This table is also available as a separate document so you can down load it and print it off. Hopefully it is a simple way for you to ‘look up’ what type of data you are working with, the number of groups involved and whether the data is paired or not. Paired data is when the two sets of data relate closely to each other and this kind of data needs different kinds of tests. An example of parried data is when it comes from the same person in an experiment. There may be of before and after treatment on the same person and the measure taken before the intervention is then compared to the measure take afterwards. It might not even be the same person but if you have matched individuals in experimental groups closely on things like age, gender, health conditions etc then you should also treat this as paired data. So firstly decide what type of data it is and look it up across the top row. Then decide how many groups are involved and whether it is paired data or not.
For example, I want to know if there is any difference between different methods of preventing pressure ulcers on patients heels so I set up a trial to measure the pressure underneath different pressure relieving devices. One is a gel pad, and the other is a ward made device by filling a latex glove with water to make a water based pad. I decide not to compare this to doing nothing as that might be quite negligent to do on real patients who are at risk. So I compare these two other ways of relieving pressure to the usual ward practice of using a a sheepskin pad. This then becomes the third type of intervention. The pressure measurements are made by a scientific device which measures quite accurately. The data then is ratio and I have 3 or more groups. The groups are not paired or matched in any way.
Which test do you think I should use to see if there is any statistical difference between the heel pressures ?
Compares between group variance to within group variance to give the F ratio
To conclude Use the table to help you make judgements when reviewing published papers and deciding what tests you should use in any research proposals. Be aware though that data should be normally distributed to use the interval or ratio level tests.
To conclude Use the table to help you make judgements when reviewing published papers and deciding what tests you should use in any research proposals. Be aware though that data should be normally distributed to use the interval or ratio level tests.
Quant2
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NURSING STUDIES MSc/Dip NURSINGNURSING STUDIES RESEARCH METHODS IN NURSING AND HEALTHCARE B QUANTITATIVE DATA ANALYSIS 2 Dr. Sheila Rodgers Nursing Studies University of Edinburgh
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NURSING STUDIES MSc/Dip NURSING Research BNURSING STUDIES This lecture aims to enable students: •to know which inferential tests can be applied to what kind of data •to be able to interpret inferential statistical analysis output
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MSc/Dip NURSING Research and Evaluation INFERENTIAL TESTSNURSING STUDIESNURSING STUDIES Nominal Ordinal lnterval / No grps Paired ratio X2 / Fisher’s Mann Whitney U test T - test 2 No exact test X2 / Fisher’s Kruskall Wallis test ANOVA 3+ No exact test McNemar test Wilcoxon signed ranks Paired T - 2 Yes test test Cochran’s Q Friedman test RANOVA 3+ Yes test Spearman’s rank Pearson’s r 2 variables Yes correlation co-effiecient
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NURSING STUDIESNURSING STUDIES MSc/Dip NURSING Research B
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MSc/Dip NURSING Research and Evaluation INFERENTIAL TESTSNURSING STUDIESNURSING STUDIES Nominal Ordinal lnterval / No grps Paired ratio X2 / Fisher’s Mann Whitney U test T - test 2 No exact test X2 / Fisher’s Kruskall Wallis test ANOVA 3+ No exact test McNemar test Wilcoxon signed ranks Paired T - 2 Yes test test Cochran’s Q Friedman test RANOVA 3+ Yes test Spearman’s rank Pearson’s r 2 variables Yes correlation co-effiecient
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NURSING STUDIES MSc/Dip NURSING Research BNURSING STUDIES Chi Square X2 Assumes: Random sampling Independent groups Expected frequency of each cell greater than 0 Expected frequency of at least 5 for at least 80% of cells
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NURSING STUDIESNURSING STUDIES MSc/Dip NURSING Research B
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NURSING STUDIESNURSING STUDIES MSc/Dip NURSING Research B
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NURSING STUDIES MSc/Dip NURSING Research BNURSING STUDIES Fisher’s Exact test – when the expected frequency of a cell is <5 McNemar test for paired or matched data with two groups Cochran’s Q test for paired or matched data with 3+ groups
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NURSING STUDIES MSc/Dip NURSING Research BNURSING STUDIES MANN WHITNEY U-TEST Assumes: Random sampling Independent groups Ordinal level data Compares the ranks of the scores from two groups to test if the distributions are the same or not.
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NURSING STUDIESNURSING STUDIES MSc/Dip NURSING Research B
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NURSING STUDIESNURSING STUDIES MSc/Dip NURSING Research B
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NURSING STUDIES MSc/Dip NURSING Research BNURSING STUDIES KRUSKAL – WALLIS TEST Makes the same assumptions as the MW test plus a minimum of 5 cases per group. Compares the ranks of the scores from 3+ groups to test of the distributions are the same or not. df= no of groups - 1
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NURSING STUDIES MSc/Dip NURSING Research B T-TESTNURSING STUDIES Assumes: •Random sampling •Normal distribution •Equal variance •At least interval measurement Compares the mean differences of the two groups
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NURSING STUDIESNURSING STUDIES MSc/Dip NURSING Research B
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NURSING STUDIES MSc/Dip NURSING Research BNURSING STUDIES ANALYSIS OF VARIANCE Assumes: • Random sampling • Normal distribution • Populations have equal variance • At least interval measurement • Groups are not matches or pairs • 3+ groups or independent variables
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MSc/Dip NURSING Research B A Water filled B Gel heel C sheepskinNURSING STUDIESNURSING STUDIES gloves pad heel cover 24 12 20 38 10 28 26 15 23 17 19 10 21 14 15 X=31.2 X=14.0 X=19.2
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MSc/Dip NURSING Research B CORRELATIONNURSING STUDIESNURSING STUDIES
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MSc/Dip NURSING Research B CORRELATIONNURSING STUDIESNURSING STUDIES
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MSc/Dip NURSING Research B CORRELATIONNURSING STUDIESNURSING STUDIES
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NURSING STUDIESNURSING STUDIES MSc/Dip NURSING Research B
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MSc/Dip NURSING Research B CORRELATIONNURSING STUDIES • Enables the prediction of Y on the basis of X.NURSING STUDIES • The coefficient gives the strength and magnitude of the relationship. • Coefficients range from 0 to + 1 and 0 to -1. • Scatter plots help determine the nature of relationships. • R 2 = proportion of the variance in one variable that can be explained by variability in the second variable (coefficient of
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MSc/Dip NURSING Research B CORRELATIONNURSING STUDIESNURSING STUDIES Pearsons r or Pearson product moment assumes; • both dependent and independent variables are at least interval or ratio scale • random sampling • variables are normally distributed • linear relationships df= no of cases - 2
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NURSING STUDIES MSc/Dip NURSING Research B SPEARMAN’S RANK ORDER CORRELATIONNURSING STUDIES CO-EFFICIENT • both dependent and independent variables are at least ordinal scale • random sampling • linear relationships df= no of cases - 2
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MSc/Dip NURSING Research and Evaluation INFERENTIAL TESTSNURSING STUDIESNURSING STUDIES Nominal Ordinal lnterval / No grps Paired ratio X2 / Fisher’s Mann Whitney U test T - test 2 No exact test X2 / Fisher’s Kruskall Wallis test ANOVA 3+ No exact test McNemar test Wilcoxon signed ranks Paired T - 2 Yes test test Cochran’s Q Friedman test RANOVA 3+ Yes test Spearman’s rank Pearson’s r 2 variables Yes correlation co-effiecient
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MSc/Dip NURSING Research and Evaluation University of Leicester Hospital NHS site has a short online module on statistics whichNURSING STUDIESNURSING STUDIES can be useful to look at. It also includes some information on medical statistics such as odds ratios. Follow the link below and look at the modules and look at the one on ‘Introduction to Statistics’. http://www.uhl-library.nhs.uk/training.html Principles of searching e-learning (10 mins) - an introduction to database searching Research methods - types of research methods and hierarchy of evidence Critical Reading Made Easy - an introduction to critical appraisal principles and tools Introduction to statistics - displaying, summarising and testing data
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