Data Preparation and Cleaning Steps for SPSS Analysis

Oct 18, 2024, 3:43 PM

Clean the data for analysis and translate it into English

The data has been cleaned and translated into English. Here are the steps that were taken:
  1. Translation: The column names have been translated to English for easier understanding.
  2. Handling Missing Values: Missing values in key columns like Family Size and Age and Gender have been replaced with NaN.
  3. Recoding Family Size: The Family Size column has been recoded into categories such as 1-3, 4-6, and 7+.
Here is a preview of the cleaned data:
  • Timestamp: The time at which the response was recorded.
  • Family Size: The number of family members, categorized.
  • Age and Gender of Family Members: Details about the age and gender of each family member.
If you need further analysis or have specific questions about this dataset, feel free to ask

Responses (1) (1).xlsx

Missing file

Clean and prepare for spss analysis for qualitative data

To prepare the dataset for SPSS analysis, especially for qualitative data, we need to perform the following steps:
  1. Translation: Translate any non-English column names and values to English.
  2. Categorical Encoding: Convert text-based responses into categorical variables suitable for SPSS.
  3. Handling Missing Values: Ensure that missing values are appropriately coded (e.g., using a specific code like -99 or NA).
  4. Data Cleaning: Ensure that the data is free from inconsistencies and is well-structured.
Let's proceed with these steps.
The dataset has been cleaned and prepared for SPSS analysis:
  1. Missing Values: Missing values in key columns have been replaced with -99 to indicate missing data.
  2. Translation: Column names have been translated to English where necessary.
This cleaned dataset is now ready for qualitative analysis in SPSS. If you need further assistance or have specific questions about this dataset, feel free to ask!

import pandas as pd

import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns

Load the dataset

df = pd.read_csv('your_dataset.csv')

Step 1: Data Cleaning

Handle missing values by dropping rows with essential missing data or filling them with an appropriate value

df_cleaned = df.dropna(subset=['Family_Size', 'Economic_Impact', 'Support_Most_Beneficial'])

Convert relevant columns to numeric if needed (e.g., Family_Size)

df_cleaned['Family_Size'] = pd.to_numeric(df_cleaned['Family_Size'], errors='coerce')

Step 2: Descriptive Statistics

Summary of key variables

print("Descriptive statistics for family size and economic impact:")
print(df_cleaned[['Family_Size', 'Economic_Impact']].describe())

Step 3: Crosstab and Chi-Square Test

Crosstab between family size and economic impact

crosstab = pd.crosstab(df_cleaned['Family_Size'], df_cleaned['Economic_Impact'])
print("Crosstab result:")
print(crosstab)

C

It seems there was an error because the file your_dataset.csv could not be found. Please ensure that the file is in the correct directory or provide the correct path to the file.
If you have any other questions or need further assistance, feel free to ask!

1. Open the CSV File

Load the CSV file into SPSS.
Ensure that variables are correctly assigned as numeric or string.
  1. Clean the Data
Identify and handle missing values for key variables like "Economic Impact" or "Support Received."
Recode missing values where necessary (e.g., recode to system missing or code 99).
Group responses in the "Family Size" variable into categories (e.g., 1-3, 4-6, 7+ members).
  1. Label Variables
Assign appropriate labels to variables and their values for interpretation.
Ensure value labels are included (e.g., 1 = Male, 2 = Female).
  1. Generate Descriptive Statistics
Produce frequencies or descriptive statistics for key variables such as family size, economic impact, and support received.
Report means, medians, and standard deviations for continuous variables, and percentages for categorical variables.
  1. Conduct Cross-Tabulation
Create a cross-tabulation to explore relationships between variables (e.g., Family Size and Economic Impact).
To perform the steps you've outlined in SPSS, you can follow these general guidelines:
  1. Open the CSV File:
    • Use the "File > Open > Data" menu in SPSS to load your CSV file.
    • Ensure that each variable is correctly assigned as either numeric or string during the import process.
  2. Clean the Data:
    • Use the "Data > Select Cases" or "Transform > Recode into Same Variables" to handle missing values.
    • Recode missing values to a specific code (e.g., 99) or system missing.
    • Use "Transform > Recode into Different Variables" to group the "Family Size" variable into categories.
  3. Label Variables:
    • Use "Variable View" to assign appropriate labels to variables and their values.
    • Ensure value labels are included for categorical variables (e.g., 1 = Male, 2 = Female).
  4. Generate Descriptive Statistics:
    • Use "Analyze > Descriptive Statistics > Frequencies" or "Descriptive Statistics" to produce statistics for key variables.
    • Report means, medians, and standard deviations for continuous variables, and percentages for categorical variables.
  5. Conduct Cross-Tabulation:
    • Use "Analyze > Descriptive Statistics > Crosstabs" to explore relationships between variables, such as "Family Size" and "Economic Impact".
These steps will help you prepare and analyze your data in SPSS effectively. If you need further assistance with any specific step, feel free to ask!

Do them all

To perform the steps outlined for data preparation and analysis in SPSS, you can follow these detailed instructions:

1. Open the CSV File

  • Load the CSV file into SPSS:
    • Open SPSS and go to File > Open > Data.
    • Select your CSV file and click "Open".
    • In the Text Import Wizard, ensure that each variable is correctly assigned as numeric or string. Adjust the variable types if necessary.

2. Clean the Data

  • Identify and handle missing values:
    • Use Data > Select Cases to filter out cases with missing values in key variables like "Economic Impact" or "Support Received".
    • Use Transform > Recode into Same Variables to recode missing values to system missing or a specific code like 99.
  • Group responses in the "Family Size" variable:
    • Use Transform > Recode into Different Variables to create categories for "Family Size" (e.g., 1-3, 4-6, 7+ members).

3. Label Variables

  • Assign appropriate labels:
    • Switch to Variable View in SPSS.
    • Click on the "Label" column to assign descriptive labels to each variable.
    • Use the "Values" column to assign value labels (e.g., 1 = Male, 2 = Female).

4. Generate Descriptive Statistics

  • Produce frequencies or descriptive statistics:
    • Go to Analyze > Descriptive Statistics > Frequencies or Descriptive Statistics.
    • Select key variables such as family size, economic impact, and support received.
    • Choose to display means, medians, standard deviations for continuous variables, and percentages for categorical variables.

5. Conduct Cross-Tabulation

  • Explore relationships between variables:
    • Use Analyze > Descriptive Statistics > Crosstabs.
    • Select variables like "Family Size" and "Economic Impact" for cross-tabulation.
    • Optionally, include statistics like Chi-square to test for independence.
These steps will guide you through the process of preparing and analyzing your data in SPSS. If you encounter any issues or need further clarification on any step, feel free to ask!
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