Uses statistical methods to analyze and interpret data, establish relationships between variables, and test hypotheses
Applies statistical methods to quantify relationships, identify patterns, and make statistical inferences that help support or refute research hypotheses
Requires a large sample of the population to deliver reliable results
Larger sample sizes help researchers identify outliers in data and provide smaller margins of error
Is considered hypothesis-driven because it involves testing specific hypotheses or research questions using numerical data and statistical methods
Collects quantifiable information that can be used to identify patterns and averages, make predictions, and generalize results to larger populations
The standardized nature allows for the replication of studies, reinforcing its objectivity and reliability
Requires recommendations to provide practical implications and guidance for future actions or decision-making
Uses a deductive approach, developing a hypothesis based on existing theory and then designing a research strategy to test the hypothesis
Is generalizable, meaning the research findings can be generalized to individuals other than those who participated in the study
Is considered important in drawing conclusions because it uses quantifiable data to make objective and reliable inferences about a population or phenomenon
Often requires tables and charts to present numerical data in a clear and organized manner
Is regarded as standardized because it adheres to a structured methodology, often employing standardized tools like surveys with fixed response options
Is close-ended because it is focused on collecting quantifiable data through structured surveys or questionnaires with pre-determined response options