Survey Data Exploration via SPSS
Budget: £20 – £250 GBP
I am seeking an experienced analyst with a background in SPSS software to explore patterns and relationships in our survey data. This will primarily involve performing descriptive statistical analysis.
Skills and experience required for this job are:
- Proficiency in SPSS software for statistical analysis
- Familiarity with survey data
- Strong background in descriptive statistics
- Ability to identify and explore patterns and relationships in data
Descriptive Statistics:
Frequency distributions for categorical variables (ethnic group, age, gender, employment status, awareness, and participation levels).
Measures of central tendency and dispersion for any numerical variables.
Comparative Analysis:
Cross-tabulations to compare awareness and participation levels across different demographic groups (e.g., age, gender, ethnic group).
Chi-square tests to test for associations between categorical variables (e.g., employment status and participation in events).
Correlation Analysis:
Pearson or Spearman correlation to explore relationships between ordinal variables (e.g., importance of environmental issues and interest in clubs).
Visualization Requirements
Bar charts: For frequency distributions of categorical variables.
Pie charts: For visualizing the distribution of responses to key questions.
Histograms: For continuous or ordinal variables.
Scatter plots: To explore relationships between two numerical variables.
Box plots: For comparing distributions across groups.
Skills and experience required for this job are:
- Proficiency in SPSS software for statistical analysis
- Familiarity with survey data
- Strong background in descriptive statistics
- Ability to identify and explore patterns and relationships in data
Descriptive Statistics:
Frequency distributions for categorical variables (ethnic group, age, gender, employment status, awareness, and participation levels).
Measures of central tendency and dispersion for any numerical variables.
Comparative Analysis:
Cross-tabulations to compare awareness and participation levels across different demographic groups (e.g., age, gender, ethnic group).
Chi-square tests to test for associations between categorical variables (e.g., employment status and participation in events).
Correlation Analysis:
Pearson or Spearman correlation to explore relationships between ordinal variables (e.g., importance of environmental issues and interest in clubs).
Visualization Requirements
Bar charts: For frequency distributions of categorical variables.
Pie charts: For visualizing the distribution of responses to key questions.
Histograms: For continuous or ordinal variables.
Scatter plots: To explore relationships between two numerical variables.
Box plots: For comparing distributions across groups.