Statistic Lab 1. 0.a. Type data directly into SPSS (make sure you tell SPSS the level of measurement: nominal, ordinal, interval, ratio)

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1 Statistics Lab 1 0. Data Acquisition 0.a. Type data directly into SPSS (make sure you tell SPSS the level of measurement: nominal, ordinal, interval, ratio) 0.b. Type data into Excel Copy and Paste into SPSS (variable labes will be lost!) Import file from excel to SPSS: o The first time you do this, you need to tell SPSS you ll do this in the future: File, open Database, New query, excel files, browse, open file (whatever your file is), ok Retrieve fields (drag to field) Ok, finish, paste (to get the syntax) o After you told SPSS for the first time: File, read text data, file name, paste (syntax), open Missing Data: Click on the column labeled missing, then click on to activate the Missing Values. You can chose them in three ways: o Discrete Missing Values: This option allows you to assign different meanings to each missing value (e.g., 8 for I don t know, -99 for not applicable, etc.) o Range (Low High) : If it is necessary to exclude data which falls between two points (e.g. exclude data between 5 and 10) o Range (Discrete Value): Range of Values PLUS ONE discrete Value 1. Descriptive Statistics: a) Frequency Tables Analyze, descriptive statistics, frequencies: o Statistics: Includes Quartiles, Mean, Median, SD, etc. o Charts: Usually it is nice to look at the Histogram with the normal curve (gain an idea on whether the data is normally distributed) o Format: If you want to rearrange the order. Usually the default option is fine. o Bootstrap: Download R from the internet, and use it as add in. Otherwise you cannot use this function Output: o Central Tendency (mean, mode, median) o Variability (range, SD, variance, quartile splits) o Shape (kurtosis, skewness): You can check if the is approximately normal if you look at the values of skewness and kurtosis: Positive values of skewness: too many low scores in the Negative values of skewness: too many high scores in the

2 Positive values of kurtosis: pointy and heavy-taled Negative values of kurtosis: flat and light-taled The further the value is from zero, the more likely that the data are NOT normally distributed You can convert these scores (skewness & kurtosis) to z- scores by dividing by their standard error. If the resulting score (ignoring the minus sign) is greater than then it is significant (p <.05) b) Descriptives Descriptives, descriptives o Drag Variables to the variable field o Options: Dispersion (SD, Minimum, Maximum, SE of the Mean etc. Distribution (skewness, kurtosis) Display Order (here you can tell SPSS in what order to show Variables in the output) o Save standardized values as variables (z-transformation) c) Explore: Boxplot Descriptives, explore o Select a dependent variable (in our case height ) o Select factor (IV, in our case sex ): SPSS will produce exploratory analysis for each group (i.e., male & female, like split file) o Statistics: Usually the default option is fine (means, SD, you can look at outliers and so forth) o Plots: Boxplots: The default factor levels together is usually fine (i.e., sex in our case). You may want to add the histogram. Descriptives: Stem-and-leaf is fine. You may sometimes want to add the histogram. Normality plots with tests: This will produce the Kolmogorov- Smirnov (K-S) test, as well as the Shapiro-Wilk (S-W) test. These are to see whether the as a whole deviates from a comparable normal. K-S test: used to see if a of scores significantly differs from a normal If K-S significant: scores DIFFERENT from normal S-W test: Same as K-S test, but has more power to detect differences from normality (therefore you may find this test sign., even if K-S not) Warning: In large samples these tests can be significant even when the scores are only slightly different from a normal. Therefore always interpret in conjunction with histograms, P-P or Q-Q plots, and the values of skew and kurtosis. Spread versus Level with Levene s test:

3 o Options: o Output: Tests whether the variance in the different groups that are being compared are similar! If Levene is significant, variances are DIFFERENT in the tested groups. Warning: If sample size is large, small differences in group variances can produce a Levene s test that is significant (because power is improved)! Therefore double check with Hartley s F Max, also known as the variance ratio. This is the ratio of the variances between the group with the biggest variance and the group with the smallest variance. In this option you can look at Power, and transform data. Transforming data with this option however, is not recommended. You should first look at the data, then transform it ONLY if really needed. Exclude cases listwise: If a case has a missing value for any variable, then they are excluded from the whole analysis. Exclude cases pairwise: If a participant has a score missing for a particular variable or analysis, then their data are excluded only from calculations involving the variable for which they have no score. K-S and S-W: Test statistic, df (should be equal to the sample size), and the significance value. In our case height is NOT normally distributed among males (p <.5), but it is normally distributed among females (p >.5). Lilliefors significance correction: Makes the K-S less conservative Report the K-S: The height among males, D(182) =.08, p <.5, was significantly non-normal, whereas the height among females was normal D(96) =.20, p >.5. Attention: I write this for practice only. In publications we usually 1) provide tables, 2) only write the p-values, SD and mean, and 3) do NOT report insignificant results. Quantile-quantile (Q-Q)-Plot: Plots the quantiles of a variable (in our case height ) against the cumulative probability of a particular (usually normal ) for all groups (in our case sex ). If the values fall on the diagonal of the plot, then the variable shares the same as the one specified. Deviation from the diagonal = deviation from the of interest. Probability-probability (P-P)-Plot: Plots the cumulative probability of a variable (in our case height ) against the cumulative probability of a particular (usually normal ) for all groups (in our case sex ). If the values fall on the diagonal of the plot, then the variable shares the same as the one

4 specified. Deviation from the diagonal = deviation from the of interest. If data wrapped around the diagonal like a snake: mostly skewed -> not normally distributed. Stem-and-Leaf: Find outliers. Frequency: How many Stem: In our case height (e.g., 16 means everyone who is between 160cm and 169 cm. The distance between the stems is 10 in our case (as shown beneath the graph: stem width. Leaf: In our case the height. E.g. for the Stem 16 we get for the leafs, which means person 1. is 165 cm, person cm, etc. If you count the heights of these people, you get the frequency (57789 are 5 digits, therefore the frequency would be 5). Boxplot: Shows the dispersion, and most importantly the deviation and the outliers. In our case there are 3 outliers for the variable height among males and among 2 females. If the wiskers are the same length, then the is symmetrical (top and bottom 25% are the same). If the top or bottom wisker is much longer, then the is asymmetrical. c) Descriptives: Crosstabs Descriptive statistics, Crosstabs Exact o Asymptotic only: This is the calculation of the Mann-Whitney test (differences between two independent groups: H 0 = Distribution among both groups is equal). o Exact: If the sample size is small this would be the option to chose. o Monte Carlo: If the sample size is particularly large, then this would be the option to choose. It basically involves creating a similar to that found in the sample and then taking several samples (10000 by default) from this and from those samples the mean significance value and the CI around it can be created. Statistics o Chi-Square: Testing the independency of two categorical variables. o Kendalls tau: Like Pearson correlation for non-parametric data. Usually better for small N. 2. The Compute Function Transform, Compute Variable: o Target Variable: Type in the Variable Name (e.g. BMI) o Label: Type in the label to be shown (can be the same as the target variable, then type use expression as label o Type: Numeric or string o Numeric expression: You could chose from the function group, or just simply type in whatever you want.. (e.g., the calculation of the BMI) 3. Recoding

5 Transform, Recode into Different Variables: o Drag the variable you want to change into the field numeric variable -> output variable (e.g. height) o Give a new name and label, and click on change (e.g. categories_height) o Click on old and new values Define the old Value, or the range, type in the new value, click on add, paste, ok. You will have a new column in the dataset which is called categories_height in my case 4. Syntax Whenever you compute, calculate, transform something, SPSS will give you the option to PASTE what you did. Click on paste, and you will get the syntax. You can easily change the syntax in the viewer. 5. Graphs: a) Boxplot with chart builder Graphs, chart builder, boxplot The popup window will ask you whether you told SPSS the measures of all the variables (nominal, ordinal, scale) -> press ok Click on Boxplot, double click simple boxplot Put DV (in our case height ) on the y-axis, and the IV (our case sex ) on the x-axis. b) Boxplot with Legacy dialogs Graphs, Legacy dialogs, boxplot Simple boxplot, and we want to look at the boxplot by group (sex). We could just as well look at it by variable (which is not very interesting most of the time) Variable = DV = height in our case Category Axis = IV = sex in our case c) Scatter plot with chart builder Graphs, chart builder, scatter plot The popup window will ask you whether you told SPSS the measures of all the variables (nominal, ordinal, scale) -> press ok Click on scatter plot, double click group scatter Put height on the y-axis, and weight on the x-axis, set color for sex. d) Scatter plot with Legacy dialogs Graphs, Legacy dialogs, scatter/dot Simple scatter, define Put height on the y-axis, and weight on the x-axis, set markers by sex. 6. Stratify the Analysis a) Split the file into as many data files as you wish Data, select cases, click: use filter variable (type in sex e.g.), copy selected cases to a new dataset, type in the name of the new dataset, paste (syntax) b) Select preferred observations Data, select cases, click: if condition is satisfied, click if Window opens, click sex=1 (e.g., for male), paste (syntax) c) Perform the analysis by splitting the file

6 Data, split file, click: organize output by group (type in sex e.g.), sort the file by grouping variable, paste (syntax) Don t forget to undo the split file option! Otherwise the SPSS will continue calculations for the groups separately.