Reliability and Validity in Research: Predictive Validity

Dr. Rakesh Maurya, an expert on qualitative methodologies, explains predictive validity in research. He currently works at the University of North Florida, U.S.A., and teaches qualitative research to doctoral students.
Explore the concept of predictive validity in our latest video! We explain what predictive validity is, its significance in research, and how to measure it effectively. This video is perfect for doctoral students in education, qualitative researchers, and anyone interested in understanding research methodologies.
Keywords:
Predictive validity, research methods, qualitative research, educational research, validity types, research validity, doctoral students, education, research video, qualitative data, data analysis, research methodology, Construct validity, Research methodology, Validity in research, Measurement accuracy, Theoretical constructs, Research reliability, Credibility in research, Validity types, Qualitative research, Quantitative research, parallel forms reliability, equivalent forms reliability, test reliability, assessment consistency, reliability measures, educational assessments, psychological assessments,Test-retest reliability, parallel forms reliability, inter-rater reliability, internal consistency reliability, Cronbach's alpha, split-half reliability, equivalent forms reliability, content validity, construct validity, criterion validity, convergent validity, discriminant validity, predictive validity, face validity, external validity, internal validity, ecological validity, statistical conclusion validity, sampling bias, instrument bias, researcher bias, measurement error, generalizability, transferability, dependability, credibility, confirmability, triangulation, reflexivity, member checking, audit trail, saturation, negative case analysis, peer debriefing, investigator triangulation, methodological triangulation, data triangulation, thick description, case study rigor, research transparency, researcher positionality, analytical rigor, trustworthiness, measurement validity, fidelity, reproducibility, replicability, data integrity, response bias, instrument standardization.