1. DATA ENTRY
Prof. Dr. Hamit ACEMOĞLU 1

2. The aim By the end of this lecture,
students will be avare of data entry &
be able to perform data entry by using SPSS.
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3. The goals Comprehend the importance of preperation before data entry
Be able to produse a data set in SPSS
Explain ASCII term
Explain the importance of exchange between data sets
Explain the importance of “zero” wile entering yes/no variables
Produse a field on SPSS for multi coded variablels
Be able to explain how to enter missing data
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4. Otherwise difficulties and mistakes could happen during analysis.
At the present time, research data are analysed only by using computers.
Begining from collecting data, up to entering to a computer, a systematic way mast be followed and obeyed certain rules.
Otherwise difficulties and mistakes could happen during analysis.
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5. Methods of entering data
During entering data, it sould be kept in mind that; there will be in need of exchane between some softwares.
Generally, the software which will be used is certain before begining research.
If it is needed, inorder to have different idea, the data may be send to other persons.
For this, during data entry, using standard methods would be better.
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6. (American Standard Code for Information Interchange)
Data entry is done as ASCII format or text file which is based on the simplest standard English alphabet and 128 of characters.
ASCII
(American Standard Code for Information Interchange)
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8. Here, the data of every individual will be entered in a different row and reagent maybe used between wariables, such as blank, tabulation or coma.
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11. Since most of the current statistical program, allow as extraction of text and excel files, there is not usually a problem in exporting and converting data.
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12. [File > Open > Data> File type]
*SPSS (Statistical Package for the Social Sciences) -Old name
*SPSS (Statistical Product and Service Solutions) -New name
In SPSS 20; Excel, Text, dBase, SATA, Data, and data can be received from over 10 different file formats including Lotus formats.
[File > Open > Data> File type]
In addition, dBase, Access and Excel data format export can be performed.
[File> Export to database]
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13. Planning data entry
Veri girişinin daha araştırmanın başlangıç aşamasında, veri toplama formu (anket) hazırlanırken planlanması gerekir. Verileri toplarken bilgisayara da nasıl gireceğimizi düşünmeliyiz: örn:
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14. Planning data entry
Veri girişinin daha araştırmanın başlangıç aşamasında, veri toplama formu (anket) hazırlanırken planlanması gerekir. Verileri toplarken bilgisayara da nasıl gireceğimizi düşünmeliyiz: örn:
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15. Types of variable
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16. Categorical data entry
Wile entering categorical data into the computer, instead of plain text, numbers representing the categories must be entered.
In stead of Gender: Male, Female
Gender: 1 (Male),
2 (Female)
Also in this way, data entry will be much faster.
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17. Yes / no categorization in the form of two (binary) data for;
"No" option to "0"
“Yes” option to "1"
would be more accurate to encode.
Otherwise, in some analyzes
(e.g. Logistic regression analysis)
since the computer percepts binary categorical data (what ever we enter)
as 0 or 1, confusion may occur during interpretation of results.
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18. 2--There are many options, but few of them may need to be selected.
Variable in our case of multiple choices:
1--There are few options and many of them may need to be selected.
2--There are many options, but few of them may need to be selected.
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19. Variable in our case of multiple choices:
1--There are few options and many of them may need to be selected.
In this case, it is better to convert the choices to yes/no questions one by one.
e.g.
What are the patient's complains related to the respiratory system?
Q1 Cough ( ) No, 1 ( ) Yes
Q2 Shortness of breath 0 ( ) No, 1 ( ) Yes
Q3 Hemoptysis ( ) No, 1 ( ) Yes
Q4 Sputum ( ) No, 1 ( ) Yes
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20. Variable in our case of multiple choices: 2--There are many options, but few of them may need to be selected. In this case it would be better to create different nominal categorical variables. e.g. Which complains does the patient have? Q1 Symptom 1: Q2 Symptom 2: Q3 Symptom 3: Q4 Symptom 4:
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21. Numerical data entry
As noted earlier, numerical data should be recorded as they are measured. Care should be taken to be the same unit:
"How old are you?
a) less than 20
b) 20-40
c) 41-60
d) more than 60" should NOT be asked in this form
"How old are you? : _____ (Year)" should be asked in this form.
If the prticipant is a 6-month-old baby,
it must be entered as 0,5 year, not 6 monts.
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22. Numbering of the survey
Sometimes, there may be more than one survey for one individual.
e.g. People filled the self-questionnaire and blood test results and demographic information filled in by the laboratory.
Each person must be given a number (individual code) to avoid confusion. This number must be written on the questionnaire. When entering data, this number must be entered first.
Thus, when faced a problem with survey data, availability can be checked again. Participant can be reached, the measurement is repeated if necessary.
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23. Empty data coding
Sometimes all of the questions in our study may not be answered.
During the analysis, we must know the cause of the lack of space;
loss of the participant’s attention
lack of data collection
not answering on purpose
not apropriate question
Therefore, we can choose a specific code for empty data (generally 9, 99 or 999).
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24. Have you ever used addictive substances?
e.g.
Have you ever used addictive substances?
0 ( ) No, 1 ( ) Yes
0 ( ) No, 1 ( ) Yes, 999 ( ) No answer
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25. Q1 Is it worthy the money you pay for this dress?
Q1 Is it worthy the money you pay for this dress? 1 ( ) Absolutely yes, 2 ( ) Yes, 3 ( ) I am not sure, 4 ( ) No, 5 ( ) Absolutely no
İnstead
Q1 Is it worthy the money you pay for this dress?
1 ( ) Absolutely yes, 2 ( ) Yes, 3 ( ) I am not sure, 4 ( ) No, 5 ( ) Absolutely no
999 ( ) Inapropriate / Unaswered
[people may not paied money for clothes]
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26. Example
-Preparing a data set
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27. Summary Methods of entering data Planning data entry
Numbering of the survey
Empty data coding
Preparing a data set
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