Statistical presentation in international scientific publications 6. Reporting more complicated findings Malcolm Campbell Lecturer in Statistics, School

Statistical presentation in international scientific publications 6. Reporting more complicated findings

Malcolm CampbellLecturer in Statistics, School of Nursing, Midwifery &

Social Work, The University of Manchester

Statistical Editor, Health & Social Care in the Community

26 March 2008 Statistical presentation - 6. Reporting more complicated findings

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6. Report more complicated findingsContents

• 6.1 Introduction

• 6.2 Reporting factor analysis

• 6.3 Reporting analysis of variance

• 6.4 Reporting multiple regression

• 6.5 Reporting logistic regression

• 6.6 Reporting survival analysis


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6.1 IntroductionReporting multivariate analyses

• It’s important to be consistent and give the reader clear, concise but complete information– more of a problem in more complicated analyses!

– find a compromise between giving too little and too much information

– this compromise may depend on the readership of the journal


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6.2 Reporting factor analysisSuggestions – see Tabachnick & Fidell (2001, pp 647-648)

• You should report

– how variables were initially chosen and types of variables involved

– preliminary assessment of factorability

• correlations, measures of sampling adequacy

– methods used for extracting and rotating factors

• which combinations were compared

– how the number of factors was determined

• whether other factor solutions were explored

– variance explained for each factor

– a table of rotated factor loadings

– an interpretation of the rotated factors

• You could also report

– communalities of variables


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The Good 1An excellent methodological paper on factor analysis

• Matthews et al (2006)– An exploratory study of the conditions important in

facilitating the empowerment of midwives, Midwifery 22, 181-191

– methods used


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The Good 2The excellent methodological paper on factor analysis

• Matthews et al (2006)

– sample size


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The Good 3That excellent methodological paper on factor analysis again

• Matthews et al (2006)

– pattern matrix showing interpretation, % variance explained, factor loadings and internal reliability of factors


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6.3 Reporting analysis of varianceSuggestions – simplify Lang and Secic (1997, pp 127-135)

• Analysis of variance:

– usually one-way ANOVA

– rarely two- or more-way ANOVA

– analysis of covariance

– repeated measures ANOVA


– appropriate means and standard deviations

– full F-test results

– post-hoc tests allowing for multiple comparisons if required


– (where applicable) an ANOVA table showing sources of variation, sums of squares, mean squares, F-statistics, degrees of freedom and p-values


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The Good 1A paper using repeated measures ANOVA

• Salmon et al (2006)

– An evaluation of the effectiveness of an educational programme promoting the introduction of routine antenatal enquiry for domestic violence, Midwifery 22, 6-14

– methods used


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The Good 2The paper using repeated measures ANOVA

• Salmon et al (2006): descriptive statistics in table


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The Good 3That paper using repeated measures ANOVA again

• Salmon et al (2006)

– test results in text

– the F-test results were not typeset properly!

– “F(2,23)=54.615, p0.001” or “F2,23=54.615, p0.001”


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6.4 Reporting multiple regressionSuggestions – simplify Lang and Secic (1997, pp 114-119)

• You should report– how variables were initially

chosen and types of variables involved

– how variables were included in the models

• simultaneously, in pre-determined order, stepwise

– whether underlying assumptions were assessed

• linearity; Normality & equality of variance for residuals; no multi-collinearity

– coefficient of multiple determination R2

• % of variation in dependent variable explained by model

– overall test of goodness-of-fit

• analysis of variance F-test results

– a table of estimated coefficients with 95% confidence intervals and p-values of t-test

• give results for every variable in the model, not just those that are significant


– standard errors of coefficients

– estimated regression equation


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The Good 1A paper using stepwise regression

• Perry and McLaren (2004)

– An exploration of nutrition and eating disabilities n relation to quality of life at 6 months post-stroke, HSCC 12(4), 288-297

– methods used


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The Good 2That paper using stepwise regression • Perry and McLaren (2004)

– stepwise selection and final model


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The Good 3That paper using stepwise regression again

• Perry and McLaren (2004)– the final

model explained


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The Good 4An alternative way of presenting results• Roelands et al (2005)

– Knowing the diagnosis and counselling the relatives of a person with dementia: the perspective of home nurses and home care workers in Belgium, HSCC 13(2), 112-124

– final models presented another way


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6.5 Reporting logistic regressionSuggestions – simplify Lang and Secic (1997, pp 122-125)


– how variables were initially chosen and types of variables involved




• change in -2 log likelihood, Hosmer & Lemeshow test

– a table of estimated odds ratios with 95% confidence intervals and p-values of Wald or t-test


• You could also report– whether and how

underlying assumptions were assessed

• no multicollinearity

– estimated coefficients and standard errors

– Nagelkerke R2

• measure of variation in dependent explained by model


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The Good 1A paper with a pragmatic way of selecting variables

• Peters et al (2004)

– Factors associated with variations in older people’s use of community-based continence services, HSCC 12(1), 53-62

– methods used


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The Good 2The paper with a pragmatic way of selecting variables

• Peters et al (2004)

– final results


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The Good 3Another way of presenting results

• Darton (2004) – What types of home are closing? The characteristics of

homes which closed between 1996 and 2001, HSCC 12(3), 254-264

– more detailed results


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6.6 Reporting survival analysis 1Suggestions – simplify Lang and Secic (1997, pp 137-146)

• Kaplan-Meier analysis


– nature and extent of the censoring

– survival rates with confidence intervals for each group

• percentage surviving at given time

– median survival time with confidence interval for each group

– Kaplan-Meier curves for each group plotting percentage survival (y) by time (x)

– comparison of survival curves by group

• log-rank (Cox-Mantel) test or Breslow-Wilcoxon test

• You could alternatively report

– life-table analysis

• table of events recorded by time interval


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3.6 Reporting survival analysis 2Suggestions – simplify Lang and Secic (1997, pp 137-146)

• Cox regression

• You should report– how variables were

initially chosen and types of variables involved




• likelihood ratio test

– how underlying assumption of proportional hazards was assessed

– a table of estimated hazard or risk ratios with 95% confidence intervals and p-values of Wald test


• You could also report– estimated coefficients and

standard errors


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The Good 1A rare survival analysis example

• Trappes-Lomax et al (2006)– Buying Time I: a prospective, controlled trial of a joint

health/social care residential rehabilitation unit for older people on discharge from hospital, HSCC 14(1), 49-62

– methods used


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The Good 2Survival analysis

• Trappes-Lomax et al (2006)– sample size


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• Trappes-Lomax et al (2006)

– Kaplan-Meier survival plot


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• Trappes-Lomax et al (2006)

– Cox regression results

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Statistical presentation in international scientific publications 6. Reporting more complicated findings Malcolm Campbell Lecturer in Statistics, School