Data
Serkan Adıgüzel
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Coding
Scoring
Course
Outline Data Entry
Importing Data: Excel to SPSS
Data Editing
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Data Preparation
§ Data Preparation ensures the accuracy of the
data and their conversion from raw form to
reduced and classified forms that are more
appropriate for analysis.
§ After data obtained through questionnaire, they
need to be coded, scored, and edited.
§ Outliers, inconsistencies and blank responses, if
any, have to be handled in the right way.
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§ Coding
§ Data coding involves assigning a number to the participants responses so,
they can be entered into data base.
§ In coding, categories are the partitions of a data set of a given variable.
§ For instance, if the variable is gender, the categories are male and female (1
for female, 2 for male)
§ if the variable is groups, the categories are experimental and control (1 for
experimental, 2 for control)
§ if the variable is age, you don’t have to assign any number since it is a
interval value but if you want to make an age categorization, then yes it is,
the categories are young, adult, elder etc. (1 for young, 2 for adult, 3 for
elderly)
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§ Scoring
§ We can enter all the values from the scales.
§ Factor analysis, missing values
§ If not, we score the scales first!
§ calculate the sub-scores if any
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Data Entry
§ After responses have been coded, they can be
entered into data base.
§ Raw data can be entered through any software
program.
§ For example: SPSS Data Editor, Excel sheet
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§ ABSTRACT
§ Objective: The aim of this study was to investigate the prevalence of internet addiction (IA), and the relationship between (IA) and
cognitive functions like selective and sustained attention, inhibitory control, set shifting by using Stroop and Trail Making Tests.
Methods: Two hundred and twenty-one university students participated in the research. At the first step, the participants were filled
Sociodemographic Form and Internet Addiction Scale (IAS). In the second step, 30 participants with IA whose score was above the
threshold (≥81 for IAS) and 29 participants without IA whose score was under the threshold for IAS were recruited. Stroop Test (ST), Trail
Making Tests (TMT), and Beck Depression Inventory (BDI) were applied to individuals with and without IA at the second step of the
study. Results: The prevalence of IA was 15.3% (n=34) in all participants. Four of the participants who had IA did not accept to participate
in the second step. IAS score was positively correlated with Stroop phonemic time, Stroop color naming time, Stroop interference time,
TMT-B form time and TMT-B form false number (respectively; r=0.31, r=0.50, r=0.46, r=0.39, and r=0.66). Conclusion: Our results have
shown that attention-related problems and inability of behavioral control may be related to IA.
§ (Anatolian Journal of Psychiatry 2018; 19(6):593-598)
§ Keywords: internet addiction, attention, cognitive functions, stroop test, trial making test
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§ https://www.ibm.com/products/spss-statistics
§ 30 Days Trial
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§ Trail-Making A
§ Duration
§ Mistake
§ Trail-Making B
§ Duration
§ Mistake
§ B-A Scores
§ Duration
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§ REAL SPSS FILE EXAMPLE
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Importing Data:
Excel to SPSS
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Importing Data:
Excel to SPSS
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Data Editing
§ Data editing deals with detecting and correcting
§ illogical,
§ inconsistent data,
§ and omissions in the information returned by the participants of study.
§ After data entered, the blank responses, if any, have to be handled
in the right way, and inconsistent data have to be checked and
followed up.
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§ Illogical
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§ Inconsistent
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§ Omission
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§ When entry gaps are present, a callback should be made rather
than guessing what the respondent probably said.
§ If callback is not applicable use statistics.
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Next Week Prepare your SPSS data file with all the parameters you have on
Presentation your research design.
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