ANCOVA for nonparallel slopes: the Johnson-Neyman technique
The Johnson-Neyman (JN) procedure, as originally formulated ( Stat Res Mem, 1 (1936) 57–93), applies to a situation in which measurements on 1 dependent (response) variable, X, and 2 independent (predictor) variables, Z 1 and Z 2, are available for the members of 2 groups. The expected value of X is...
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| Veröffentlicht in: | International journal of bio-medical computing 1994-11, Vol.37 (3), p.273-286 |
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| creator | Kowalski, Charles J. Schneiderman, Emet D. Willis, Stephen M. |
| description | The Johnson-Neyman (JN) procedure, as originally formulated (
Stat Res Mem, 1 (1936) 57–93), applies to a situation in which measurements on 1 dependent (response) variable,
X, and 2 independent (predictor) variables,
Z
1 and
Z
2, are available for the members of 2 groups. The expected value of
X is assumed to be a linear function of
Z
1 and
Z
2, but not necessarily the same function for both groups. The JN technique is used to obtain a set of values for the
Z variables for which one would reject, at a specified level of significance α (e.g., α = 0.05), the hypothesis that the 2 groups have the same expected
X values. This set of values, or ‘region of significance,’ may then be plotted to obtain a convenient description of those values of
Z
1 and
Z
2 for which the 2 groups differ. The technique can thus be described as a generalization of the analysis of covariance (ANCOVA) which does not make the assumption that the regression coefficients for the regression of
X on the covariates,
Z
1 and
Z
2, are equal in the groups being compared. In this paper we describe, illustrate and make available a menu-driven PC program (TXJN2) implementing the JN procedure. |
| doi_str_mv | 10.1016/0020-7101(94)90125-2 |
| format | Article |
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Stat Res Mem, 1 (1936) 57–93), applies to a situation in which measurements on 1 dependent (response) variable,
X, and 2 independent (predictor) variables,
Z
1 and
Z
2, are available for the members of 2 groups. The expected value of
X is assumed to be a linear function of
Z
1 and
Z
2, but not necessarily the same function for both groups. The JN technique is used to obtain a set of values for the
Z variables for which one would reject, at a specified level of significance α (e.g., α = 0.05), the hypothesis that the 2 groups have the same expected
X values. This set of values, or ‘region of significance,’ may then be plotted to obtain a convenient description of those values of
Z
1 and
Z
2 for which the 2 groups differ. The technique can thus be described as a generalization of the analysis of covariance (ANCOVA) which does not make the assumption that the regression coefficients for the regression of
X on the covariates,
Z
1 and
Z
2, are equal in the groups being compared. In this paper we describe, illustrate and make available a menu-driven PC program (TXJN2) implementing the JN procedure.</description><identifier>ISSN: 0020-7101</identifier><identifier>DOI: 10.1016/0020-7101(94)90125-2</identifier><identifier>PMID: 7705908</identifier><identifier>CODEN: IJBCBT</identifier><language>eng</language><publisher>Barking: Elsevier B.V</publisher><subject>Analysis of covariance ; Analysis of Variance ; Biological and medical sciences ; Computer Graphics ; Computerized, statistical medical data processing and models in biomedicine ; Confidence Intervals ; Humans ; Kidney Function Tests ; Liver Cirrhosis - physiopathology ; Liver Cirrhosis - surgery ; Mathematical Computing ; Medical sciences ; Medical statistics ; Nonparallel regressions ; PC program ; Randomized Controlled Trials as Topic - methods ; Region of significance ; Regression Analysis ; Software ; Software Design ; Three-dimensional graphics ; Urea - metabolism</subject><ispartof>International journal of bio-medical computing, 1994-11, Vol.37 (3), p.273-286</ispartof><rights>1994</rights><rights>1995 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c432t-60829a4757677dfe6acd7c2aafb44fe734df507e0ac457537fc90310867a5be83</citedby><cites>FETCH-LOGICAL-c432t-60829a4757677dfe6acd7c2aafb44fe734df507e0ac457537fc90310867a5be83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3363106$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/7705908$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kowalski, Charles J.</creatorcontrib><creatorcontrib>Schneiderman, Emet D.</creatorcontrib><creatorcontrib>Willis, Stephen M.</creatorcontrib><title>ANCOVA for nonparallel slopes: the Johnson-Neyman technique</title><title>International journal of bio-medical computing</title><addtitle>Int J Biomed Comput</addtitle><description>The Johnson-Neyman (JN) procedure, as originally formulated (
Stat Res Mem, 1 (1936) 57–93), applies to a situation in which measurements on 1 dependent (response) variable,
X, and 2 independent (predictor) variables,
Z
1 and
Z
2, are available for the members of 2 groups. The expected value of
X is assumed to be a linear function of
Z
1 and
Z
2, but not necessarily the same function for both groups. The JN technique is used to obtain a set of values for the
Z variables for which one would reject, at a specified level of significance α (e.g., α = 0.05), the hypothesis that the 2 groups have the same expected
X values. This set of values, or ‘region of significance,’ may then be plotted to obtain a convenient description of those values of
Z
1 and
Z
2 for which the 2 groups differ. The technique can thus be described as a generalization of the analysis of covariance (ANCOVA) which does not make the assumption that the regression coefficients for the regression of
X on the covariates,
Z
1 and
Z
2, are equal in the groups being compared. In this paper we describe, illustrate and make available a menu-driven PC program (TXJN2) implementing the JN procedure.</description><subject>Analysis of covariance</subject><subject>Analysis of Variance</subject><subject>Biological and medical sciences</subject><subject>Computer Graphics</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Confidence Intervals</subject><subject>Humans</subject><subject>Kidney Function Tests</subject><subject>Liver Cirrhosis - physiopathology</subject><subject>Liver Cirrhosis - surgery</subject><subject>Mathematical Computing</subject><subject>Medical sciences</subject><subject>Medical statistics</subject><subject>Nonparallel regressions</subject><subject>PC program</subject><subject>Randomized Controlled Trials as Topic - methods</subject><subject>Region of significance</subject><subject>Regression Analysis</subject><subject>Software</subject><subject>Software Design</subject><subject>Three-dimensional graphics</subject><subject>Urea - metabolism</subject><issn>0020-7101</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1994</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kM1LwzAUwHNQ5pz-Bwo9iOihmrZJX6sgjOEnY7uo15ClL6zSJjXphP33tq7s6Ckv7_3eBz9CziJ6E9EovaU0piF04VXOrnMaxTyMD8h4nz4ix95_dV8GLBmREQDlOc3G5H66mC0_p4G2LjDWNNLJqsIq8JVt0N8F7RqDN7s23ppwgdtamqBFtTbl9wZPyKGWlcfT4Z2Qj6fH99lLOF8-v86m81CxJG7DlGZxLhlwSAEKjalUBahYSr1iTCMkrNCcAlKpGAeegFY5TSKapSD5CrNkQi53cxtnu7W-FXXpFVaVNGg3XgBAzhmLO5DtQOWs9w61aFxZS7cVERW9J9ELEb0QkTPx50n0befD_M2qxmLfNEjq6hdDXXolK-2kUaXfY0mSdtemHfaww7Bz8VOiE16VaBQWpUPVisKW_9_xC_kQhBY</recordid><startdate>19941101</startdate><enddate>19941101</enddate><creator>Kowalski, Charles J.</creator><creator>Schneiderman, Emet D.</creator><creator>Willis, Stephen M.</creator><general>Elsevier B.V</general><general>Applied Science Publishers</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>19941101</creationdate><title>ANCOVA for nonparallel slopes: the Johnson-Neyman technique</title><author>Kowalski, Charles J. ; Schneiderman, Emet D. ; Willis, Stephen M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c432t-60829a4757677dfe6acd7c2aafb44fe734df507e0ac457537fc90310867a5be83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Analysis of covariance</topic><topic>Analysis of Variance</topic><topic>Biological and medical sciences</topic><topic>Computer Graphics</topic><topic>Computerized, statistical medical data processing and models in biomedicine</topic><topic>Confidence Intervals</topic><topic>Humans</topic><topic>Kidney Function Tests</topic><topic>Liver Cirrhosis - physiopathology</topic><topic>Liver Cirrhosis - surgery</topic><topic>Mathematical Computing</topic><topic>Medical sciences</topic><topic>Medical statistics</topic><topic>Nonparallel regressions</topic><topic>PC program</topic><topic>Randomized Controlled Trials as Topic - methods</topic><topic>Region of significance</topic><topic>Regression Analysis</topic><topic>Software</topic><topic>Software Design</topic><topic>Three-dimensional graphics</topic><topic>Urea - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kowalski, Charles J.</creatorcontrib><creatorcontrib>Schneiderman, Emet D.</creatorcontrib><creatorcontrib>Willis, Stephen M.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of bio-medical computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kowalski, Charles J.</au><au>Schneiderman, Emet D.</au><au>Willis, Stephen M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ANCOVA for nonparallel slopes: the Johnson-Neyman technique</atitle><jtitle>International journal of bio-medical computing</jtitle><addtitle>Int J Biomed Comput</addtitle><date>1994-11-01</date><risdate>1994</risdate><volume>37</volume><issue>3</issue><spage>273</spage><epage>286</epage><pages>273-286</pages><issn>0020-7101</issn><coden>IJBCBT</coden><abstract>The Johnson-Neyman (JN) procedure, as originally formulated (
Stat Res Mem, 1 (1936) 57–93), applies to a situation in which measurements on 1 dependent (response) variable,
X, and 2 independent (predictor) variables,
Z
1 and
Z
2, are available for the members of 2 groups. The expected value of
X is assumed to be a linear function of
Z
1 and
Z
2, but not necessarily the same function for both groups. The JN technique is used to obtain a set of values for the
Z variables for which one would reject, at a specified level of significance α (e.g., α = 0.05), the hypothesis that the 2 groups have the same expected
X values. This set of values, or ‘region of significance,’ may then be plotted to obtain a convenient description of those values of
Z
1 and
Z
2 for which the 2 groups differ. The technique can thus be described as a generalization of the analysis of covariance (ANCOVA) which does not make the assumption that the regression coefficients for the regression of
X on the covariates,
Z
1 and
Z
2, are equal in the groups being compared. In this paper we describe, illustrate and make available a menu-driven PC program (TXJN2) implementing the JN procedure.</abstract><cop>Barking</cop><pub>Elsevier B.V</pub><pmid>7705908</pmid><doi>10.1016/0020-7101(94)90125-2</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | International journal of bio-medical computing, 1994-11, Vol.37 (3), p.273-286 |
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| language | eng |
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| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Analysis of covariance Analysis of Variance Biological and medical sciences Computer Graphics Computerized, statistical medical data processing and models in biomedicine Confidence Intervals Humans Kidney Function Tests Liver Cirrhosis - physiopathology Liver Cirrhosis - surgery Mathematical Computing Medical sciences Medical statistics Nonparallel regressions PC program Randomized Controlled Trials as Topic - methods Region of significance Regression Analysis Software Software Design Three-dimensional graphics Urea - metabolism |
| title | ANCOVA for nonparallel slopes: the Johnson-Neyman technique |
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