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NIH and IRB Purpose and Method

NIH and IRB Purpose and Method. M.Ed. 6085 Session 2. NIH Certification. The National Institute of Health certifies researchers to ensure they understand protections dealing with human subjects.

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NIH and IRB Purpose and Method

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  1. NIH and IRBPurpose and Method M.Ed. 6085 Session 2

  2. NIH Certification • The National Institute of Health certifies researchers to ensure they understand protections dealing with human subjects. • You must complete your NIH certification prior to beginning your project (and as a part of this class). • http://faculty.weber.edu/kristinhadley/med6085

  3. Institutional Review Board for use of Human Subjects in Research • http://departments.weber.edu/meduc/irb/default.htm • IRB application • http://departments.weber.edu/meduc/irb/hsr_appl.rtf • Once you have a signed proposal, complete the IRB application. Give a hard copy to Dr. Gowans and email her an electronic copy. • You must include a copy of your signed proposal title page and a copy of your NIH Certification with your hard copy IRB application.

  4. Literature Review Rubric • http://faculty.weber.edu/kristinhadley/med6085/ • DOI Lookup site • http://www.crossref.org/guestquery/

  5. NATURE OF THE PROBLEM Problem Statement:Premise I, Premise II, Interaction problem (III)Literature Review: supporting details Premise I - details, analyze, synthesize Premise II – details, analyze, synthesize Problem (III) that arises due to I & II Previous work to address III – include method, instrumentsWeaknesses or “holes” in literatureSummary:bring it all together and lead reader to the need for the study THE MASTER’S PROJECT PROPOSAL PURPOSE Restate problem The purpose of this research is . . . The specific objectives are 1. . . . 2 . . . METHOD How will you accomplish the objectives stated in the purpose through your study?ParticipantsInstrumentation Procedure Data Analysis

  6. Purpose • In the introductory paragraph, briefly review the problem or issue • State the purpose of the proposed study • May be broken down into objectives which could be stated as questions or intended outcomes OR • Could be written as hypotheses • Example: • The purpose of this study is to . . . . Specifically, the research will answer the following questions: 1. How does increased . . . . impact student achievement in 6th grade social studies? 2. Does increased . . . .. help students feel more connected in the classroom? • Should be no more than 1, sometimes 2 pages

  7. Purpose activity • Purpose activity

  8. Method • In the introductory paragraph, discuss the study type and how it will meet the purpose of the study. • Identify the major tasks that will be completed in order to achieve the objectives stated in the purpose. Under each category, provide a detailed description of the tasks. Tasks could include • Identification of participants - specific • Description of measurement instruments • Step by step procedures – specific • Data analysis plan – to answer research questions DETAILS !!! SPECIFIC ! SPECIFIC !!! DETAILS !!

  9. Data Analysis Plan • This subsection describes how the data will be analyzed for your project • Quantitative: statistical analysis • Qualitative: how will you present the findings? Your data analysis plan should enable you to answer your research question

  10. Statistics Review (Quantitative Studies) • Descriptive statistics: describing an outcome with numbers • Measures of Central Tendency • Mean: the average ( X ) • Mode: the most common • Median: the middle number when the data is put in order from least to greatest • When should you use which measure?

  11. More Descriptive Statistics • Measures of Variability • Standard Deviation (SD): a measure of how spread out the data are; roughly, the average of how far each data point is from the mean • Range: difference between the lowest data point and the highest data point • Interquartile Range: rank order the data, split it in half and in half again, subtract the median of the bottom half from the median of the top half

  12. More Descriptive Statistics • Measures of Association • Correlation coefficient (r ) : a number between -1 and 1 that describes the relationship between two data sets • r=0 if there is no relationship • r=1 if there is a perfect positive relationship (as one goes up, the other goes up a perfectly predictable amount) • r=-1 if there is a perfect negative relationship (as one goes up, the other goes down a perfectly predictable amount) • Most correlation coefficients are somewhere in between • Square the correlation coefficient to show how much (%) of the second variable can be attributed to differences in the first variable. This is called the coefficient of determination (R2). Association does not mean Causation!

  13. Inferential Statistics • What is the probability that the difference found between these samples would have occurred if there was really no difference in the total populations?

  14. t-tests • What is the probability that the differences between TWO groups has occurred by chance alone? The way it is reported: t(49) = 1.34, p<.05 It is likely that there is a real difference Probability that this difference is due to chance alone Degrees of freedom (typically n-1) Value calculated by the t-test

  15. Analysis of Variance (ANOVA) • What is the probability that the differences between more than two groups has occurred by chance alone? The way it is reported: F(3,53) = 26.26, p<.001 Probability that this difference is due to chance alone (number of groups -1, roughly the number of subjects) Value calculated by the ANOVA

  16. Analysis of Variance (ANOVA) • ANOVA doesn’t indicate where the differences occur, just that there is a difference • Researchers must then pair the means to find the differences

  17. Analysis of Covariance (ANCOVA) • Like ANOVA but some covariate (something that is in common between the two groups) is statistically held constant when the comparison is calculated. • For example: comparing the achievement level of different schools with SES held constant

  18. Chi-Square • Comparisons when data can’t be averaged • Nonparametric: without assumptions about the shape of the data distribution The way it is reported: Χ2 (2, N=120) = 12.39, p=.002 (number of groups -1, number of subjects) Probability that this difference is due to chance alone Value calculated by the statistic

  19. Regression Analysis • Method used to develop a predictive equation based on the relationship between two variables • Multiple regression is when two or more variables are used to predict another variable using an equation • Confidence interval: accuracy band around the predicted scores.

  20. Statistical Significance When a difference is found that appears unlikely to have occurred by chance, that difference is identified as being statistically significant. It does not mean the difference is important, crucial, or practically significant. Effect size: a standard measure of the size of the difference Standardized mean difference effect size: difference between means divided by the standard deviation

  21. Data Analysis Plan • Think – how will the data enable me to answer my research question? • Evaluate the data in such a way that you can answer your question with confidence.

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