Introduction to SPSS S0
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1 Basic medical statistics for clinical and experimental research Introduction to SPSS S0 Katarzyna Jóźwiak November 10, /55
2 Introduction SPSS = Statistical Package for the Social Sciences Initial release in 1968 Very user friendly software for editing and alayzing all sorts of data After opening SPSS, two windows appear: Data Editor with two spreadsheets: Data View Variable View Output 2/55
3 Data editor SPSS Data View: displays our data values; columns represent variables, rows represent subjects/patients 3/55
4 Data editor SPSS Variable View: displays information regarding the meaning of our data, list of all variables, characteristics of all variables 4/55
5 Data editor SPSS Variable View: displays information regarding the meaning of our data, list of all variables, characteristics of all variables Label: "FUinDays" = number of days between breast cancer diagnosis and follow-up visit in clinic Values: "Gender" = 0 for female, 1 for male; "SES" = 3 for low, 2 for medium, 1 for high Missing: 99=unkown Measure: "Age" = scale (it is a numeric continuous variable), "Tstage" = ordinal (possible values are T stage I, T stage II, T stage III; it is a categorical variable but there is an order between categories: T stage I is better than T stage II and T stage II is better than T stage III), "HairColor" = nominal (it is a categorical variable and there is no order between categories) 5/55
6 Data editor: File New: to create new data set, syntax, output Open: to open existing data set (in data structure format like.sav,.xls,.xlsx,.txt,.dat,.csv), syntax, output Save as: to save data (in data structure format like.sav,.csv,.xls,.xlsx,.dta) 6/55
7 Data editor: Data Sort Cases: to sort by a specific variable Sort Variables: to sort by, e.g., variables names, types, widths Merge Files: to combine data sets by adding variables or subjects Split File: to split a data set into smaller data sets Select Cases: to indicate which subjects should be used in analyses 7/55
8 Data editor: Transform Compute: to compute a new variable Recode into Same Variables: to change values of an existing variable Recode into Different Variables: to change values of an existing variable and save the variable as an additional variable Create Dummy Variables: to create indicator variables that have only values 0/1 8/55
9 Data editor: Analyze All the standard statistical analyses can be found here 9/55
10 Data editor: Graphs All the standard graphical tools can be found here 10/55
11 11/55
12 12/55
13 13/55
14 Ms L. Meulen has a unique patient number 7. She is 26 years old and has 3 children (she was pregnant 3 times). She is a diabetic with glucose level 78, blood pressure 50, BMI 31 and height 1.69 cm. 14/55
15 What is the weight of the patients? 15/55
16 What is the weight of the patients? BMI = Weight Height 2 15/55
17 What is the weight of the patients? BMI = Weight Weight = BMI Height 2 Height2 15/55
18 What is the weight of the patients? Weight = BMI Height 2 16/55
19 What is the weight of the patients? Weight = BMI Height 2 17/55
20 What is the weight of the patients? 18/55
21 How old are the patients in our data set? 19/55
22 How old are the patients in our data set? 20/55
23 How old are the patients in our data set? 21/55
24 How old are the patients in our data set? 22/55
25 How old are the patients in our data set? 23/55
26 How old are the patients in our data set? 24/55
27 How old are the patients in our data set? 25/55
28 How old are the diabetic patients and non-diabetic patients in our data set? 26/55
29 How old are the diabetic patients and non-diabetic patients in our data set? 27/55
30 How old are the diabetic patients and non-diabetic patients in our data set? 28/55
31 How old are the diabetic patients and non-diabetic patients in our data set? 29/55
32 How old are the diabetic patients and non-diabetic patients in our data set? 30/55
33 How old are the diabetic patients and non-diabetic patients in our data set? we can first split the file based on the "Diabetes" variable 31/55
34 How old are the diabetic patients and non-diabetic patients in our data set? we can first split the file based on the "Diabetes" variable 32/55
35 How old are the diabetic patients and non-diabetic patients in our data set? we can first split the file based on the "Diabetes" variable 33/55
36 How old are the diabetic patients and non-diabetic patients in our data set? we can first split the file based on the "Diabetes" variable 34/55
37 How old are the diabetic patients? we can first select patients of interest 35/55
38 How old are the diabetic patients? we can first select patients of interest 36/55
39 How old are the diabetic patients? we can first select patients of interest 37/55
40 How old are the diabetic patients? we can first select patients of interest 38/55
41 How old are the diabetic patients? we can first select patients of interest 39/55
42 How old are the diabetic patients? we can first select patients of interest 40/55
43 How to indicate that diabetic patients are younger or older than 40 years of age? we can create a categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age 40 41/55
44 How to indicate that diabetic patients are younger or older than 40 years of age? we can create a categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age 40 42/55
45 How to indicate that diabetic patients are younger or older than 40 years of age? we can create a categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age 40 43/55
46 How to indicate that diabetic patients are younger or older than 40 years of age? we can create a categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age 40 44/55
47 How to indicate that diabetic patients are younger or older than 40 years of age? we can create a categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age 40 45/55
48 How many diabetics patients younger and older than 40 we have in our data set? 46/55
49 How many diabetics patients younger and older than 40 we have in our data set? 47/55
50 How many diabetics patients younger and older than 40 we have in our data set? 48/55
51 How many diabetics patients younger and older than 40 we have in our data set? 49/55
52 Syntax editor Behind all the "drop down menu steps" there is a list of commands that perform the statistical analyses and data manipulation 50/55
53 Syntax editor Behind all the "drop down menu steps" there are lists of commands that perform the statistical analyses and data manipulation saving commands helps re-run everything when necessary 51/55
54 Syntax editor All commands can be saved in a syntax editor file while performing statistical analyses and data manipulation, push "Paste" button instead of "OK" button 52/55
55 Syntax editor All commands can be saved in a syntax editor file while performing statistical analyses and data manipulation, push "Paste" button instead of "OK" button 53/55
56 Syntax editor All commands can be saved in a syntax editor file while performing statistical analyses and data manipulation, push "Paste" button instead of "OK" button 54/55
57 Syntax editor Any command from a syntax can be run separately by selecting the appropriate part and pushing "green arrow" button 55/55
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Model Based Statistics in Biology. Part V. The Generalized Linear Model. Single Explanatory Variable on an Ordinal Scale ReCap. Part I (Chapters 1,2,3,4), Part II (Ch 5, 6, 7) ReCap Part III (Ch 9, 10,