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**Putting Your Research Ideas Into Practice**

FH Health Research Intelligence Unit

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Who we are: Dr. Peter Hill ( ), Vice-President, Academic, Research, and Clinical System Redesign. Susan Chunick ( ), Director, Research Administration and Development (RAD). Rosa Haywood ( ), RAD administrative assistant. Michael Wasdell ( ), Grant Facilitator-Writer. Rae Spiwak ( ), Epidemiologist.

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**RESEARCH ETHICS BOARD STATUS REPORT 01 October 2007**

Total Studies 388 (From 2005 September 01 to Date) ACTIVE STUDIES = PENDING APPROVAL = 18 Active Studies by Department Area Addiction Services 1 Finance Neurology 3 Pharmacy 17 Administration Food & Nutrition Nursing 5 Prevention/Promotion Anaesthesia Health Services 2 Nutrition Professional Practice 4 Cardiology 27 Home Health Obstetrics 6 Psychiatry 18 Community Site Infection Control Occupational Therapy Public Health Prevention Critical Care (ICU) 13 Information Management Oncology Residency Facility Decision Support Service Internal Medicine Orthopaedics 15 Respiratory Diversity Services Laboratory-Cytology Palliative Care Social Work Elder Research Medicine Pathology Surgery Emergency 7 Mental Health Paediatrics Systems Analysis/Performance Ethics Multiple Sclerosis 8 People Service Workplace Health Family Medicine Nephrology Perinatal Non FH Research

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**FH Health Research Intelligence Unit How can we help?**

Grant Facilitator-Writer Conducting a search for funding opportunities Automatic notification of new funding sources and deadlines Identifying a research team Preparing letters of intent Identifying resources required for conducting research Formulating the research budget Writing the grant application in collaboration with researchers Understanding FH and funding agency requirements regarding preparation of specific documents

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**FH Health Research Intelligence Unit How can we help?**

Epidemiologist Specifying the research goal, objectives and hypothesis Identifying measurable outcomes Specifying the variables for analysis Identifying sources of data Developing data collection tools for quantitative or qualitative studies Developing the statistical analysis plan Analyzing the data Understanding how to use statistical software, such as SPSS

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**Workshop Activities What is research? Research process**

Introductory statistics for practical use Assignment of work group Group work: Practical Exercise for identifying: Research Goals Statistical Tests Levels of Data Sharing of group work

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What is research? “….the systematic process of collecting and analyzing information (data) in order to increase our understanding of the phenomenon about which we are concerned or interested.” (Leedy and Ormrod, 2001)

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**Research Types CONCLUSION-ORIENTED RESEARCH Epidemiological**

Experimental Effectiveness/Evaluation Theory Generation Health Services DECISION-ORIENTED RESEARCH Technical Analyses – systematic reviews Policy Analysis & Research Evaluation Research

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Research Uses 29% of total epidemiological consults involve decision-oriented research

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**Fraser Health Research Involving Human Subjects**

Research involving human subjects is defined as any systematic investigation (including pilot studies, exploratory studies, and academic course work assignments) designed to contribute to generalizable knowledge. Generalizable knowledge consists of facts, theories, principles or relationships, or the accumulation of information on which they are based, that can be corroborated by accepted scientific methods of observation and inference.

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**Research Characteristics**

1. Originates with question/problem 2. Guided by the hypothesis, question or problem 3. Follows a specific plan or procedure. 4. Accepts certain critical assumptions 5. Requires collection and interpretation of data 6. Cyclical in nature

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**Types of Health Research**

Biomedical Research Clinical Research Health Services/Systems Research Population Health Research

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**Health Research Biomedical Research**

To understand normal and abnormal functioning at the molecular, cellular, organ system and whole body levels. Includes the development of tools and techniques and new therapies or devices that can improve health and quality of life up to the point where they are tested on human subjects. Does not have a diagnostic or therapeutic orientation. i.e. Looking at spontaneous mutations in cells.

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**Health Research Clinical Research**

Targets improving the diagnosis and treatment of disease and injury. Focus on the health and quality of life of individuals. Includes research on animal models of human disease, clinical trials and other therapeutic interventions. i.e. Testing the effectiveness of a new medication

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**Health Research Health Services/Systems Research**

Multidisciplinary field Aims to improve the efficiency and effectiveness of health professionals and the health care system. Interventions at the level of practice and policy. i.e. Evaluating a new intervention for improving patient flow.

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**Health Research Population Health Research**

Studies the impact of social and environmental factors on the health of populations/subpopulations. May examine social, cultural, environmental, occupational, and economic factors that determine health status. Research data is used to identify areas where the health of a population can be improved. i.e. Statistics Canada Community Health Survey.

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**Research is a process 1. Generate idea 2. Conduct literature review**

3. Refine research question 4. Plan research methodology 5. Create research proposal 6. Apply for funding 7. Apply for ethics approval 8. Collect and analyze data 9. Draw conclusions and relate findings

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**Conducting Research Evaluation Guidelines**

Significance and relevance to health Knowledge of the field (cited literature) Clear, testable hypothesis or central research problem Originality and innovation in concept or approach Feasibility of work plan

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**Significance and Relevance to Health**

Research begins with the identification of a problem/knowledge gap and formulation of a research question. Identifying this problem can be the hardest part of research. The problem or question does not have to be complex. Where to obtain a research idea Experience in your area of specialty. Knowledge of the relevant literature and issues. Practice guidelines. Journal editorials and review articles.

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**Significance and Relevance to Health**

Good research proposals should: Address an important question. State the problem clearly and completely. Advance knowledge. How do you know if your idea is good? Talk to your peers and other experts in your field. Obtain an outside opinion. Look to the literature to see if it has already been studied.

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**Knowledge of the Field Sources of Research Literature**

Journal articles – PUBMED, MEDLINE, EMBASE, etc Conference papers - Conference Proceedings Dissertations - Dissertation Abstracts Gray literature (reports, working papers, government documents)

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**Knowledge of the Field What to look for Systematic Reviews**

An approach to summarizing the medical literature where the methods undertaken to conduct the search are reported so that it may be replicated and updated. Meta-analysis A review that uses quantitative methods to summarize the results. Review Articles Original Articles

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Clear, Testable Problem/Hypothesis Factors to consider when developing an hypothesis or statement of the research question: Phase Theory Building (creating new theory) Testing (testing a question or hypothesis) Theory Extension (adding to existing theory)

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Clear, Testable Problem/Hypothesis Factors to consider when developing an hypothesis or statement of the research question: Purpose Exploration Description Explanation Prediction Hypothesis Testing

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Clear, Testable Problem/Hypothesis Factors to consider when developing an hypothesis or statement of the research question: Unit of Analysis Individual Dyad Group Organizational Unit Industry Segment / Sector Community Society

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Clear, Testable Problem/Hypothesis Factors to consider when developing an hypothesis or statement of the research question: Definition Population (entire group) Sampling Frame (how your sample will be selected) Sample (sample of entire group)

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Clear, Testable Problem/Hypothesis Factors to consider when developing an hypothesis or statement of the research question: Time Horizon Cross- Sectional (snap-shot) Longitudinal (over time) Retrospective (past tense)

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Clear, Testable Problem/Hypothesis Factors to consider when developing an hypothesis or statement of the research question: Study Setting Natural (i.e. Participant observation in natural setting) Contrived (i.e. artificial setting) Researcher Influence (i.e. Randomized Control Trial)

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Clear, Testable Problem/Hypothesis Factors to consider when developing an hypothesis or statement of the research question: Research Design – depends on the research question Descriptive Analytic Experimental Quasi-Experimental Qualitative

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**Methodology Comparison**

Quantitative Explanation, prediction Test theories Known variables Larger sample Standardized instruments Deductive Qualitative Explanation, description Build theories Unknown variables Smaller sample Observations, interviews Inductive

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**Classification of Research Studies**

Observational Studies: Descriptive Studies: Focus on describing populations and describing the relationship between variables Analytic Studies: Make inferences about the population based on a random sample. Experimental Studies: Test relationships between exposures and outcomes. Investigator has direct control over study condition and exposure status.

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**Type of study is selected according to the purpose of research.**

Hierarchy of Studies Type of study is selected according to the purpose of research. If you want to control the status of an exposure or intervention, in order to examine causality or effectiveness, choose an experimental study. If you want to make inferences about a population based on a sample, or look at the association between variables, choose an analytic study. If you want to describe a population, choose a descriptive study INFORMATION

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**What about Quasi-Experimental?**

Quasi=Almost Lacks random assignment Many types: Pretest Posttest Nonequivalent Group- Both a control group and an experimental group are compared. But, groups are chosen and assigned out of convenience (rather than randomization). Ex. Examining two groups of students. One group signs up for an educational program, one group does not. Would measure all of the students’ grades prior to the start of the program and then again after the program. Those students who participated would be our treatment group; those who did not would be our control group.

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**Clear, Testable Problem/Hypothesis**

Method: Subjects/Patients/Units of Measure Sample size and justification (power, statistical tests) Inclusion criteria (who will you include?) Exclusion criteria (who will you exclude?) Procedure Detailed account of how the research design is executed (methods) Provides information on timelines and measures Sufficient information so that it can be replicated Analysis Statistical analysis is appropriate for the research design and will answer the research question

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**Clear, Testable Problem/Hypothesis**

Methodological Considerations Objectivity/Bias Definitions Reliability Validity Assumptions Limitations Confounds

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**Originality and Innovation in Concept or Approach**

Open up a new area Provide a unifying framework Resolve a long-standing question Thoroughly explore an area Challenge existing knowledge Experimentally validate a theory Produce an efficient system Provide needed empirical data Derive superior algorithms Develop new methodology Develop a new tool

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**Feasibility of Work Plan**

Time Human Resources Technology Money

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**Basic Statistics for Putting your Research Ideas into Practice**

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What is statistics? The collecting, summarizing, and analyzing of data. The term also refers to raw numbers, or “stats”, and to the summarization of data. Example: Frequencies

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**Research begins when there is a question.**

Different kinds of questions: Descriptive: How many men work at Fraser Health? How many hours a week do employees spend at their desks? Inferential: Does having a science degree help students learn statistical concepts? What risk factors most predict heart disease?

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Types of Statistics Descriptive Statistics: describe the relationship between variables. E.g. Frequencies, means, standard deviation Inferential Statistics: make inferences about the population, based on a random sample.

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Types of Variables Variables can be classified as independent or dependent. An independent variable is the variable that you believe will influence your outcome measure. A dependent variable is the variable that is dependent on or influenced by the independent variable(s). A dependent variable may also be the variable you are trying to predict.

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**Example A researcher wants to study the effect of Vitamin C on cancer.**

Vitamin C would be the independent variable because it is hypothesized that it will have an effect on cancer, and cancer would be the dependent variable because it is the variable that may be influenced by Vitamin C . Independent Variable Direction of Effect Dependent Variable Vitamin C Increase or Cancer decrease of certain effect

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Variables In research, the characteristic or phenomenon that can be measured or classified is called a variable. There are 3 levels of variables: Nominal Ordinal Continuous/Scale

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**Levels of Data Nominal= categorical**

E.g. Apples and pears, gender, eye colour, ethnicity. Data that is classified into categories and cannot be arranged in any particular order. Nominal=Categorical=Dichotomous Ordinal= data ordered, but distance between intervals not always equal. E.g. Low, middle and high income, or rating a brand of soft drink on a scale of 1-5. Continuous/Scale= equal distance between each interval. Also called interval.

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**Statistical Test Selection**

Selecting the appropriate statistical test requires several steps. Test selection should be based on: What is your goal: Description? Comparison? Prediction? Quantify association? Prove effectiveness? Prove causality? What kind of data have you collected? What are the levels of data (Nominal, ordinal, or continuous)? Was your sample randomly selected? Is your data normally distributed? Should you use a parametric or non-parametric test? What are the assumptions of the statistical test you would like to use? Does the data meet these assumptions?

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**Inferential Statistics**

Inferential statistics generally require that data come from a random sample. In a random sample each person/object/item of the population has an equal chance of being chosen. Various methods of randomization Excel

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Parametric Tests Parametric tests assume that the variable in question is from a normal distribution. Non-parametric tests do not require the assumption of normality. Most non-parametric tests do not require an interval level of measurement; can be used with nominal/ordinal level data.

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**Parametric vs. Non-Parametric**

Some Advanced Statistics... Some examples of parametric and non-parametric tests.

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Non-Parametric Tests Use when all assumptions of parametric statistics cannot be met. Can be used with data that are not normally distributed.

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**Assumptions There are various assumptions for each test.**

Before you select a test, be sure to check the assumptions of each test. You will need to contact a consultant, or review statistical/research methods resources to find this information. Some examples of common assumptions are: The dependent variable will need to be measured on a certain level (i.e. Interval level). The independent variable(s) will need to be measured on a certain level (i.e. Ordinal level). The population is normally distributed (not skewed). If your data do not meet the assumptions for a specific test, you may be able to use a non-parametric test instead.

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Sample Size There are several rules of thumb for determining sample size. 1) It’s a good idea to have a minimum of 30 cases (as a total group, or if comparing groups, 30 for each group). If you have less you can use a non-parametric test, but it is still better to have close to 30 cases. 2) If using regression, it is best to have between cases per independent variable. 3) If you are validating a survey, it is never good to have more questions than cases. 4) If the total population that you are examining is less than 30. Use all of them. 5) For pilot studies the recommendation is a sample size of 12 per group 6) With a sample size of 400 per group, you can do just about anything. 7) For surveys, a 30% response rate is the bare minimum. Note: For a precise sample size estimate you will need to conduct a power analysis.

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**Statistical Decision Making Tree**

Type of Data Goal Measurement Normal Population Ordinal, or Non-Normal Population Binomial -Two Possible Outcomes Survival Time Describe one group Mean, SD Median, interquartile range Proportion Kaplan Meier survival curve Compare one group to a hypothetical value One-sample t test Wilcoxon test Chi-square or Binomial test ** Compare two unpaired groups Unpaired t test Mann-Whitney test Fisher's test (chi-square for large samples) Log-rank test or Mantel-Haenszel* Compare two paired groups Paired t test McNemar's test Conditional proportional hazards regression* Compare three or more unmatched groups One-way ANOVA Kruskal-Wallis test Chi-square test Cox proportional hazard regression** Compare three or more matched groups Repeated-measures ANOVA Friedman test Cochrane Q** Conditional proportional hazards regression** Quantify association between two variables Pearson correlation Spearman correlation Contingency coefficients** Predict value from another measured variable Simple linear regression or Nonlinear regression Nonparametric regression** Simple logistic regression* Cox proportional hazard regression* Predict value from several measured or binomial variables Multiple linear regression* or Multiple nonlinear regression** Multiple logistic regression* Statistical Decision Making Tree

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Type of Data Goal Measurement Normal Population Rank, Score, or Measurement Non-Normal Population Binomial -Two Possible Outcomes Survival Time Compare three or more matched groups Repeated-measures ANOVA Friedman test Cochrane Q** Conditional proportional hazards regression** Quantify association between two variables Pearson correlation Spearman correlation Contingency coefficients** Predict value from another measured variable Simple linear regression or Nonlinear regression Nonparametric regression** Simple logistic regression* Cox proportional hazard regression* Predict value from several measured or binomial variables Multiple linear regression* or Multiple nonlinear regression** Multiple logistic regression*

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Break

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**During the group exercise…**

Steps to choose the appropriate statistical method for the data analysis: 1. Identify whether the research problem raises the question of describe, relate (association), or compare (difference). 2. Identify the levels of measurement in the research question (Nominal/Categorical, Ordinal/Rank, Continuous/Evenly spaced). 3. Identify the number of variables, or samples being described, related, or compared. 4. Identify whether comparison samples are related (analyze same group before and after) or independent (not at all related, looking at different groups). 5. Choose the appropriate statistical tool for the data and situation using the decision tree in the handout.

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**WORK GROUP ASSIGNMENT Now it’s time to meet your research team!**

You will be assigned to groups. Get ready to meet your Co-investigators!

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1. A pilot experiment designed to test the effectiveness of a new approach to electrode placement for Electro Shock Therapy (ECT) has been conducted over a one year time period in the Fraser Health Authority. Patients from two different mood disorder clinics participated in this study. Patients from Clinic X received ECT therapy according to current practice guidelines. Patients from Clinic Y received a new exploratory ECT treatment. Patients in each clinic were matched for age, gender, and type of disorder. A random sample of 30 matched pairs of patients were selected for inclusion in the study. At end of one year, patients were administered a memory test yielding a total score out of 100. Dr. Vasdil would like to know what statistical procedure needs to be selected to test for differences among groups of patients on the memory test. What is the question: Compare How many samples: 2 Related or independent: Independent What is the level of measurement: Continuous How many dependent variables: 1 Test: T-test

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**Presentation of Group Exercise**

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**BONUS: Can you really sing in statistics? Prove it!**

What category of statistical test could you use if you have a sample of less than 30 people? What is the recommended sample size for pilot studies? Quantitative research is deductive, qualitative research is ________. Reliability, validity, and objectivity are all important ________ considerations.

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**Research is fun…who knew?**

Questions?

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Please see the FH Research Administration and Development research education calendar for more upcoming events. Online Workshop Evaluation Forms will be sent shortly after the workshop. Please take the time to complete this short online survey.

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