Data Analysis and Statistics
Data analysis transforms raw data into meaningful information through statistical techniques. It involves organising, summarising, and interpreting data to answer research questions and test hypotheses.
Data Preparation
Before analysis, data must be cleaned (removing errors, handling missing values), coded (assigning numerical values to categories), and entered into analysis software (SPSS, R, Excel, Python). Data screening checks for outliers, normality, and data entry errors.
Descriptive Statistics
Descriptive statistics summarise data. Central tendency: mean, median, mode. Dispersion: range, variance, standard deviation. Shape: skewness, kurtosis. Frequency distributions and cross-tabulations organise categorical data. Visual representations include histograms, bar charts, and box plots.
Inferential Statistics
Inferential statistics draw conclusions about populations from samples. Key concepts: sampling distribution, standard error, confidence intervals, and significance level (α, typically 0.05). The p-value indicates the probability of observing the result if the null hypothesis is true.
Hypothesis Testing
Steps: state null and alternative hypotheses, choose significance level, select the test, compute the test statistic, compare with critical value or p-value, and make a decision (reject or fail to reject H₀). Type I error (false positive) and Type II error (false negative) are risks.
Common Statistical Tests
t-test compares means of two groups. ANOVA compares means of three or more groups. Chi-square tests association between categorical variables. Correlation (Pearson's r) measures linear relationship strength. Regression predicts one variable from others.
Qualitative Analysis
Qualitative data is analysed through coding (identifying themes and patterns), content analysis, thematic analysis, and grounded theory (building theory from data). Software like NVivo assists in managing qualitative data.
Software Tools
Common tools include SPSS (user-friendly, widely used in social sciences), R (powerful, open-source), Python (pandas, scipy, statsmodels), and Excel (basic analysis). Choosing the right tool depends on data complexity and analysis requirements.
Summary
Data analysis bridges data collection and interpretation. Mastery of descriptive and inferential statistics, hypothesis testing, and analysis software is essential for rigorous research.