dataset. 18
19 STEP 5: TECHNIQUE CHOICE GOAL: CHOOSING THE ANALYSIS TECHNIQUE In order to answer the following questions the same steps will be taken each time: 1. What statistical technique is necessary? (You can use the tables in the appendix for this) 2. How can you perform the technique in SPSS and how does the most important output look? 3. How do you report the results? TECHNIQUE: BIG THREE A method for choosing the right technique is the Big Three which are three questions about the data you are using. These questions will also be discussed later on. 1. How many variables are involved in the analysis? One: univariate analysis (descriptive statistics) Two: bivariate analysis (inferential statistics) > Two: multivariate analysis (inferential statistics) 2. What is the data type of the involved variable(s)? Independent variable (X): nominal, ordinal or interval (ratio) Dependent variable (Y): nominal, ordinal or interval (ratio) 3. Asymmetric vs. symmetric? (only for 2 or more variables) Asymmetric: when variables have a different data types or you want to explain the dependent variable based on the independent variable (causal relationship) Symmetric: when there is no need for predicting causal relationships and the variables are of the same data type. HOW TO REPORT THIS? It is important to know that directly copying SPSS output to your report is NOT allowed! You should use the values found in the SPSS output and report them in your own words by using the ABCDFormula: A. What was the goal? (= WHAT) B. How did you do this? (= HOW) C. Was the test significant? Report this in the right way! (= RESULT) D. What can be concluded from the results? ( = CONCLUSION) In this formula, part A and D must be described in such a way that anyone can understand it. However, part B and C are for people who know about statistics. The best way of reporting the analyses is described in more detail in the results. Even though the letters A until D are also used in the results section, you should never use these in your research paper. 19
20 STEP 6: THE CONTROL VARIABLES GOAL: TTEST INDEPENDENT SAMPLES The next step is checking the influence of the control variables that were included in the model (in this example: age, gender, religion and education level) on the most important dependent variable (job satisfaction). TECHNIQUE: INDEPENDENT SAMPLES TTEST Needed technique: the variable gender is nominal and job satisfaction is interval. Furthermore, gender consists of two categories; male and female (k=2). Therefore, we need to use an independent samples ttest to find out whether or not gender influences job satisfaction. SPSS output: Analyze > Compare Means > Independent samples ttest Please make sure you add the new sum variable of job satisfaction in the box Test variable and gender in the box Grouping variable. Press Define groups and fill in a 1 for Group 1 and a 2 for Group 2. Press Continue and then OK. 20
21 SPSSOUTPUT. Group Statistics Gender N Mean Std. Deviation Std. Error Mean SumW2W3 man 107 7,8037 3,31503,32048 woman 118 8,1525 3,07631,28320 Independent Samples Test Levene's Test for Equality of Variances ttest for Equality of Means F Sig. t df Sig. (2 tailed) Mean Std. Error Difference Difference 95% Confidence Interval of the Difference Lower Upper Sum W2W3 Equal variances assumed Equal variances not assumed,791,375 , , ,34880, ,18852, , ,551, ,34880, ,19174,49413 HOW TO REPORT THIS? Remember, you cannot use these inconvenient tables in your results section. Please explain the results in words. A. In order to analyze whether or not the average job satisfaction of men is different from the average job satisfaction of women, (=WHAT) B. we performed an independent samples ttest with gender and job satisfaction. (=HOW) C. The independent samples ttest was not significant, t(223) = 0,82, p = 0,414 (=RESULT) D. The average job satisfaction of men (M= 7,8, SD=3,32) does not differ from the average job satisfaction of women (M = 8,1, SD = 3,08). (=CONCLUSION) 21
22 GOAL: LINEAR REGRESSION TECHNIQUE The variables age and job satisfaction are both measured on interval data type. Therefore, we have to use regression analysis in order to examine the influence of age on job satisfaction. Analyze > Regression > Linear Regression Put the sum variable job satisfaction in the dependent field, and age in the independent field, then press OK. SPSSOUTPUT Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1,022 a,001 ,004 3,19600 a. Predictors: (Constant), Age 22
23 Model Sum of Squares df ANOVA b Mean Square F Sig. 1 Regression 1, ,142,112,738 a Residual 2277, ,214 Total 2278, a. Predictors: (Constant), Age b. Dependent Variable: SumW2W3 Model Unstandardized Coefficients Coefficients a B Std. Error Beta Standardized Coefficients 1 (Constant) 8,210,701 11,719,000 t Sig. Age ,005,016 ,022 ,334,738 a. Dependent Variable: SumW2W3 HOW TO REPORT THIS A. In order to analyze whether or not the age of employees influences their job satisfaction, (=WHAT) B. we performed a regression analysis with age regressed on job satisfaction. (=HOW) C. The regression analysis was not significant, R 2 =0,001, F(1,223) =.11, p =.738. (=RESULT) D. The age of employees does not influence job satisfaction, B = 0,005, t = 11.72, p = (=CONCLUSION). Important! Whenever you use more independent variables (e.g. four) in a socalled multivariate linear regression, the Ftest (in the ANOVA table) will display the overall significance of the influence of all these variables together on the dependent variable. If you want to know what variables have a specific effect, you can find this information in the table Coefficients. This is because it is possible that only two of the four regressors have a significant effect which leads to an overall significant Ftest. Because this case only considers one independent variable, the significant Ftest also means our sole predictor significantly predicts the dependent variable. 23
24 GOAL: ONEWAY ANOVA TECHNIQUE The variable education level is ordinal while job satisfaction is interval. Unfortunately, there is no statistical test available for this combination of variables. However, in this case it is the easiest to assume the variable education level is nominal (remember, you can always make the transition to a lower data type, but never from low to high). This means you get an independent variable of more than two categories which leads to a oneway ANOVA. SPSS output: Analyze > Compare Means > Oneway ANOVA Input the sum variable for job satisfaction in the dependent list and education level in factor. Tick the box descriptives under Options which provides the means of all treatment levels of the independent variable. SPSS OUTPUT SumW2W3 Sum of Squares df Mean Square F Sig. Between Groups 17, ,672,554,646 Within Groups 2261, ,235 Total 2278,
25 Descriptives SumW2W3 95% Confidence Interval for Mean Lower Upper N Mean Std. Deviation Std. Error Bound Bound Minimum Maximum Primary school 43 8,35 3,309,505 7,33 9, Secondary school 72 8,01 3,270,385 7,25 8, College 72 7,62 2,971,350 6,93 8, University 38 8,21 3,354,544 7,11 9, Total 225 7,99 3,190,213 7,57 8, HOW TO REPORT THIS? A. In order to analyze whether or not the job satisfaction of employees differs per education level, (=WHAT) B. we performed a Oneway ANOVA of education level on job satisfaction. ( = HOW) C. This Oneway ANOVA was not significant, F(3, 221) = 0,55, p = 0,65 ( = RESULT) D. The education level of employees does not influence their job satisfaction. ( = CONCLUSION) GOAL: ONEWAY ANOVA (2) TECHNIQUE Needed technique: the variable religion is nominal (with more than two categories, so k>2) and job satisfaction is interval. Therefore, we have to use a Oneway ANOVA to find out whether or not religion influences job satisfaction. SPSS output: Analyze > Compare Means > Oneway ANOVA. 25
26 Input the sum variable for job satisfaction in the dependent list and relgion in factor. Tick the box descriptives under Options which displays the means of your treatment levels, then press OK. SPSSOUTPUT ANOVA Descriptives SomW2W3 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum reformed 14 8,21 3,017,806 6,47 9, catholic 28 7,93 3,344,632 6,63 9, hindoeism 48 8,44 3,814,551 7,33 9, muslim 48 8,15 2,552,368 7,40 8, jewish 46 8,11 3,261,481 7,14 9, atheist 28 7,61 3,095,585 6,41 8, other 13 6,00 2,309,641 4,60 7, Total 225 7,99 3,190,213 7,57 8, SumW2W3 Sum of Squares df Mean Square F Sig. Between Groups 67, ,303 1,114,355 Within Groups 2211, ,143 Total 2278, HOW TO REPORT THIS A. In order to analyze whether or not the job satisfaction of employees differs per religion, (=WHAT) B. we performed a Oneway ANOVA of religion on job satisfaction. ( = HOW) C. This Oneway ANOVA was not significant, F(6, 218) = 1,11, p = 0,355 ( = RESULT) D. The religion of employees does not influence their job satisfaction. ( = CONCLUSION) 26
27 STEP 7: TESTING THE HYPOTHESIS After checking the control variables, it is now time to examine the effect of the independent variable on the dependent variable. This will be done by using the aforementioned hypothesis. Unfortunately it is not possible to test the influence of a nominal variable on an ordinal variable due to a mistake in an earlier version of this SPSS guide involving the conceptual model and the dataset. Therefore, an example that was NOT included in the conceptual model will be used to show how this test is performed, even though there is no theoretical grounding for this research question. The variables that are referred to here are religion and autonomy. Does religion have an influence on the degree of autonomy people experience during their job? One of the questions for autonomy (A1) is ordinal and religion is nominal with more than two categories (K=7). Therefore, a KruskalWallis test has to be performed here. GOAL: KRUSKAL WALLIS TEST What is the effect of religion on the variable How often is it necessary to explain yourself beforehand to a superior about the tasks that have to be performed? (A1) TECHNIQUE The variable religion is nominal (k>2) and autonomy A1 is ordinal. Therefore the Kruskal Wallis test is used to determine the influence of religion on autonomy. Analyze > Nonparametric Tests > K Independent Samples Put the autonomy variable A1 in Test Variable List and religion in Grouping Variable, then click Define Range. Because religion has seven categories, you fill in a 1 at minimum and a 7 at maximum. Then press Continue and OK. 27
28 SPSSOUTPUT Test Statistics a,b A1 ChiSquare 2,520 df 6 Asymp. Sig.,866 a. Kruskal Wallis Test b. Grouping Variable: religie HOW TO REPORT THIS A. In order to analyze whether people with a different religion differ in the degree of experience autonomy, (= WHAT) B. we performed a KruskalWallis test with religion on autonomy. (= HOW) C. This KruskalWallis test was not significant, ChiSquare(6) = 2,52, p = 0,866 (=RESULT) D. People with a different religion do not differ in experienced autonomy. (=CONCLUSION) NB When the test is significant you can find frequency tables for the observed autonomy per religion through the crosstabs option. You can report these to show the significant difference. 28
29 GOAL: LINEAR REGRESSION (2) What is the effect of the sum variable of autonomy on job satisfaction? TECHNIQUE The sum variable of autonomy and job satisfaction are both interval variables, therefore we can use a regression analysis to examine the influence of autonomy on job satisfaction. Analyze > Regression > Linear SPSSOUTPUT Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1,919 a,844,843 1,26341 a. Predictors: (Constant), MeanA2A3 29
30 ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 1923, , ,746,000 a Residual 355, ,596 Total 2278, a. Predictors: (Constant), MeanA2A3 b. Dependent Variable: SumW2W3 Coefficients a Model Unstandardized Coefficients B Std. Error Beta Standardized Coefficients 1 (Constant),545,230 2,364,019 MeanA2A3 1,934,056,919 34,709,000 a. Dependent Variable: SumW2W3 t Sig. HOW TO REPORT THIS? A. In order to analyze whether or not a higher autonomy leads to a higher job satisfaction, (=WHAT) B. we performed a regression analysis of autonomy on job satisfaction, (=HOW) C. The results of this regression, R 2 = 0,84, F(1,224) = , p =.000 reveal a significant effect. There is a positive relationship between autonomy and job satisfaction B = 1.93, t(224) = 34,70, p <.001. (=RESULT) D. A higher autonomy of employees does lead to a significantly higher job satisfaction. (CONCLUSION) 30
31 GOAL: PAIRED SAMPLES TTEST Imagine that Solar Energy Plc decided to raise the autonomy of their employees based on past research within the company. A year after these changes have taken place, job satisfaction is measured again. Test whether or not job satisfaction has risen the past year. Mind you, the measurement of the second year has not yet been summed, make sure this is allowed. TECHNIQUE Before we can compare the original sum variable of job satisfaction with the new one, a sum variable for the second year has to be made (in the same way as the First one). Check if W2later and W3later correlate significantly (which is in fact true, see table below) and if the Cronbach s Alpha is sufficiently high to sum the variables (which is in fact true; Cronbach s Alpha is 0,974, see table below). Next, you can sum the variables in the same way as assignment 3a: (W2later+W3later). SPSSOUTPUT Correlations W2later W3later W2later Pearson Correlation 1,000,950 ** Sig. (2tailed),000 N 225, W3later Pearson Correlation,950 ** 1,000 Sig. (2tailed),000 N ,000 **. Correlation is significant at the 0.01 level (2 tailed). **. Correlation is significant at the 0.01 level (2 tailed). Reliability Statistics Cronbach's Alpha N of Items,
32 TECHNIQUE We are dealing with two related samples that have to be compared here, because every person answered the same questions twice. The first time he/she answered the questions influence the second time he/she answered the questions which means the samples are related and we have to use the paired sample ttest. Analyze > Compare Means > Paired samples ttest Drag the old job satisfaction variable (SumW2W3) to variable 1 and the new job satisfaction variable (SumW2laterW3later) to variable 2, then press OK. NB. You could input additional pairs of variables here if you wanted to. However, since we only have one pair here, this is not relevant for this analysis. SPSSOUTPUT Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 SumW2W3 7, ,190,213 SumW2laterW3later 9, ,042,203 Paired Samples Test Pair 1 SumW2W3  SumW2laterW3later Paired Differences Mean Std. Std. Error Deviation Mean 95% Confidence Interval of the Difference Lower Upper t df Sig. (2 tailed) 1,91111,43416, , , , ,000 32
33 HOW TO REPORT THIS? A. In order to analyze whether or not job satisfaction has risen in the last year, (=WHAT) B. we performed a paired samples ttest on the original job satisfaction variable and the new one. (=HOW) C. The paired samples t test was significant, t(224) = 66,03, p <.001 (=RESULT) D. Job satisfaction did significantly rise this year, from M=7,99 to M=9,90 a year later (=CONCLUSION).* *it is important to note here that it is not possible to proof whether or not the increase is due to the increase in autonomy based on this test! Another test would have to be performed which includes both measurement moments of autonomy too. GOAL: CROSSTABLE WITH CHISQUARE Imagine another question was posed regarding job satisfaction, namely are you satisfied with your job? 1=yes, 2=no and we wanted to find out whether or not gender influences this question? Perform the appropriate analysis and report the results. TECHNIQUE The relevant variables are both nominal, therefore a Chisquare with cross table has to be used. Analyze > Descriptive Statistics > Crosstabs Drag gender to Row and the T (the other question about job satisfaction) to Column. Then press Statistics in the top right and click the box Chisquare. Press Continue and then Cells, tick the boxes Row and Column in the Percentages heading. Then press Continue and OK. 33
34 SPSSOUTPUT gender * T Crosstabulation T 1 2 Total gender man Count % within geslacht 94,4% 5,6% 100,0% % within T 87,8% 5,5% 47,6% women Count % within geslacht 11,9% 88,1% 100,0% % within T 12,2% 94,5% 52,4% Total Count % within geslacht 51,1% 48,9% 100,0% % within T 100,0% 100,0% 100,0% ChiSquare Tests Value Pearson ChiSquare 152,954 a 1,000 Continuity Correction b 149,669 1,000 Likelihood Ratio 179,621 1,000 df Asymp. Sig. (2 sided) Exact Sig. (2 sided) Fisher's Exact Test,000,000 LinearbyLinear Association N of Valid Cases ,274 1,000 a. 0 cells (,0%) have expected count less than 5. The minimum expected count is 52,31. b. Computed only for a 2x2 table Exact Sig. (1 sided) NB. If the value in an SPSS table is shown with an E in it, double click the table in the SPSS output and widen the column of Value a bit. HOW TO REPORT THIS? A. In order to analyze whether or not man and women differ concerning their job satisfaction, (=WHAT) B. we performed a cross table with Chisquare with gender and job satisfaction (1=yes, 2=no) (=HOW) C. The Chisquare test was significant, ChiSquare(1) = 152,95, p <.001. (=RESULT) D. Men are more often satisfied with their job (94,4%) than women (11,9%). (=CONCLUSION) 34
35 GOAL: MANNWHITNEY UTEST Imagine one of the researchers working on this report is bored and wants to know if gender influences education level. Even though it is not in the conceptual model and he has no theory to suspect there is a difference (and how the difference would look), he still decides to find out if there is a relationship. Therefore, we have to find out if there is a difference between men and women concerning their education level. TECHNIQUE We are dealing with a nominal independent variable (gender, k = 2) and an ordinal dependent variable education level, which means a MannWhitney Utest is appropriate. SPSS output: Analyze > Nonparametric tests > 2 independent samples Input education level in the Testing variable box and gender in the Grouping variable one. Press define groups and indicate Group 1 equals 1 and Group 2 equals 2. Press Continue and then OK. 35
36 SPSSOUTPUT Ranks Education level gender N Mean Rank Sum of Ranks , , , ,00 Total 225 Test Statistics a Education level MannWhitney U 5485,000 Wilcoxon W 11263,000 Z 1,768 Asymp. Sig. (2tailed),077 a. Grouping Variable: gender The test shows that the Mean Ranks appear to differ from each other (105 vs 120), but using a α =.05 the difference is not significant. Since you can observe a trend in this case, you could describe the effect as marginally significant. NB All pvalues above.10 cannot be described as such. Since it is not possible to determine the medians of both groups (namely the median of men and women) in this menu, you have to determine these yourself in SPSS. In order to do this, first select only the male subjects in your dataset. Go to Data > Select Cases. Press the button if condition is satisfied under select and press if next. Indicate that you only want to select people that scored a 1 on the variable gender: gender=1 and press Continue, then OK. NB Always make sure you filter out unselected cases (option in the Output section) which will make sure the unselected cases are not deleted but simply kept out of the analyses. 36
37 You can now get the median of education level through Analyze > Frequencies. Since only men are selected at the moment, the median only applies to the male group, which will indicate the median is 2 (secondary school). In order to determine the median of women, you go back to data > select cases > if... and indicate that you want to use people that scored 2 now: gender = 2 and press Continue, then OK. If you ask SPSS to determine the median through Frequencies, the median of the female group will be displayed, which is 3 (College). HOW TO REPORT THIS? A. In order to analyze if men and women differ regarding their education level, (= WHAT) B. we performed a MannWhitney Utest. (=HOW) C. This test was marginally significant, MWU = 5485, p =.077. (=RESULT) D. Men have a lower education level (MR = 105.3; median = Secondary school) than women (MR = 120.0; median = College). (= CONCLUSION) GOAL: MANOVA If you want to examine multiple dependent variables at once as a researcher, you can use the Multivariate Analysis of Variance (MANOVA). This is especially useful when one or more of your variables are not interval and can be used to answer questions like: do job satisfaction and autonomy of employees differ per age category? 37
38 TECHNIQUE: MANOVA We are dealing with two dependent variables and 1 independent variable. All variables are measured on the interval data type. Analyze >General Linear Model > Multivariate The dependent variables SumW2W3 and mean A2A3 are put in Dependent variables and age in Fixed factor. Press Model and tick Full factorial with Sum of squares Type III, then press continue. Press Options, in order to get extra means with the analyses tick Descriptive statistics, Parameter estimates and Homogeneity test. Press Continue, then OK. 38
39 SPSSOUTPUT Multivariate Tests c Effect Value F Hypothesis df Error df Sig. Intercept Pillai's Trace, ,792 a 2, ,000,000 Wilks' Lambda, ,792 a 2, ,000,000 Hotelling's Trace 5, ,792 a 2, ,000,000 Roy's Largest Root 5, ,792 a 2, ,000,000 leeftijd Pillai's Trace,318,752 90, ,000,948 Wilks' Lambda,705,754 a 90, ,000,946 Hotelling's Trace,384,755 90, ,000,945 Roy's Largest Root,251 1,000 b 45, ,000,481 a. Exact statistic b. The statistic is an upper bound on F that yields a lower bound on the significance level. c. Design: Intercept + age 39
40 Source Dependent Variable Tests of BetweenSubjects Effects Type III Sum of Squares df Mean Square F Sig. Corrected Model SumW2W3 269,302 a 45 5,984,533,993 MeanA2A3 64,011 b 45 1,422,565,987 Intercept SumW2W , , ,815,000 MeanA2A3 2379, , ,908,000 leeftijd SumW2W3 269, ,984,533,993 MeanA2A3 64, ,422,565,987 Error SumW2W3 2009, ,227 MeanA2A3 450, ,516 Total SumW2W , MeanA2A3 3847, Corrected Total SumW2W3 2278, MeanA2A3 514, a. R Squared =,118 (Adjusted R Squared = ,104) HOW TO REPORT THIS? When interpreting a MANOVA, first look at multivariate effects. These describe if the independent variables have an effect on all dependent variables. Four different statistics are used to interpret this, (Pillai's Trace and Wilk's Lambda, Roy's largest root and Hotelling's Trace) which all use a slightly different calculation. Normally, it is best to use Wilk s Lambda because it shows the amount of unexplained variance (the opposite of the R 2 ). Only if a multivariate effect was found specific univariate effects are relevant. You can do this in the table with univariate results that automatically comes after the multivariate table, and look at the relevant independent variables there. 40
41 A. In order to analyze whether or not the degree of job satisfaction and autonomy of employees differs per age (=WHAT) B. we performed a Multivariate Analysis of Variance. (=HOW) C. This test was not significant at all, F (90,356)= 0,754, p =0,946. (=RESULT) D. Apparently there is no difference in job satisfaction and autonomy for employees of different ages. Therefore, it is unnecessary to interpret the univariate analysis. (= CONCLUSION) GOAL: TWOWAY ANOVA Besides the OneWay ANOVA there is also the TwoWay ANOVA which is used when you want to perform an analysis of variance with two independent variables. In this case, autonomy and need for structure were chosen as the independent variables. Both variables will be split based on the median ( median split ). The dependent variable is, again, job satisfaction. The question is whether or not there is a difference in job satisfaction for employees with a high or low autonomy and employees with a high or low need for structure. TECHNIQUE: SPLITTING This test is often used when you have two independent variables on nominal data type. An example of this could be an experiment in which you manipulated power (high vs low) and valence (win vs lose) and want to find out how these factors influence negotiating behavior. In this case it is unnecessary to split, since they are already nominal! However, in this case it is needed to split the two independent variables based on their medians. The median can be found through Descriptives and splitting them goes as follows: Transform > Compute Variable Indicate how you want to name the new variable in Target Variable and press if. This allows you to specify how you want to split the variable. Press Continue and provide a new value for the variable. After splitting both variables in two groups, the TwoWay ANOVA can be performed. 41
42 42
43 TECHNIQUE: TWOWAY ANOVA Analyze > General Linear Model > Univariate Input the (in)dependent variables and press Model. Specify the model as Full factor with sum of squares Type III, then press Continue. 43
44 Tick the box descriptive statistics at Options so the marginal and cellmeans are displayed. Then press Contrast and change the factors to simple contrasts, don t forget to press Change and press continue. There are several contrasts you can use here. More information about this can be found in advanced statistics books. Next, indicate that you want a plot of the main effect as well as the interaction effect in the Plot section. Then press Continue and OK. 44
45 SPSSOUTPUT Descriptive Statistics Dependent Variable:SumW2W3 SplitA SplitS Mean Std. Deviation N Low Autonomy Low Need for Structure 5,26 1, High Need for Structure 5,33 1, Total 5,30 1, High Autonomy Low Need for Structure 8,37, High Need for Structure 11,05 1, Total 10,61 1, Total Low Need for Structure 6,04 2, High Need for Structure 8,98 3, Total 7,99 3, Dependent Variable:SumW2W3 Tests of BetweenSubjects Effects Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 1698,749 a 3 566, ,682,000 Intercept 9081, , ,048,000 SplitA 784, , ,946,000 SplitS 76, ,463 29,124,000 SplitA * SplitS 68, ,869 26,232,000 Error 580, ,625 Total 16631, Corrected Total 2278, a. R Squared =,745 (Adjusted R Squared =,742) 45
46 Test Results Dependent Variable:SumW2W3 Source Sum of Squares df Mean Square F Sig. Contrast 784, , ,946,000 Error 580, ,625 HOW TO REPORT THIS? Before you report anything (in a TwoWay design) about the main effect of both variables, it is important to first look at the interaction effects. When the two lines in the graph cross each other, an interaction effect is usually present. This means the effect of autonomy on job satisfaction is influenced by their need for structure. Employees with a high autonomy and a high need for structure have a higher job satisfaction than employees with high autonomy and a low need for structure. Use the sample means to describe this effect! A. In order to analyze the influence of autonomy and need for structure on job satisfaction of employees, (=WHAT) B. we performed a 2 (autonomy: low vs. high) x 2 (need for structure: low vs. high) ANOVA on job satisfaction. (=HOW) C. Both main effects proved to be significant. A high autonomy leads to a higher job satisfaction (M = 10.61) than low autonomy (M = 5.30), F(1,221)= 298,95, p < 0,001. Also, a high need for structure leads to more job satisfaction (M = 8.98) than a low need for structure (M = 6.04), F (1,221)= 29,124, p < 0,001. The interaction effect also appeared significant, F (1,221)= 26,232, p < 0,001. However, for people with a low score for autonomy, it does not matter if they have a high need for structure, because in both cases they have a low score for job satisfaction (Mlow need for structure = 5.26 en M high need for structure = 5.33). Furthermore, for people with a high score for autonomy, need for structure does make a 46
47 difference regarding their score for job satisfaction. Especially people with a high need for structure are very satisfied with their job (M = 11.05) when compared to people with a low need for structure(m = 8.37). (=RESULT) D. The most important conclusion is that the need for structure is not influential for people with a low score for autonomy. However, for people with a high score on autonomy, this effect is present; especially people with a high need for structure are very satisfied with their job. (=CONCLUSION) Figure 1. Autonomy and need for structure on job satisfaction Low need for structure High need for structure 2 0 Low autonomy High autonomy 47
48 APPENDIX TABLES FOR UNIVARIATE AND BIVARIATE ANALYSES Univariate analyses Nominal Ordinal Interval Central tendency Mode Median Mean Distribution Range (not very precise) Standard deviation Bivariate analyses: symmetric vs. asymmetric Asymmetric when: 1) variables have different measurement scales OR 2) predict DV based on IV Symmetric bivariate analyses (X X) X X Nominal Ordinal Interval Nominal Cross table with Chisquare test of independence Ordinal Spearman Rank correlation coefficient Interval Pearson correlation coefficient 48
The Dummy s Guide to Data Analysis Using SPSS
The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests
More informationSPSS Tests for Versions 9 to 13
SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list
More informationSCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
More informationAn introduction to IBM SPSS Statistics
An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive
More informationSPSS Explore procedure
SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stemandleaf plots and extensive descriptive statistics. To run the Explore procedure,
More informationAnalysing Questionnaires using Minitab (for SPSS queries contact ) Graham.Currell@uwe.ac.uk
Analysing Questionnaires using Minitab (for SPSS queries contact ) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:
More informationII. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
More informationChapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means Oneway ANOVA To test the null hypothesis that several population means are equal,
More informationIntroduction to Quantitative Methods
Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................
More informationData analysis process
Data analysis process Data collection and preparation Collect data Prepare codebook Set up structure of data Enter data Screen data for errors Exploration of data Descriptive Statistics Graphs Analysis
More informationJanuary 26, 2009 The Faculty Center for Teaching and Learning
THE BASICS OF DATA MANAGEMENT AND ANALYSIS A USER GUIDE January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS Table of Contents Table of Contents... i
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationUsing SPSS, Chapter 2: Descriptive Statistics
1 Using SPSS, Chapter 2: Descriptive Statistics Chapters 2.1 & 2.2 Descriptive Statistics 2 Mean, Standard Deviation, Variance, Range, Minimum, Maximum 2 Mean, Median, Mode, Standard Deviation, Variance,
More informationChapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS
Chapter Seven Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Section : An introduction to multiple regression WHAT IS MULTIPLE REGRESSION? Multiple
More informationProjects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
More informationDirections for using SPSS
Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...
More informationUNDERSTANDING THE TWOWAY ANOVA
UNDERSTANDING THE e have seen how the oneway ANOVA can be used to compare two or more sample means in studies involving a single independent variable. This can be extended to two independent variables
More informationResearch Methods & Experimental Design
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
More informationTHE KRUSKAL WALLLIS TEST
THE KRUSKAL WALLLIS TEST TEODORA H. MEHOTCHEVA Wednesday, 23 rd April 08 THE KRUSKALWALLIS TEST: The nonparametric alternative to ANOVA: testing for difference between several independent groups 2 NON
More informationBill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1
Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Calculate counts, means, and standard deviations Produce
More informationData Analysis for Marketing Research  Using SPSS
North South University, School of Business MKT 63 Marketing Research Instructor: Mahmood Hussain, PhD Data Analysis for Marketing Research  Using SPSS Introduction In this part of the class, we will learn
More informationDoing Multiple Regression with SPSS. In this case, we are interested in the Analyze options so we choose that menu. If gives us a number of choices:
Doing Multiple Regression with SPSS Multiple Regression for Data Already in Data Editor Next we want to specify a multiple regression analysis for these data. The menu bar for SPSS offers several options:
More informationMultivariate Analysis of Variance (MANOVA)
Chapter 415 Multivariate Analysis of Variance (MANOVA) Introduction Multivariate analysis of variance (MANOVA) is an extension of common analysis of variance (ANOVA). In ANOVA, differences among various
More informationSPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011
SPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011 Statistical techniques to be covered Explore relationships among variables Correlation Regression/Multiple regression Logistic regression Factor analysis
More informationHYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
More informationMixed 2 x 3 ANOVA. Notes
Mixed 2 x 3 ANOVA This section explains how to perform an ANOVA when one of the variables takes the form of repeated measures and the other variable is betweensubjects that is, independent groups of participants
More informationData Analysis in SPSS. February 21, 2004. If you wish to cite the contents of this document, the APA reference for them would be
Data Analysis in SPSS Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 354870348 Heather Claypool Department of Psychology Miami University
More informationMultivariate Analysis of Variance. The general purpose of multivariate analysis of variance (MANOVA) is to determine
2  Manova 4.3.05 25 Multivariate Analysis of Variance What Multivariate Analysis of Variance is The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 15 scale to 0100 scores When you look at your report, you will notice that the scores are reported on a 0100 scale, even though respondents
More informationSPSS Notes (SPSS version 15.0)
SPSS Notes (SPSS version 15.0) Annie Herbert Salford Royal Hospitals NHS Trust July 2008 Contents Page Getting Started 1 1 Opening SPSS 1 2 Layout of SPSS 2 2.1 Windows 2 2.2 Saving Files 3 3 Creating
More informationInstructions for SPSS 21
1 Instructions for SPSS 21 1 Introduction... 2 1.1 Opening the SPSS program... 2 1.2 General... 2 2 Data inputting and processing... 2 2.1 Manual input and data processing... 2 2.2 Saving data... 3 2.3
More informationSPSS for Exploratory Data Analysis Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav)
Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav) Organize and Display One Quantitative Variable (Descriptive Statistics, Boxplot & Histogram) 1. Move the mouse pointer
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationExamining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish Statistics Statistics are quantitative methods of describing, analysing, and drawing inferences (conclusions)
More informationSPSS Guide: Regression Analysis
SPSS Guide: Regression Analysis I put this together to give you a stepbystep guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar
More informationUsing Excel for inferential statistics
FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied
More informationIntroduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
More informationData Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
More informationOverview of NonParametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS
Overview of NonParametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS About Omega Statistics Private practice consultancy based in Southern California, Medical and Clinical
More informationSimple linear regression
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
More informationData exploration with Microsoft Excel: analysing more than one variable
Data exploration with Microsoft Excel: analysing more than one variable Contents 1 Introduction... 1 2 Comparing different groups or different variables... 2 3 Exploring the association between categorical
More informationSimple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
More informationEPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST
EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeatedmeasures data if participants are assessed on two occasions or conditions
More informationIntroduction to Analysis of Variance (ANOVA) Limitations of the ttest
Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One Way ANOVA Limitations of the ttest Although the ttest is commonly used, it has limitations Can only
More informationIndependent t Test (Comparing Two Means)
Independent t Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent ttest when to use the independent ttest the use of SPSS to complete an independent
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationOneWay ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate
1 OneWay ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,
More informationABSORBENCY OF PAPER TOWELS
ABSORBENCY OF PAPER TOWELS 15. Brief Version of the Case Study 15.1 Problem Formulation 15.2 Selection of Factors 15.3 Obtaining Random Samples of Paper Towels 15.4 How will the Absorbency be measured?
More informationSPSS TUTORIAL & EXERCISE BOOK
UNIVERSITY OF MISKOLC Faculty of Economics Institute of Business Information and Methods Department of Business Statistics and Economic Forecasting PETRA PETROVICS SPSS TUTORIAL & EXERCISE BOOK FOR BUSINESS
More informationThe ChiSquare Test. STAT E50 Introduction to Statistics
STAT 50 Introduction to Statistics The ChiSquare Test The Chisquare test is a nonparametric test that is used to compare experimental results with theoretical models. That is, we will be comparing observed
More informationWhen to use Excel. When NOT to use Excel 9/24/2014
Analyzing Quantitative Assessment Data with Excel October 2, 2014 Jeremy Penn, Ph.D. Director When to use Excel You want to quickly summarize or analyze your assessment data You want to create basic visual
More informationAn introduction to using Microsoft Excel for quantitative data analysis
Contents An introduction to using Microsoft Excel for quantitative data analysis 1 Introduction... 1 2 Why use Excel?... 2 3 Quantitative data analysis tools in Excel... 3 4 Entering your data... 6 5 Preparing
More information4. Descriptive Statistics: Measures of Variability and Central Tendency
4. Descriptive Statistics: Measures of Variability and Central Tendency Objectives Calculate descriptive for continuous and categorical data Edit output tables Although measures of central tendency and
More informationDESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.
DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,
More informationLinear Models in STATA and ANOVA
Session 4 Linear Models in STATA and ANOVA Page Strengths of Linear Relationships 42 A Note on NonLinear Relationships 44 Multiple Linear Regression 45 Removal of Variables 48 Independent Samples
More informationINTERPRETING THE ONEWAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONEWAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the oneway ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
More informationChapter 7 Section 7.1: Inference for the Mean of a Population
Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used
More informationMultiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear.
Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. In the main dialog box, input the dependent variable and several predictors.
More informationAnalysis of Data. Organizing Data Files in SPSS. Descriptive Statistics
Analysis of Data Claudia J. Stanny PSY 67 Research Design Organizing Data Files in SPSS All data for one subject entered on the same line Identification data Betweensubjects manipulations: variable to
More informationOnce saved, if the file was zipped you will need to unzip it. For the files that I will be posting you need to change the preferences.
1 Commands in JMP and Statcrunch Below are a set of commands in JMP and Statcrunch which facilitate a basic statistical analysis. The first part concerns commands in JMP, the second part is for analysis
More informationSimple Linear Regression, Scatterplots, and Bivariate Correlation
1 Simple Linear Regression, Scatterplots, and Bivariate Correlation This section covers procedures for testing the association between two continuous variables using the SPSS Regression and Correlate analyses.
More informationChapter 7: Simple linear regression Learning Objectives
Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) 
More informationNCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, twosample ttests, the ztest, the
More informationUNDERSTANDING THE INDEPENDENTSAMPLES t TEST
UNDERSTANDING The independentsamples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly
More informationBivariate Statistics Session 2: Measuring Associations ChiSquare Test
Bivariate Statistics Session 2: Measuring Associations ChiSquare Test Features Of The ChiSquare Statistic The chisquare test is nonparametric. That is, it makes no assumptions about the distribution
More informationNCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
More informationChapter 23. Inferences for Regression
Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily
More informationASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS
DATABASE MARKETING Fall 2015, max 24 credits Dead line 15.10. ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS PART A Gains chart with excel Prepare a gains chart from the data in \\work\courses\e\27\e20100\ass4b.xls.
More informationKSTAT MINIMANUAL. Decision Sciences 434 Kellogg Graduate School of Management
KSTAT MINIMANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To
More informationChapter 13. ChiSquare. Crosstabs and Nonparametric Tests. Specifically, we demonstrate procedures for running two separate
1 Chapter 13 ChiSquare This section covers the steps for running and interpreting chisquare analyses using the SPSS Crosstabs and Nonparametric Tests. Specifically, we demonstrate procedures for running
More information1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
More informationAn SPSS companion book. Basic Practice of Statistics
An SPSS companion book to Basic Practice of Statistics SPSS is owned by IBM. 6 th Edition. Basic Practice of Statistics 6 th Edition by David S. Moore, William I. Notz, Michael A. Flinger. Published by
More informationStatistics for Sports Medicine
Statistics for Sports Medicine Suzanne Hecht, MD University of Minnesota (suzanne.hecht@gmail.com) Fellow s Research Conference July 2012: Philadelphia GOALS Try not to bore you to death!! Try to teach
More informationDescribing, Exploring, and Comparing Data
24 Chapter 2. Describing, Exploring, and Comparing Data Chapter 2. Describing, Exploring, and Comparing Data There are many tools used in Statistics to visualize, summarize, and describe data. This chapter
More informationHow to Get More Value from Your Survey Data
Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2
More informationSimple Tricks for Using SPSS for Windows
Simple Tricks for Using SPSS for Windows Chapter 14. Followup Tests for the TwoWay Factorial ANOVA The Interaction is Not Significant If you have performed a twoway ANOVA using the General Linear Model,
More informationHYPOTHESIS TESTING WITH SPSS:
HYPOTHESIS TESTING WITH SPSS: A NONSTATISTICIAN S GUIDE & TUTORIAL by Dr. Jim Mirabella SPSS 14.0 screenshots reprinted with permission from SPSS Inc. Published June 2006 Copyright Dr. Jim Mirabella CHAPTER
More informationIBM SPSS Statistics 20 Part 1: Descriptive Statistics
CALIFORNIA STATE UNIVERSITY, LOS ANGELES INFORMATION TECHNOLOGY SERVICES IBM SPSS Statistics 20 Part 1: Descriptive Statistics Summer 2013, Version 2.0 Table of Contents Introduction...2 Downloading the
More informationDESCRIPTIVE STATISTICS AND EXPLORATORY DATA ANALYSIS
DESCRIPTIVE STATISTICS AND EXPLORATORY DATA ANALYSIS SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi  110 012 seema@iasri.res.in 1. Descriptive Statistics Statistics
More informationbusiness statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
More informationThe Statistics Tutor s Quick Guide to
statstutor community project encouraging academics to share statistics support resources All stcp resources are released under a Creative Commons licence The Statistics Tutor s Quick Guide to Stcpmarshallowen7
More informationTesting for differences I exercises with SPSS
Testing for differences I exercises with SPSS Introduction The exercises presented here are all about the ttest and its nonparametric equivalents in their various forms. In SPSS, all these tests can
More informationData Analysis, Research Study Design and the IRB
Minding the pvalues p and Quartiles: Data Analysis, Research Study Design and the IRB Don AllensworthDavies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB
More informationChapter 2: Descriptive Statistics
Chapter 2: Descriptive Statistics **This chapter corresponds to chapters 2 ( Means to an End ) and 3 ( Vive la Difference ) of your book. What it is: Descriptive statistics are values that describe the
More informationSPSS Resources. 1. See website (readings) for SPSS tutorial & Stats handout
Analyzing Data SPSS Resources 1. See website (readings) for SPSS tutorial & Stats handout Don t have your own copy of SPSS? 1. Use the libraries to analyze your data 2. Download a trial version of SPSS
More informationTwo Related Samples t Test
Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person
More informationRow vs. Column Percents. tab PRAYER DEGREE, row col
Bivariate Analysis  Crosstabulation One of most basic research tools shows how x varies with respect to y Interpretation of table depends upon direction of percentaging example Row vs. Column Percents.
More informationCOMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES.
277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies
More informationSPSS Manual for Introductory Applied Statistics: A Variable Approach
SPSS Manual for Introductory Applied Statistics: A Variable Approach John Gabrosek Department of Statistics Grand Valley State University Allendale, MI USA August 2013 2 Copyright 2013 John Gabrosek. All
More information13: Additional ANOVA Topics. Post hoc Comparisons
13: Additional ANOVA Topics Post hoc Comparisons ANOVA Assumptions Assessing Group Variances When Distributional Assumptions are Severely Violated KruskalWallis Test Post hoc Comparisons In the prior
More informationMain Effects and Interactions
Main Effects & Interactions page 1 Main Effects and Interactions So far, we ve talked about studies in which there is just one independent variable, such as violence of television program. You might randomly
More informationIntroduction to Statistics with SPSS (15.0) Version 2.3 (public)
Babraham Bioinformatics Introduction to Statistics with SPSS (15.0) Version 2.3 (public) Introduction to Statistics with SPSS 2 Table of contents Introduction... 3 Chapter 1: Opening SPSS for the first
More informationBowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition
Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology StepbyStep  Excel Microsoft Excel is a spreadsheet software application
More informationIntroduction Course in SPSS  Evening 1
ETH Zürich Seminar für Statistik Introduction Course in SPSS  Evening 1 Seminar für Statistik, ETH Zürich All data used during the course can be downloaded from the following ftp server: ftp://stat.ethz.ch/u/sfs/spsskurs/
More informationStatistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
More informationWe are often interested in the relationship between two variables. Do people with more years of fulltime education earn higher salaries?
Statistics: Correlation Richard Buxton. 2008. 1 Introduction We are often interested in the relationship between two variables. Do people with more years of fulltime education earn higher salaries? Do
More informationChapter 2 Probability Topics SPSS T tests
Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the OneSample T test has been explained. In this handout, we also give the SPSS methods to perform
More informationModule 3: Correlation and Covariance
Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis
More informationUnit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a
More informationQUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NONPARAMETRIC TESTS
QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NONPARAMETRIC TESTS This booklet contains lecture notes for the nonparametric work in the QM course. This booklet may be online at http://users.ox.ac.uk/~grafen/qmnotes/index.html.
More information