1. Putting Your Research Ideas Into Practice
FH Health Research Intelligence Unit

2. 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.

4. 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

5. 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

6. 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

7. 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

8. 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) 

9. 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

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

11. 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.

12. 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

13. Types of Health Research
Biomedical Research
Clinical Research
Health Services/Systems Research
Population Health Research

14. 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.

15. 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

16. 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.

17. 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.

18. 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

19. 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

20. 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.

21. 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.

22. 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)

23. 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

24. 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)

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

26. 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

27. 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)

28. 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)

29. 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)

30. 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

31. 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

32. 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.

33. 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

34. 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.

35. 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

36. Clear, Testable Problem/Hypothesis
Methodological Considerations
Objectivity/Bias
Definitions
Reliability
Validity
Assumptions
Limitations
Confounds

37. 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

38. Feasibility of Work Plan
Time
Human Resources
Technology
Money

39. Basic Statistics for Putting your Research Ideas into Practice

40. 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

41. 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?

42. 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.

43. 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.

44. 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

45. 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

46. 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.

47. 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?

48. 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

49. 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.

50. Parametric vs. Non-Parametric
Some Advanced Statistics...
Some examples of parametric and non-parametric tests.

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

52. 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.

53. 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.

54. 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

55. 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*

56. Break

57. 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.

58. 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!

59. 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

60. Presentation of Group Exercise

61. 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.

62. Research is fun…who knew?
Questions?

64. 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.