Data Review #

Concept:
The researcher conducts an initial review of the collected raw data, correcting errors and removing incomplete, incorrect, or unusable responses (such as blank or irrelevant answers). The goal is to ensure the data’s accuracy, completeness, and authenticity, which lays the foundation for subsequent data entry and analysis.

• Completeness Review: Ensuring all necessary data fields are filled in, without missing responses.
• Accuracy Review: Checking for errors or inconsistencies in the data, such as incorrect or out-of-range values.

Data Conversion #

Data conversion involves translating the responses to questionnaire items into numerical values that can be recognized and processed by a computer for statistical analysis. Example of questionnaire items and their numeric conversion:

• P1. What is your current employment status? (Single choice)
  [1] Employed full-time
  [2] Employed part-time
  [3] Self-employed
  [4] Unemployed
  [5] Student
  [6] Retired

• P2. How often do you exercise? (Single choice)
  [1] Never
  [2] Occasionally
  [3] Weekly
  [4] Several times a week
  [5] Daily

• P3. Do you own a car? (Single choice)
  [1] Yes
  [2] No

• P4. What is your monthly household income? (Single choice)
  [1] Less than $1,000
  [2] $1,000 - $2,000
  [3] $2,000 - $3,000
  [4] $3,000 - $5,000
  [5] More than $5,000

• P5. What is your preferred method of commuting to work? (Single choice)
  [1] Car
  [2] Public transport
  [3] Bicycle
  [4] Walking
  [5] Other

These responses are transformed into numeric values for easier data processing and analysis.

SPSS Data Entry Introduction and Operation #

Data View:
In SPSS, Data View is where the raw data is entered or viewed. Each row represents a case (e.g., a respondent), and each column represents a variable (e.g., a question from the survey).

Variable View:
In Variable View, you define the properties of each variable, such as the variable name, data type, and value labels. This is where you set up the structure of your dataset.

Key fields in Variable View:

• Name: The variable name, which should be concise and descriptive (e.g., Employment_Status, Exercise_Frequency).
• Type: The type of data for each variable (e.g., numeric, string).
• Width: The number of characters allowed for the variable’s values.
• Decimals: The number of decimal places, if applicable (for continuous data).
• Label: A more detailed description of the variable.
• Value: The labels for specific values (e.g., 1 = ‘Employed full-time’, 2 = ‘Never’).
• Missing: Indicates whether there are any missing data values for that variable.
• Columns: The width of each column in the data view.
• Align: Alignment of data in the columns (left, center, right).
• Measure: Specifies the measurement scale for the variable (e.g., nominal, ordinal, scale).

Example:
For the variable Employment_Status (from P1):

• Name: Employment_Status
• Type: Numeric
• Width: 1
• Decimals: 0
• Label: Respondent's current employment status
• Value:
  1 = ‘Employed full-time’
  2 = ‘Employed part-time’
  3 = ‘Self-employed’
  4 = ‘Unemployed’
  5 = ‘Student’
  6 = ‘Retired’
• Missing: Specify if there are any missing responses.
• Measure: Ordinal (since employment status has an ordered relationship).

For the variable Exercise_Frequency (from P2):

• Name: Exercise_Frequency
• Type: Numeric
• Width: 1
• Decimals: 0
• Label: How often the respondent exercises
• Value:
  1 = ‘Never’
  2 = ‘Occasionally’
  3 = ‘Weekly’
  4 = ‘Several times a week’
  5 = ‘Daily’
• Measure: Ordinal (the frequency of exercise has a natural order).

Data Entry Procedure:

1. Open SPSS and create a new dataset.
2. Variable View: Set up your variables (Employment_Status, Exercise_Frequency, etc.) with appropriate names, types, labels, and values (e.g., coding responses like 1 = ‘Employed full-time’, 2 = ‘Never’, etc.).
3. Data View: Enter the numeric values for each participant’s response to each question in the dataset. Each row corresponds to one respondent.
4. After entering the data, perform statistical analysis according to the measurement scale of each variable.

With these steps, your SPSS dataset will be structured and ready for accurate analysis.

#SPSS

Last modified on 2023-12-20