FEM3002 Research Method: Topic 9

FEM3002 Research Method: Topic 9 Data Analysis (Part 2)

• Statistical Analysis involves descriptive and inferential statistics.
• Descriptive Statistics provides a way to describe data in manageable forms, including data collection, tabulation, summarization to create meaningful information.
• Descriptive statistics include quantitative descriptions of single variables, and the associations between variables.
• Data Reduction is a type of descriptive statistics, used to reduce unmanageable data and summarize it into manageable categories. For example, data on 100 different ages could be summarized into 3-4 age categories.
• Measures of Association show the nature and extent of the relationship between variables.
  • For nominal variables, it's Lambda.
  • For ordinal variables, it's Gamma.
  • For interval or ratio variables, it's Pearson's product-moment correlation (r).
• The Value of r (Pearson Correlation Coefficient):
  • 0.0: no linear relationship.
  • +1.0: strong positive linear relationship (as X increases, Y increases).
  • -1.0: strong inverse linear relationship (as X increases, Y decreases).
• Positive or Negative r: Graphs illustrate the direction of a relationship.
• Pearson Correlation Analysis: used in statistical software like SPSS, to find correlation between variables (e.g., age and income).
• Regression Analysis: represents relationships between variables via equations to predict values of a dependent variable based on independent variables.
• Simple Linear Regression: a basic regression equation (Y = a + bx + e) explains the relationship; where:
  • Y: estimated value of dependent variable
  • a: constant/intercept
  • b: slope, coefficient of independent variable (X)
  • e: error
• Significance Level: Researchers set a significance level for statistical tests using probability theory to assess if findings are real or due to chance.
• Multiple Regression: involves multiple independent variables (IV) to predict the dependent variable (DV).
• Inferential Statistics: helps draw conclusions from a sample to a population.
  • It's based on the assumption of normal distribution of population variables.
  • The area under a bell curve of the distribution is very important in inferential statistics
  • Inferential statistics also requires that the sample accurately represents the population from which it's drawn. Random selection is key in creating representative samples.

Software Usage

• SPSS: software used for performing statistical analyses like frequency analysis. SPSS has a user-interface to input data from a CSV file, and calculate values like correlations or regressions. A video on frequency analysis in SPSS is linked.

This quiz covers statistical analysis in research methods, focusing on descriptive and inferential statistics. You'll explore data collection, summarization techniques, and measures of association between variables. Test your understanding of key statistical concepts such as data reduction and Pearson's correlation coefficient.