L4
Quantitative Research Overview
Presenter: Kamyar Motavaze, Ph.D. Institution: Oxford College
Key Terms
Sample: A subset of a population selected for measurement, carefully chosen to represent the larger group in order to draw valid conclusions.
Data Collection: The systematic process of gathering information for analysis and interpretation. This may involve various methods such as surveys, experiments, and observational studies.
Statistical Power: The probability of correctly rejecting a false null hypothesis, which depends on the sample size, effect size, and significance level (alpha). Higher statistical power increases the likelihood of detecting an effect if there is one.
Inter-rater Reliability: The degree of agreement among different raters or observers measuring the same phenomenon, crucial for ensuring the consistency of qualitative assessments.
Test-retest Reliability: A measure of the consistency of a test over time; determined by administering the same test to the same subjects at different points in time.
Internal Consistency: A measure of reliability among items in a test, indicating how well the items measure the same construct.
Cronbach’s Alpha: A statistic used to assess internal consistency; an acceptable threshold is ≥0.70, indicating good reliability among test items.
Sensitivity: The test's ability to correctly identify true positives, important in determining the effectiveness of diagnostic tests.
Specificity: The test's ability to correctly identify true negatives, ensuring that the test can accurately rule out conditions in non-affected individuals.
True Positive Rate: The proportion of actual positives correctly identified by the test, significant in assessing the accuracy of a screening method.
True Negative Rate: The proportion of actual negatives correctly identified, representing the specificity of a diagnostic test.
Data Analysis: Techniques used to evaluate collected data, including statistical tests, modeling, and interpretation of results to inform decisions or hypotheses.