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How to enter data in SPSS
How to enter data in SPSS
Statistical Software Packages Most Commonly Cited in the NEJM and JAMA
Statistical Software Packages Most Commonly Cited in the NEJM and JAMA
Before you perform analysis in SPSS, let’s set up the following option
Before you perform analysis in SPSS, let’s set up the following option
SPSS Windows has 3 windows:
SPSS Windows has 3 windows:
SPSS Data View
SPSS Data View
SPSS Variable View
SPSS Variable View
1.2 Data Entry into SPSS
1.2 Data Entry into SPSS
Figure 1. Data from Hell
Figure 1. Data from Hell
Data from Heaven
Data from Heaven
How to move from Hell to Heaven (1):
How to move from Hell to Heaven (1):
General guidelines for data entry
General guidelines for data entry
5. Each variable should be in its own column
5. Each variable should be in its own column
7. Each patient should be entered on a single line or row
7. Each patient should be entered on a single line or row
9. For yes/no questions, enter “0” for no and “1” for yes
9. For yes/no questions, enter “0” for no and “1” for yes
Entering Date in Excel
Entering Date in Excel
Entering Time in Excel
Entering Time in Excel
In Excel, go to: Format, Cells, select Time under Category, Choose
In Excel, go to: Format, Cells, select Time under Category, Choose
Entering Date, Time in SPSS
Entering Date, Time in SPSS
Importing data from Excel spreadsheet into SPSS
Importing data from Excel spreadsheet into SPSS
Importing data from SPSS to Excel
Importing data from SPSS to Excel
Data merging in SPSS (1)
Data merging in SPSS (1)
Data merging in SPSS (2)
Data merging in SPSS (2)
Data merging in SPSS (3)
Data merging in SPSS (3)
Note in Data merging in SPSS (3)
Note in Data merging in SPSS (3)
1.3 Data Cleaning in SPSS
1.3 Data Cleaning in SPSS
Data cleaning in SPSS (1): Recoding existing variables (1)
Data cleaning in SPSS (1): Recoding existing variables (1)
Data cleaning in SPSS (2): Recoding existing variables (2)
Data cleaning in SPSS (2): Recoding existing variables (2)
Data cleaning in SPSS (1): Recoding existing variables (3)
Data cleaning in SPSS (1): Recoding existing variables (3)
Data cleaning in SPSS (1): Recoding existing variables (4)
Data cleaning in SPSS (1): Recoding existing variables (4)
Data Cleaning in SPSS (2) Creating a new variable for Diastolic blood
Data Cleaning in SPSS (2) Creating a new variable for Diastolic blood
Data Cleaning in SPSS (3)
Data Cleaning in SPSS (3)
Data Cleaning in SPSS (4): Data labeling and formatting (1) Specifying
Data Cleaning in SPSS (4): Data labeling and formatting (1) Specifying
Data Cleaning in SPSS (4): Data labeling and formatting (2)
Data Cleaning in SPSS (4): Data labeling and formatting (2)
Data Cleaning in SPSS (4): Data labeling and formatting (3)
Data Cleaning in SPSS (4): Data labeling and formatting (3)
Data Cleaning in SPSS (4): Data labeling and formatting (4)
Data Cleaning in SPSS (4): Data labeling and formatting (4)
Data Cleaning in SPSS (4): Data labeling and formatting (5)
Data Cleaning in SPSS (4): Data labeling and formatting (5)
Retrieve data property from existing files in SPSS (1)
Retrieve data property from existing files in SPSS (1)
Retrieve data property from existing files in SPSS (2)
Retrieve data property from existing files in SPSS (2)
Retrieve data property from existing files in SPSS (3)
Retrieve data property from existing files in SPSS (3)
Using syntax in SPSS:
Using syntax in SPSS:
Using syntax in SPSS (1): Creating a new syntax file
Using syntax in SPSS (1): Creating a new syntax file
Using syntax in SPSS (2): Editing a syntax file
Using syntax in SPSS (2): Editing a syntax file
Using syntax in SPSS (3): Saving a syntax file
Using syntax in SPSS (3): Saving a syntax file
Using syntax in SPSS (4): Opening an existing syntax
Using syntax in SPSS (4): Opening an existing syntax
Using a syntax in SPSS (5): Example Syntax
Using a syntax in SPSS (5): Example Syntax
Using syntax in SPSS (6):Recoding syntax from command dialog box
Using syntax in SPSS (6):Recoding syntax from command dialog box
Saved syntax from the previous PASTE command
Saved syntax from the previous PASTE command
Using syntax in SPSS (7): Executing the syntax
Using syntax in SPSS (7): Executing the syntax
Data confidentiality
Data confidentiality
Communication with a biostatistician:
Communication with a biostatistician:

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Spss data entry как делать

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1 How to enter data in SPSS

How to enter data in SPSS

1.1 Introduction of SPSS

1.2 Data Entry

1.3 Data Cleaning using SPSS

1

2 Statistical Software Packages Most Commonly Cited in the NEJM and JAMA

Statistical Software Packages Most Commonly Cited in the NEJM and JAMA

between 1998 and 2002

Number of articles software was sited

302

SAS

87

SPSS

STATA

80

49

Epi Info

43

SUDAAN

S-PLUS

33

StatXact

18

9

BMDP

9

StatView

Statistica

8

0

100

200

300

400

2

3 Before you perform analysis in SPSS, let’s set up the following option

Before you perform analysis in SPSS, let’s set up the following option

Go to Edit, Options,..

3

4 SPSS Windows has 3 windows:

SPSS Windows has 3 windows:

Data Editor

Viewer or Draft Viewer which displays the output files

Syntax Editor, which displays syntax files

The Data Editor has two parts:

Data View window, which displays data from the active file in spreadsheet format

Variable View window, which displays metadata or information about the data in the active file, such as variable names and labels, value labels, formats, and missing value indicators.

4

5 SPSS Data View

SPSS Data View

5

6 SPSS Variable View

SPSS Variable View

6

7 1.2 Data Entry into SPSS

1.2 Data Entry into SPSS

There are 2 ways to enter data into SPSS:

1. Directly enter in to SPSS by typing in Data View

2. Enter into other database software such as Excel then import into SPSS

Let’s start with the second option, using data in Excel.

7

8 Figure 1. Data from Hell

Figure 1. Data from Hell

8

9 Data from Heaven

Data from Heaven

9

10 How to move from Hell to Heaven (1):

How to move from Hell to Heaven (1):

1. Add a patient’ ID number

2. Delete the first row with the title of the project

3. Delete the 2 rows under the variable name.

4. Delete the 2 row between the groups.

5. Delete the row of average at the bottom.

6. Add a variable called group and code the first 10 with Drug A as 1 and the next 10 as 2.

7. Change the variable names to less than 8 or 8 characters with no spaces, (you can use numeric, but not starting with numeric, avoid symbols).

8. Insert 2 columns before BP as SYSBP and DIASBP. Delete the BP text column.

9. Change missing values, NA, unknown, ?, to blanks.

10. Change age of 6 months to 0.5 (years). Fix errors.

11. Code males=1 and females=2.

12. Code complications as 0 for no and 1 for yes

13. Go back to the source and complete the missing information

14. If a column was entered as a string (words), you may have to select the column and format the cells for change it to numeric.

10

11 General guidelines for data entry

General guidelines for data entry

1. Give each variable a valid name (8 characters or less with no spaces or punctuation, beginning with a letter not a numeric number). Short, easy to remember word names. Avoid the following variable names: TEST, ALL, BY, EQ, GE, GT, LE, LT, NE, NOT, OR, TO, WITH. These are used in the SPSS syntax and if they were permitted, the software would not be able to distinguish between a command and a variable. Each variable name must be unique; duplication is not allowed. Variable names are not case sensitive. The names NEWVAR, NewVar, and newvar are all considered identical.

2. Encode categorical variables. Convert letters and words to numbers.

3. Avoid mixing symbols with data. Convert them to numbers.

4. Give each patient a unique, sequential case number (ID). Place this ID number in the first column on the left

11

12 5. Each variable should be in its own column

5. Each variable should be in its own column

Change to: Animal Group 1 0 2 0 3 1 4 1

Avoid this: Animal Control1 Control2 Experiment1 Experiment2

* Do not combine variables in one column

* It is recommended to use 0/1 for 2 groups with 0 as a reference group.

6. All data for a project should be in one spreadsheet. Do not include graphs or summary statistics in the spreadsheet.

12

13 7. Each patient should be entered on a single line or row

7. Each patient should be entered on a single line or row

Do not copy a patient’s information to another row to perform subgroup analysis.

8. However when data are repeatedly collected over a patient, it’s recommended to have patient-day observation on a simple line to ease data management. SPSS has a nice feature to convert from the longitudinal format to horizontal format. When the number of repeats are few 2 or 3, horizontal format may be preferred for simplicity.

Longitudinal data entry

Horizontal data entry

Date ID SYSBP 1/2/2005 1 130 1/3/2005 1 120 1/4/2005 1 120 3/1/2005 2 110 3/2/2005 2 140

ID SYSBP1 SYSBP2 SYSBP3 1 130 120 120 2 110 140

13

14 9. For yes/no questions, enter “0” for no and “1” for yes

9. For yes/no questions, enter “0” for no and “1” for yes

Do not leave blanks for no. Do not enter “?”, “*”, or “NA” for missing data because this indicates to the statistical program than the variable is a string variable. String variables cannot be used for any arithmetic computation.

10. Put ordinal variables into one column if they are mutually exclusive.

Preferred: Pain 1 2 3

Avoid: Pain Mild Moderate Severe 1 0 0 0 1 0 0 0 1

11. Do not make columns wider then 8 characters, unless absolutely essential.

14

15 Entering Date in Excel

Entering Date in Excel

In Excel,go to: Format, Cells, select Date under Category, Choose Type for a format you like

15

16 Entering Time in Excel

Entering Time in Excel

In Excel, go to: Format, Cells, select Time under Category, Choose Type for a format you like

16

17 In Excel, go to: Format, Cells, select Time under Category, Choose

In Excel, go to: Format, Cells, select Time under Category, Choose

Data/Time format

Entering Date / Time in Excel.

17

18 Entering Date, Time in SPSS

Entering Date, Time in SPSS

In SPSS, open Variable View, Click Type for the variable you want to Assign date format, click on Date, and select a format of your choice.

18

19 Importing data from Excel spreadsheet into SPSS

Importing data from Excel spreadsheet into SPSS

In SPSS, go to: File, Open, Data Select Type of file (for example, Excel) you want to open Select File name you want to open

19

20 Importing data from SPSS to Excel

Importing data from SPSS to Excel

In SPSS, go to: Data, Save as, Select Type of file (for example, Excel) you want to save into Give File name you want to save into

20

21 Data merging in SPSS (1)

Data merging in SPSS (1)

Make sure that both files are sorted by Key variable in ascending order In SPSS, open Data from Hell to Heaven.sav Select Add Variables under Data, Merge Files

21

22 Data merging in SPSS (2)

Data merging in SPSS (2)

4. Select the dataset you want to merge into the working file.

22

23 Data merging in SPSS (3)

Data merging in SPSS (3)

Click on Match cases on key variables in sorted files, Click on Both files provide cases Highlight ID in the excluded variables box, then click ? near key Variables

23

24 Note in Data merging in SPSS (3)

Note in Data merging in SPSS (3)

Cases must be sorted in the same order in both data files. If one or more key variables are used to match cases, the two data files must be sorted by ascending order of the key variable. Variable names in the second data file that duplicate variable names in the working data file are excluded by default because Add Variables assumes that these variables contain duplicate information. Thus before you merge data files, you need carefully to check two variables with the same name. If two variables contain different information, SPSS automatically delete variable from the file, which is being merged into (Birthday.sav).

24

25 1.3 Data Cleaning in SPSS

1.3 Data Cleaning in SPSS

1. Re-coding existing variables

2. Creating new variables

3. Creating new variable from existing variables

4. Data labeling and formatting

25

26 Data cleaning in SPSS (1): Recoding existing variables (1)

Data cleaning in SPSS (1): Recoding existing variables (1)

We want to use numeric coding for group instead of A and B.

Old New ID Group Group 1 A 0 2 A 0 3 B 1 4 B 1

26

27 Data cleaning in SPSS (2): Recoding existing variables (2)

Data cleaning in SPSS (2): Recoding existing variables (2)

From SPSS dialog box, go to: Transform Recode Into Same variables

27

28 Data cleaning in SPSS (1): Recoding existing variables (3)

Data cleaning in SPSS (1): Recoding existing variables (3)

1. Select Group from the variable box into String Variables box 2. Click on Old and new Values to proceed

28

29 Data cleaning in SPSS (1): Recoding existing variables (4)

Data cleaning in SPSS (1): Recoding existing variables (4)

1. Type the old value and the new value you want to convert into 2. Click on Add (To remove, or change, click on Change or Remove) 3. Type all values in the Old ? New box, then click Continue 4. Click OK to execute the commands.

29

30 Data Cleaning in SPSS (2) Creating a new variable for Diastolic blood

Data Cleaning in SPSS (2) Creating a new variable for Diastolic blood

pressure (DiasBP):

In SPSS, go to Variable View, Then type DiasBP at the last row under Name

Go back to Data View and directly type diastolic blood pressure to separate from SysBP. For ease of data entry, you can move DiasBP right after SysBP. Now also edit sysBP.

30

31 Data Cleaning in SPSS (3)

Data Cleaning in SPSS (3)

Computing patient’s age from birthday and date enrolled into the study.

31

32 Data Cleaning in SPSS (4): Data labeling and formatting (1) Specifying

Data Cleaning in SPSS (4): Data labeling and formatting (1) Specifying

Type of Variable

HT 61.00 68.00 47.00 66.00 72.00 67.00 72.00 72.00 66.00 60.00 61.00 59.00 73.00 65.00 71.00 68.00 69.00 66.00 66.00 68.00

32

33 Data Cleaning in SPSS (4): Data labeling and formatting (2)

Data Cleaning in SPSS (4): Data labeling and formatting (2)

Data Labeling

33

34 Data Cleaning in SPSS (4): Data labeling and formatting (3)

Data Cleaning in SPSS (4): Data labeling and formatting (3)

Variable Formatting

34

35 Data Cleaning in SPSS (4): Data labeling and formatting (4)

Data Cleaning in SPSS (4): Data labeling and formatting (4)

Specifying missing values

35

36 Data Cleaning in SPSS (4): Data labeling and formatting (5)

Data Cleaning in SPSS (4): Data labeling and formatting (5)

Measurement category

36

37 Retrieve data property from existing files in SPSS (1)

Retrieve data property from existing files in SPSS (1)

This property is extremely handy when you need to construct a similar database for expanded, or new group of patients. You can save time on creating variable label, format, etc, rather you can retrieve these information from existing files.

Now let’s create a copy from “Data from heaven.sav” after you delete formats and labels you just created. Save it as “Data from hell to heaven without format.sav”.

Modified

Note: Before you perform this commands, make sure that Type of variables matched between the two datasets.

37

38 Retrieve data property from existing files in SPSS (2)

Retrieve data property from existing files in SPSS (2)

38

39 Retrieve data property from existing files in SPSS (3)

Retrieve data property from existing files in SPSS (3)

39

40 Using syntax in SPSS:

Using syntax in SPSS:

SPSS has its great advantage in producing high level graphs and statistical analysis by easy point-and-click operations. However, some people may criticize SPSS for irreproducibility of analysis which were conducted before. In fact, SPSS has a high level capacity of programming syntax which can be saved and repeatedly operated.

Throughout the course, I will provide “how to” box to conduct all analysis used in the class, here I will show how to save your commands in syntax. I highly recommend the use of syntax for better organization on haw has been done.

40

41 Using syntax in SPSS (1): Creating a new syntax file

Using syntax in SPSS (1): Creating a new syntax file

41

42 Using syntax in SPSS (2): Editing a syntax file

Using syntax in SPSS (2): Editing a syntax file

42

43 Using syntax in SPSS (3): Saving a syntax file

Using syntax in SPSS (3): Saving a syntax file

43

44 Using syntax in SPSS (4): Opening an existing syntax

Using syntax in SPSS (4): Opening an existing syntax

44

45 Using a syntax in SPSS (5): Example Syntax

Using a syntax in SPSS (5): Example Syntax

I find syntax very handy especially when you get tired of clicking so many times!

45

46 Using syntax in SPSS (6):Recoding syntax from command dialog box

Using syntax in SPSS (6):Recoding syntax from command dialog box

You can in fact use command dialog box (point and click method) as your main tool and still save what you did with point and click into syntax. Then later you can simply execute the syntax to repeat the analysis.

Step 1

46

47 Saved syntax from the previous PASTE command

Saved syntax from the previous PASTE command

Step 2:

47

48 Using syntax in SPSS (7): Executing the syntax

Using syntax in SPSS (7): Executing the syntax

48

49 Data confidentiality

Data confidentiality

Data need to be stored in a secure locked place, need to be back-up daily or once a week. When you send your data to a biostatistician for further statistical analysis, delete patient name, social security numbers, medical record numbers, actual dates (birth day, admission date, etc)

49

50 Communication with a biostatistician:

Communication with a biostatistician:

Most statisticians prefer to have data submitted as SPSS format or in the statistical software they use. An advantage of entering data directly into a statistical package, such as SPSS is that one can enter variable label and value labels in the file.

When communicating with a biostatistician, also describe the research problem, study hypothesis, and the primary comparison that you are interested in. Explain any variables that need to be controlled for. Explain the code used for missing values.

Also answer the following questions:

What is the name of your study? What is the purpose of your study? What is the type of your study? Will all subjects be included in the analysis? Was there any matched (repeated) measures? How will outliers be defined and handled? Has the data been cleaned? What is our goal and deadline for this goal?

50

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