The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter?
<p>Abstract</p> <p>Background</p> <p>Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data repres...
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BMC
2011-08-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | http://www.biomedcentral.com/1471-2288/11/118 |
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| author | Gajewski Byron J Mahnken Jonathan D Hou Qingjiang Dunton Nancy |
| author_facet | Gajewski Byron J Mahnken Jonathan D Hou Qingjiang Dunton Nancy |
| author_sort | Gajewski Byron J |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings.</p> <p>Methods</p> <p>Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI<sup>®</sup>) for the 3<sup>rd </sup>quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect.</p> <p>Results</p> <p>Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter.</p> <p>Conclusions</p> <p>The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects.</p> |
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| spelling | doaj.art-8fa3f540ee5141aa81b11aaf8a385e2b2022-12-22T03:10:27ZengBMCBMC Medical Research Methodology1471-22882011-08-0111111810.1186/1471-2288-11-118The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter?Gajewski Byron JMahnken Jonathan DHou QingjiangDunton Nancy<p>Abstract</p> <p>Background</p> <p>Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings.</p> <p>Methods</p> <p>Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI<sup>®</sup>) for the 3<sup>rd </sup>quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect.</p> <p>Results</p> <p>Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter.</p> <p>Conclusions</p> <p>The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects.</p>http://www.biomedcentral.com/1471-2288/11/118Data transformationNDNQINursing quality indicatorANOVA, Mixed model |
| spellingShingle | Gajewski Byron J Mahnken Jonathan D Hou Qingjiang Dunton Nancy The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter? BMC Medical Research Methodology Data transformation NDNQI Nursing quality indicator ANOVA, Mixed model |
| title | The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter? |
| title_full | The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter? |
| title_fullStr | The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter? |
| title_full_unstemmed | The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter? |
| title_short | The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter? |
| title_sort | box cox power transformation on nursing sensitive indicators does it matter if structural effects are omitted during the estimation of the transformation parameter |
| topic | Data transformation NDNQI Nursing quality indicator ANOVA, Mixed model |
| url | http://www.biomedcentral.com/1471-2288/11/118 |
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