SPSS - Beginnings Data, Descriptive Statistics, Select cases, recode Structure SPSS has 3 different fields (windows) 1. Data window (double window). O

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1 SPSS - Beginnings Data, Descriptive Statistics, Select cases, recode Structure SPSS has 3 different fields (windows) 1. Data window (double window). One can see a) data; b) Variable information. 2. Output window. 3. Syntax window. 2 1

2 Structure SPSS output contains results and program syntax. SPSS syntax window is used for programs.typically syntax window is not used, since it is more convenient to use mouse than to write a program. 3 Data window icons Typical SPSS view when program is started is an empty data window. 4 2

3 Opening File here čia File can be opened from the list... 5 Empty file čia FILE -> NEW -> DATA 6 3

4 We see data if Here Botom left-hand corner has Data view'... 7 We see data if here Icon is not pressed 8 4

5 Data SPSS : 1. One respondent one case (row). 2. Variables - columns. 9 Data structure Variable names data 10 5

6 Variable information 1. Name. 2. Type. 3. Variable label. 4. Value labels. 5. Missing values. 11 Variable name 1. Begins with the letter. 2. One word. 3. K1; K1.1; Lmnsvs - correct. 4. Ali Baba, 1grupė incorrect. 5. Variable name = columns title. 12 6

7 Most common types of variables 1. String string of symbols (name, curse, etc.). Has only length (number of symbols). 2. Numeric for numbers and codes. Has length (number of symbols allowed) and decimal part (number of decimals). 3. Other types are rarely used. 13 Variable label 1. Comment about variable (full question, short description etc.) 2. For example, name K1, label place of birth. 3. Variable label is unnecessary for statistical analysis. 14 7

8 Missing value(s) 1. Special numerical codes for unanswered questions. 2. For example, we can apply code 0, if question is not applicable to respondent. 3. If 0 is marked as 'missing value', it is treated as an empty cell in all statistical calculations. 15 Value labels 1. Comments about specific values of the variable. 2. For example, in data we can use codes 1 and 2, with explaining value labels: 1male, 2 female. 3. Value labels are unnecessary for statistical analysis. 16 8

9 Variable Information in SPSS: Click here 17 Variable list Name Other information 18 9

10 Variable list Missing values type- numeric (F) Value labels 19 Variable list Possibility to Check the data 20 10

11 Entering Data 1. Variable view window is for entering variable information. 2. Data view window is for entering data. 21 Variable view window Name variable label Type 22 11

12 Value labels in variable view window value label Add 23 Entering Data Use Keyboard 24 12

13 Entering Data Some remarks: 1. Mouse is used sparsely. 2. Apply tab button for entering data row by row. 3. Apply 'enter button for entering data column by column. 25 Descriptive Statistics 1. Frequency table. 2. Mean 3. Variance. 4. Standard deviation. Remark. There are many other characteristics in descriptive statistics

14 Frequency table in SPSS We will check, what percent of respondents has 3 children. File 'GSS93subset' Variable 'childs' 27 Frequency table in SPSS ANALYZE -> DESCRIPTIVE STATISTICS -> FREQUENCES 28 14

15 Frequency table in SPSS Press for moving variable check 29 Frequency table in SPSS childs Number of Children Valid Missing Total Eight or More Total 9 NA Frequency 414 Percent 27,6 Valid Percent 27,7 Cumulative Percent 27, ,1 26,5 15,1 16,2 26,6 15,1 43,9 70,5 85, ,7 7,7 93, ,9,9,5 1,4 3,9,9,5 1,4 97,2 98,1 98,6 100, ,7 100, ,3 100,0 'percent' with missing values, 'valid percent'- without 30 15

16 Variance 1) Shows dispersion of values. 2) Larger variance for larger dispersion. s 2 (x 1 x ) 2 (x 2 x ) 2... (x n -1 n x)2 Standard deviation: s = square root of variance. 31 Descriptives with SPSS File: GSS93 subset Variable agewed (age when first time married) We will calculate the mean and standard deviation of agewed

17 Descriptives with SPSS ANALYZE -> DESCRIPTIVE STATISTICS -> DESCRIPTIVES 33 Descriptives with SPSS perkeliame move 34 17

18 Descriptives with SPSS Descriptive Statistics agewed Age When First Married Valid N (listwise) N Minimum Maximum Mean 58 Std. Deviation 22,79 5, Mean age is 22,79 years The mean The std 35 Select cases Retains part of the data for further analysis; For example, let us assume that we want to find the average age when first married for males. Note that 'Select cases' does not perform statistical analysis (just selects some cases)

19 Select cases in SPSS here 37 Select cases in SPSS check Then If Leave Alone! 38 19

20 Select cases in SPSS for conditions not equal and or select cases if = atrinkti eilutes, jei 'select cases' syntax Begins from the variable name, then some condition. Conditions can be connected by and or or. For example, if we want to investigate males only and know that males are coded by 1 in variable sex, we write condition sex=

21 Select cases in SPSS Then Continue and OK 41 Select cases in SPSS 'išbraukta' Cases are left intact, but are crossed. They are not used in subsequent analysis

22 Select cases in SPSS For example, after calculation of the mean for 'agewed, we obtain Descriptive Statistics N MinimumMaximum Mean Std. Deviation agewed Age When ,16 4,867 First Married Valid N (listwise) Select cases in SPSS Unselecting' means that we choose All cases 44 22

23 select cases - remarks Condition is applied to each case separately. For example, if we want to select white males we write (sex=1) & (race=1). Younger than 50: age <50. between 20 and 50 years of age: (age>=20) & (age=<50). 45 select cases - remarks But If we want to investigate black and white respondents we write (race=1) (race=2). Since (race=1)&(race=2) means that we expect each respondent to be white and black at the same time (zebra)

24 select cases - remarks INCORRECT race = <age<40 30 < age age =15 / age =20 [race=1] & [age =50] (race=1) (age =50) (age >50) (age <60) CORRECT race =1 race =3 age>30 & age <40 age >30 age=15 age=20 (race=1) &(age=50) (race=1) &(age=50) Drop it, since it means all respondents 47 select cases string variables Values of string variables are taken into apostrophes: gender='m'. Symbol must be copied EXACTLY as it appears in data.. gender='m' incorrect. gender=' M' incorrect (contains space after the first apostrophe)

25 COMPUTE Compute command is applied 1. For creation of a new variable (column) 2. For changing of some variable. 3. For creation of 0 1 variable. In all cases we transform data

26 Compute TRANSFORM -> COMPUTE new name 51 Compute content 52 26

27 Compute: new variable Mean of two variables 53 Compute: 0-1 variable Logical expression New variable zzz=1, if age>30 and zzz=0, if age =<

28 Compute: 0-1 variable New variable zzz=1, if age>30 and zzz=0, if age =< Compute: conditional variables Choose If and write condition New variable ww=1, if age>30 (and empty cells Otherwise)

29 AUTOMATIC RECODE 57 Automatic recode 1. Numeric variable is created from String variable. 2. Values of string variable become 'value labels for the new numeric variable

30 Automatic recode here Automatic recode 59 String variable Name For a new variable press 60 30

31 Automatic recode New numeric Variable appears in data 61 Automatic recode Old values became new Value labels 62 31

32 RECODE 63 Recode 1. Allows numeric variable replace (or create a new variable) by another numeric variable with different codes. 3. For example we will create a new variable'islam with code 1 for muslim countries, code 2 for budhist countries and code 3 for all other countries

33 Recode 1. Variable 'tikyba': has value labels muslim for 6, budhist for Recode here TRANSFORM -> RECODE into Different Variables' 66 33

34 Recode Old variable New name Then Change 67 Recode New name appears Next here 68 34

35 Recode New code Old code And Add 69 Recode 3 All other values. Budhist into

36 Recode New variable appears in data 71 Recode: second example New variable amzius, with codes for different age groups: 1 if age <30; 2 if age 31 50; 3 if older than

37 Recode: second example New name 73 Recode: second example nuo Missing mažiausio values iki... Just to ensure that all missing values remain unchanged

38 Recode: second example From Lowest to 75 Recode: second example From......to 76 38

39 Recode: second example from... to Highest value 77 Recode: second example 78 39