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Skaidrojošā pētniecība×Hipozēžu pārbaudes pētījumi×
NozarePētījuma dizainsPētījuma dizains
SaimeProcess / pipelineProcess / pipeline
Izcelsmes gads1960s–1980s (codified in behavioral and social science methodology)Early 20th century (Fisher 1925; Neyman–Pearson 1933)
AutorsFormalized by Earl Babbie and Fred Kerlinger among othersKarl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson
TipsNon-experimental quantitative research designQuantitative confirmatory research design
PirmavotsKerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417559Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603
Citi nosaukumianalytical research, causal research, explanatory study, explanatory quantitative researchhypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design
Saistītās54
KopsavilkumsExplanatory research is a non-experimental quantitative research design that goes beyond describing a phenomenon to identifying why it occurs — examining the relationships or mechanisms that account for observed patterns. Rooted in positivist social science methodology, it uses theory-driven hypotheses and statistical analysis to test whether specific variables explain variation in an outcome, without necessarily manipulating those variables.Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences.
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ScholarGateSalīdzināt metodes: Explanatory Research · Hypothesis Testing Research. Izgūts 2026-06-18 no https://scholargate.app/lv/compare