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ERIC Number: ED201661
Record Type: RIE
Publication Date: 1981-Apr-14
Pages: 23
Abstractor: N/A
Reference Count: 0
Loglinear Analysis of Learning Hierarchy and Developmental Sequence Data.
Davison, Mark L.
The interest in developmental sequences and learning hierarchies is growing. One approach to the study of such sequences is to gather data on several variables, each of which corresponds to a stage, step, or phase in the sequence and to examine the associations between the variables as displayed in a contingency table. If the variables are associated in ways predicted by the hypothesized sequence, then the data lend support to the sequence. Goodman's loglinear model for developmental or learning sequences is presented and illustrated on number concept data gathered by Brainerd and Fraser. Where its strong assumptions are satisfied, the model provides a probabilistic framework within which to: (1) test the plausibility of an hypothesized developmental sequence or learning hierarchy; (2) compare several hypothesized sequences on the same data; (3) estimate the proportion of subjects who do not conform to the sequence; and (4) estimate the proportion of subjects at each step in the sequence. (Author/RL)
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Academy of Education, Washington, DC.; National Inst. of Education (ED), Washington, DC.
Authoring Institution: N/A
Identifiers: Contingency Tables; Hierarchical Learning; Log Linear Models
Note: Paper presented at the Annual Meeting of the American Educational Research Association (65th, Los Angeles, CA, April 13-17, 1981).