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Statistical Modeling for (Actual) Hypothesis Testing: Building Cumulative Knowledge in Corpus Linguistics (Elements in Corpus Linguistics) - Softcover
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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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Statistical Modeling for (Actual) Hypothesis Testing
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Statistical Modeling for (Actual) Hypothesis Testing: Building Cumulative Knowledge in Corpus Linguistics (Elements in Corpus Linguistics)
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Statistical Modeling for (Actual) Hypothesis Testing (Paperback)
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Cambridge University Press, Cambridge, 2026
ISBN 10: 1009660918
ISBN 13: 9781009660914
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Paperback. Condition: new. Paperback. 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. Empirically informed hypotheses enable us to test to what extent generalizations from previous research are consistent with our results, or if adjustments are required to our existing knowledge. This Element provides a practical and accessible introduction to select statistical methods for evaluating empirically informed hypotheses. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009660914
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Statistical Modeling for (Actual) Hypothesis Testing
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Statistical Modeling for (Actual) Hypothesis Testing: Building Cumulative Knowledge in Corpus Linguistics
Published by
Cambridge University Press, 2026
ISBN 10: 1009660918
ISBN 13: 9781009660914
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Statistical Modeling for (Actual) Hypothesis Testing: Building Cumulative Knowledge in Corpus Linguistics
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ISBN 10: 1009660918
ISBN 13: 9781009660914
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Statistical Modeling for (Actual) Hypothesis Testing (Paperback)
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Cambridge University Press, Cambridge, 2026
ISBN 10: 1009660918
ISBN 13: 9781009660914
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Paperback. Condition: new. Paperback. 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. Empirically informed hypotheses enable us to test to what extent generalizations from previous research are consistent with our results, or if adjustments are required to our existing knowledge. This Element provides a practical and accessible introduction to select statistical methods for evaluating empirically informed hypotheses. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781009660914
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Statistical Modeling for (Actual) Hypothesis Testing (Paperback)
Published by
Cambridge University Press, Cambridge, 2026
ISBN 10: 1009660918
ISBN 13: 9781009660914
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Paperback. Condition: new. Paperback. 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. Empirically informed hypotheses enable us to test to what extent generalizations from previous research are consistent with our results, or if adjustments are required to our existing knowledge. This Element provides a practical and accessible introduction to select statistical methods for evaluating empirically informed hypotheses. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781009660914
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