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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Statistical Modeling for (Actual) Hypothesis Testing: Building Cumulative Knowledge in Corpus Linguistics

Statistical Modeling for (Actual) Hypothesis Testing: Building Cumulative Knowledge in Corpus Linguistics

Larsson, Tove
Hancock, Gregory R.

Paperback

Series: Elements in Corpus Linguistics

Linguistics

PREORDER - Expected ship date August 31, 2026

ISBN10: 1009660918
ISBN13: 9781009660914
Publisher: Cambridge University Press
Published: Aug 31 2026
Pages: 75
Language: English
By building knowledge in a deliberate and systematic manner, we can gain a more complete understanding of a given research area relevant to corpus linguists. Specifically, empirically informed hypotheses (i.e., hypotheses that result from a synthesis of findings from all relevant prior studies) play a key role in this endeavor in that they enable us to test to what extent generalizations from previous research are consistent with our results, or if we need to make adjustments to our existing knowledge or theory. In this Element, we aim to provide a practical and accessible introduction to select statistical methods for evaluating such empirically informed hypotheses. In particular, we illustrate techniques from the broader null-hypothesis significance testing framework (e.g., equivalence testing), and structural equation modeling framework (e.g., measured variable path analysis), with the goal of encouraging knowledge building in a more principled and systematic manner in corpus linguistics.

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