Download - Educational Research 101: How to Manage Your Data and Prepare for the Statistical Consultation
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Educational Research 101:How to Manage Your Data and Prepare
for the Statistical Consultation
Francis S. Nuthalapaty, MDH. Lee Higdon III, PhD
2009 APGO Faculty Development Seminar
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Case Study: The wrong way
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Case Study: The wrong way
• Statistician was consulted after the data had
been collected.
• Study question was not clearly defined.
• Variables were not defined.
• Data Dictionary was not developed.
• Data were not cleaned/validated.
• Result: a statistician that is asked to perform a
miracle!
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Case Study: Lesson
Arrangements to consult with a statistician should be made before you start enrolling and collecting
data on patients! In fact, they should be made before protocol development to prevent issues
downstream.
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Learning Objectives
1. Describe the continuum of data management
2. List data collection instruments / approaches
3. Understand how to create a data dictionary
4. Describe methods to validate data
5. Describe various data analytic tools
6. Describe how to decide on statistical
approaches
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Question
Where does data management fit into the
research process?
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The Research Process
1. Question2. Literature search3. Objective / Hypothesis4. Study design5. IRB6. Study conduct7. Data analysis8. Dissemination of results
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Data Management Pearl
“No study is better than the quality of its data”
- Friedman
“…get it right the first time”- Crerand
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Analysis
Steps in Data Management
• Definition
• Acquisition
• Data Entry
• Validation
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Data Definitions
• Identifying your data
• Identifying your data types
• Naming your data variables
• Creating a data dictionary
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Data Types
Types of Variables
Qualitative Quantitative
Nominal
Ordinal
Interval
Ratio
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Data Definition Exercise
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Data Variable Names
• Make the name descriptive (easier to remember)
• Keep it short (less than 10 characters)
• User lower case
• Avoid spaces – use “underscore”
• Use numbers to indicate sequences
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Data Variable Formats
• Variable formats:
– Numeric
– String
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Data Variable Values
• Possible responses for a variable
– Numeric format:
• 0 = no / 1 = yes
– String format:
• a = no / b = yes
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Data Variable Values
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Note on Missing Values
• What about variables with no response?
– Leave it blank
– Assign a period “.”
– Assign a value (usually out of the expected
response range)
– Avoid text
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Data Naming Exercise
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Data Dictionaries / Code Books
• Brings together all data elements:
– Data types / formats
– Variable names
– Expected response values (range)
– Comments
• Self-generated vs. computer generated
• “Rosetta Stone” for the database
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Data Dictionary Exercise
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Data Acquisition
Pick the best method for the environment
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Data Acquisition Methods
• Interviews
• Questionnaires
• Assessments
– MCQ examinations
– OSCE / OSAT
• Laboratory studies
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Data Acquisition Environments
• Observational encounters
• Structured research encounters
• Self-report
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Data Acquisition Problems
• Major types of data issues:
– Missing data
– Incorrect data
– Excess variability
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Data Acquisition Problems
• Reasons for poor data quality:
– Researcher-dependent data:
• Insufficient time
• Inadequate training
• Lack of focus on study tasks
• Poor communication
• Protocol deviation
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Data Acquisition Problems
• Reasons for poor data quality:
– Subject-dependent data:
• Inadequate instruction
• Poor comprehension
• Sensitive or stigmatized behaviors
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Data Acquisition Options
• Paper forms
• Direct entry
• Computer assisted data acquisition
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Data Acquisition: Paper Forms
Advantages• Controlled
distribution and return
• Comments• Double data entry
Disadvantages• Anonymity• Manual quality
checks• Data entry time /
errors
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Data Acquisition: Direct Entry
• Options:– MS Excel, MS Access– Epi Info – free on the web– Direct entry into statistical software
• Pros / Cons:– No data transcription– Errors
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Data Acquisition
• Computer assisted data acquisition:
– Automated data collection
– OCR forms
– Computer-based case report forms /
questionnaires
– Computer-assisted self-interviews
– Mobile computing device diaries
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Data Acquisition: CASI
• Special Focus: Health Behaviors
– Factors which may affect reporting:
• Sensitive or stigmatized behaviors
• Age discrepancy between participant and
interviewer
• Lack of privacy
• Lack of comprehension of self-administered
questionnaires
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Data Acquisition: CASI
• Computer-assisted self-interview (CASI):
– Computer-based interview
– Can incorporate audio, video, and text
– Respondent listens to or reads questions on
screen
– Submits answers through keypad or touch
screen
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Data Acquisition: CASI
• Benefits of CASI:
– Interview conducted in privacy
– Standardized interview
– Computer controlled branching
– Automated consistency and range checking
– Multilingual administration
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Analysis
Steps in Data Management
• Definition
• Acquisition
• Data Entry
• Validation
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Data Validation
1. Is all of the data present?
2. Are the responses within the expected
range?
3. Does the data make sense?
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Data Validation
• Is all of the data present?
– Visually examine the data cells
– Frequencies
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Data Validation
• Are the responses within the expected
range?
– Frequencies
• Maximum / minimum values
– Descriptive statistics
• Means
• Standard deviations
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Data Validation
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Once the outlier is found, one can reference the chart for clarification
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Descriptive Statistics
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Data Distribution
Definitions by SPSS 16.0
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Data Distribution
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Data Distribution
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Scatterplots
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Who is Represented in the Data?
• Sample test of proportions– Percent of gender– Percent of ethnicity
• Sample test of means – Age– BMI
• Does our data reflect the population at large or a subset?
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Who is not?
• Compare data of the included and excluded individuals– Are they similar for:
• Age (continuous – Student t test)• BMI (continuous – Student t test)• Ethnicity (discrete/categorical – Chi-square test)• Gender (discrete/categorical – Chi-square test)
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Analysis
Steps in Data Management
• Definition
• Acquisition
• Data Entry
• Validation
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Data Analysis
• Choose the right tool for the job
• Commonly used statistical tests:
– If the data are normally distributed (i.e. bell-shaped curve) then we use parametric statistical test
– If the data are (1) not “bell-shaped”, or (2) have small sample sizes, generally less than 30 per group or (3) contain “outliners”, then we use nonparametric statistical tests.
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• Choice of statistical tests is used on:– Distribution of the sample data– Sample size– Number of groups– Independence of the groups
Comparison Measurement
Normal Distributi
on
# of groups
Statistical Test
Mean (Average) Yes 2 Student’s t-test
Mean (Average) Yes ≥3 Analysis of Variance
Median No 2 Wilcoxon Rank-Sum or Mann-Whitney U-test
Median No ≥3 Kruskal-Wallis test
Proportions Yes ≥2 Chi-square test
Proportions No ≥2 Fisher’s exact test
Data Analysis
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• Univariate vs. Multivariate– Multivariate methods are being required more frequently in
medical research because we are looking at relationships that involve more than one-to-one association.
• Multivariate methods allow us to:– Examine many variables simultaneously– Adjust for baseline differences between groups– Adjust for potential “confounding” variables– Obtain “adjusted” measures of effect
• Examples of multivariate methods: (Explain or predict the independent variables)– Linear regression – to predict the values of a numerical measurement (viral load)– Logistic regression – to predict a dichotomous outcome (pregnant/not pregnant)– Cox proportional hazard – to predict time to an event (survival time)
Data Analysis
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Session content, including narrated MS Powerpoint slides available at:
http://www.obgynknowledgebank.net