When faced with missing data, researchers have two primary strategies: deletion and imputation. Deletion involves removing rows (listwise deletion) or columns (variable deletion) that contain missing values. This is straightforward but can significantly reduce the dataset size and potentially bias the remaining data if the missingness is not random. Imputation involves replacing missing values with estimated ones. Common imputation methods include using the mean, median, or mode of the variable, or more sophisticated techniques like K-Nearest Neighbors (KNN) imputation or regression imputation. The choice of imputation method should be guided by the nature of the missing data and its potential impact on the analysis.

Many social science datasets contain variables measured on different scales (e.g., Likert scales, counts, percentages). To ensure that these variables contribute equally to analyses and to prevent variables with larger ranges from dominating models, standardization or normalization is often necessary. Standardization (or Z-score normalization) transforms data to have a mean of 0 and a standard deviation of 1, using the formula: z = (x - μ) / σ. Normalization (or min-max scaling) scales data to a fixed range, typically [0, 1], using the formula: x_scaled = (x - min) / (max - min). The choice between these depends on the specific algorithm or analysis being performed.

Outliers can disproportionately influence statistical models and analyses. They can be detected using visual methods like box plots or scatter plots, or statistically using methods like the IQR rule (values outside Q1 - 1.5IQR or Q3 + 1.5IQR) or Z-scores (values with |Z| > 3). Once identified, outliers can be addressed by removing them (if they are clearly errors), transforming the data (e.g., using log transformations), or capping them (replacing them with a maximum or minimum allowable value). It's crucial to investigate the cause of outliers before deciding on a treatment.

Many social science datasets contain categorical variables. Machine learning algorithms typically require numerical input. Label Encoding assigns a unique integer to each category (e.g., 'Low'=0, 'Medium'=1, 'High'=2). This can imply an ordinal relationship that may not exist. One-Hot Encoding creates new binary (0/1) columns for each category, avoiding the ordinal assumption. For example, a 'Gender' variable with 'Male' and 'Female' could become two columns: 'Is_Male' and 'Is_Female'.

Feature engineering is an art and science that can significantly boost the predictive power of models. In social science, this might involve creating an 'Age Group' variable from a 'Date of Birth' column, calculating a 'Socioeconomic Status Index' from multiple indicators, or extracting sentiment scores from textual survey responses. Domain knowledge is crucial for effective feature engineering.