Analyzing Research Data Using Excel

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http://research.fraserhealth.ca/

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Objectives

To review key concepts and elements of quantitative research

To explore the application of Excel in conducting a research project:

Creating data files

Creating data dictionary

Linking research question to appropriate analysis

To apply statistics to analytical data

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Workshop Outline

09:00 - 09:15 Review of Quantitative Research09:15 - 09:30 Measurement09:30 - 10:30 Excel 101

Excel as database10:30 - 10:45 Break10:45 - 11:15 Using Excel to Clean/Explore Data 11:15 - 12:00 Using Excel to Analyze Data

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Excel Pop Quiz

How many columns?

2003: 256

2007 & 2010: 16,384

How many rows?

2003: 65,546

2007 & 2010: 1,048,576 1,048,576

True or False

You can conduct statistical analyses in Excel?

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Quick Review of Quantitative Research

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Framework for Quantitative Research

Conduct literature reviewDevelop rationale

Formulate research question

Generate objective(s) and/or hypothesis

Apply methods and conductthe study

Why do want to do this research?What do others say?What are knowledge gaps?

PICO MethodP = population / patientI = interventionC = comparisonO = outcome

Hypothesis(Usually) statement of anticipated

results

Objective“Action” statement

MeasurementStudy DesignAnalysis

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Research Question

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Measurement: “Thinking in Numbers”

IDID GenderGender AgeAge Disease Disease OutcomeOutcome

1 Male 59 Y

2 Female 52 Y

3 Male 53 N

4 Female 60 N

IDID GenderGender AgeAge Disease Disease OutcomeOutcome

1 1 59 1

2 2 52 1

3 1 53 0

4 2 60 0

From this To this

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Types of Variables

Independent variable (IV)

Influences your outcome measure

Active (intervention) or Attribute (characteristic)

Dependent variable (DV)

Influenced by the IV(s)

Usually represents outcome studied

Confounders

Alternative explanation for an association between an

exposure (IV) and an outcome (DV)

Not a focus of the study

Independently associated with the outcome

Associated with the exposure under study

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Level of MeasurementNominal

Example: gender

Data categorized into mutually exclusive and unordered groups

Can assign number codes but calculations would be meaningless (male=1; female=2)

Ordinal

Example: SES level: low, middle and high income

Data classified/categorized with implied order

Distance between data not always equal

Can't measure the magnitude or quantify the difference between data: how much lower is middle from higher income?

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Interval: attributes measured on interval scales

Equal distance between each interval

Distance between scale numbers has meaning

Arbitrary zero point (e.g., temperature)

Ratio: similar to interval scale

Has true zero point

Clear definition of 0: There is none of the variable

Example: weight, salary ($0=$0).

Can make assumptions about the ratio of two measurements – 6 grams is twice as much as 3 grams

Level of Measurement

Level of Measurement & Acceptable Statistical Operations

Nominal Ordinal Interval Ratio

Frequency distribution Yes Yes Yes Yes

Mode Yes Yes Yes Yes

Median & percentile No Yes Yes Yes

Mean & standard deviation No No Yes Yes

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Excel 101

Objectives

How to organize data in an MS Excel spreadsheet

How to define variables

How to code in preparation for analysis

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Terminologies

Data: Information that you collect

Dataset

Collection of data usually presented in tabular form

Columns represents variables

Rows represent members of the dataset

Spreadsheet

Computer application that facilitates use of datasets (enter data, analyses, sharing)

MS Excel is a spreadsheet program

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Using Excel for a Research Study

To capture data

Facilitate data collection, minimize entry errors

To clean/explore/describe data

Starting point for analyses is “cleaning” raw data

Preliminary descriptive statistics

To analyze data

Using program “add-ins”

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Stages in preparing data for analysis

Interpret results

Explore data

Create data file: Enter & clean data

Collect data

Analyze data

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Stages in preparing data for analysis

Good practice

Design your spreadsheet keeping your statistical analyses in mind Use logic check to clean data Create data dictionary Consult with analyst

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Creating data file using spreadsheet

Each variable (e.g., ID #) represented by a column

Each participant is represented by a row

All the information for a single case is entered across one row only

the data in each column summarizes information on a particular variable

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Creating Your SpreadsheetDefining variables

Use descriptive, unique, names for variables

Use underscore (_) instead of space

Be consistent in naming especially with array variables

Variables that capture a pattern

Example: measuring blood pressure at regular intervals (e.g., Q30 min for 12 hours)

BP_time1 BP_time2 BP_time3

BP_30min BP_1hr BP_final

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Creating Your SpreadsheetEach column captures only single piece of information

ID Intervention_Used1

2

3

Yes surgeryYes medication

No

ID Intervention_Used Intervention_Type123

SurgeryMedication

Not applicable

YesYesNo

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Creating Your SpreadsheetUse tools to facilitate entry: Add notes for data entry Insert Comments

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Creating Your SpreadsheetUse tools to facilitate entry: Colour-code columns Fill color icon

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Creating Your SpreadsheetFor numeric variables, use Format to “force” entry into specific form

Date

Number of decimals

Highlight entire column to ensure consistent format applied

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Data Dictionaries

AKA code books

A “centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format”

Develop data dictionary: Before collecting data

Components:

Variable name

Variable label

Type: nominal or interval

Values/coding for each variable

Always assign values for missing or non applicable cases

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Sample Data DictionaryData Dictionary for Research Consults Database

Item Variable Label Type Coding/ entry instruction

1 ID_no ID number Numeric Enter unique number for each 

record/patient (1….100)

2 Gender Gender of participant String Enter corresponding number:0= Female1= Male999= missing

3 Date_T1 Date at baseline  Date Specify format: mm/dd/yy

4 Education  Highest level of 

educationNumeric Enter corresponding number:

1= Grade 102= Grade 123= College Diploma4= University

Data Structures

Re-emphasize importance of keeping analyses in mind when designing dataset

Statistical packages require that data are entered in specific way in order to run analytical steps

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Using Excel to Clean / Explore Data

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Data Cleaning & Data Exploration

Data at this stage is raw dataset

Need to

Clean: any entry errors, duplicate entries

convert text variables into numeric variables

Explore: any outliers

Excel tools to facilitate cleaning and exploring

Filter

Sort

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Data Cleaning & Data Exploration

Sources of data errors: Missing: never leave blank

Assign a meaningful number for missing values

Consider coding for cases with non-applicable responses

Typing errors on data entry (e.g., age= 121) Measurement errors (e.g., height)

Identify errors: Descriptive statistics for each variable Minimum and maximum values Means, medians and SD

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Our “Dataset” for the Remainder of the Workshop

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Gout and AMI Study

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Gout and AMI 2 Study

Research Question

What clinical factors are associated with the development of AMI among elderly women with gout?

Objectives

To compare women with gout and no gout on clinical factors including age, BMI, uric acid level

To evaluate the correlation between clinical factors among elderly women with gout and without gout

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Gout and AMI 2 Dataset

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Gout and AMI 2 Study

Data dictionary (handout)

13 Variables

What are the continuous variables?

What are the categorical variables?

N = 200 subjects

Excel Tool: Filter

Place cursor over the block by A1

Data Filter AutoFilter (2003)

Data Sort & Filter Filter (2007)

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Exercise 1

You are the analyst for the Gout and AMI 2 study. A data dictionary was not implemented (GASP!) and there are entry errors.

Clean the dataset by locating and finding entry errors using tools in Excel.

Sample_worksheet_Exercise1.xls

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SummaryImportant data considerations while conducting the study

Designing the dataset

Collecting the data

Entering the data

Cleaning/exploring raw data

Applying statistics to analytical data

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Using Excel to Analyze Data

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Using Excel to Analyze Data

Analysis ToolPak: add-in to be installed in Excel

Supplemental program that adds custom commands

Descriptive statistics

Analytical statistics

T-tests

Correlation

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Getting Started

1. Click Tools Add-Ins

2. Select Analysis Tool Pack

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Statistics used to describe characteristics of study population/sample

Not used to infer the properties of the population from which the sample was drawn

For continuous variables

Measures of central tendency: mean, median, mode

Measures of variability (standard deviation)

Shape of Distribution (skewness, kurtosis)

Descriptive Statistics

Level of Measurement & Acceptable Statistical Operations

Nominal Ordinal Interval Ratio

Frequency distribution Yes Yes Yes Yes

Mode Yes Yes Yes Yes

Median & percentile No Yes Yes Yes

Mean & standard deviation No No Yes Yes

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Descriptive Statistics

1.Click Tools Data Analysis

2. Select Descriptive Statistics

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Example: Age

Input range

Select columns “$A:$E”

Group by columns

Click on Labels in 1st row

Output options

New worksheet

Summary statistics

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Output for Descriptive Statistics: Age

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Exercise 2

Data file = sample_worksheet_Masterfile.xls

Using Descriptive Statistics in Analysis ToolPak, obtain descriptive statistics for:

Uric Acid

BMI

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Descriptive Statistics by Group

Use Pivot Tables Useful to obtain descriptive statistics by group For example, if you wanted to know the average and standard deviation of BMI for men and women

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Pivot Tables

Click Data Pivot Table and Pivot Chart

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Example Age by Gout Diagnosis

Step 1

Where is data?

What kind of report?

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Step 2

Where is data you want to use? Drag pointer over entire dataset

Example Age by Gout Diagnosis

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Step 3

Where do you want to put the Pivot Table?

Layout

This is where you tell Excel which groups you want to output your results by and for what variables

Example Age by Gout Diagnosis

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Step 3 Layout

Row represents your grouping variable (Gout_Dx)

Column variable you want to

output according to groups

May need to drag several times for parameters needed

Example Age by Gout Diagnosis

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Example Age by Gout Diagnosis

No gout

Gout

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Exercise 3

Using Pivot Tables and Charts, obtain the mean and standard deviation for uric acid according to gout diagnosis

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Analytic Statistics

Statistical procedures used to draw conclusions about a population from sample data

Compare groups

T-tests

Evaluate correlation

Correlation coefficients

Evaluate association

Regression models

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Analytic Statistics: Considerations

Research question: Describe, compare or predict? Levels of data measurement: nominal, ordinal or

interval? Are you comparing same or different subjects? Number of experimental groups? Is your data normally distributed?What are the assumptions of the statistical test you

would like to use?

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Check Data Assumptions

What are the assumptions of the statistical test you would like to use?

Some common assumptions are:

The DV and IV will need to be measured on a certain level (e.g. continuous)

The population is normally distributed (not skewed)

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1. Research goal2. Identify ID and DV3. Describe level of the data4. Identify the # & group

pairing groups5. Check data assumptions

Type of Dependent Variable Data

Goal Continuous Normal

Ordinal Non-normal

Categorical

Describe one group

Mean, SD Median, interquartile range

Proportion

Compare one group to a hypothetical value

One-sample t test Wilcoxon test Chi-square

Compare two unpaired groups

Unpaired t test Mann-Whitney test Fisher's test (chi-square for large samples)

Compare two paired groups

Paired t test Wilcoxon test McNemar's test

Compare three or more unmatched groups

One-way ANOVA Kruskal-Wallis test Chi-square test

Compare three or more matched groups

Repeated-measures ANOVA

Friedman test Cochrane Q

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

Predict value from several measured or binomial variables

Multiple linear regressionor Multiple nonlinear regression

Multiple logistic regression

Selecting Appropriate Statistical Test

Statistical decision tree (handout)

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Exercise 4 (handout)

A pilot experiment designed to test the effectiveness of a new therapy to pain management for patients with chronic pain, conducted over a one year time period.

What is the goal? What is the IV?What is the DV? How many groups? Paired/matched or independent?What is the level of measurement?

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Comparing groups: Mean differences

Independent Samples T-Test Comparison of the means of 2 non-paired groups Differences in pain levels between 2 groups

(standard care and new intervention)

Paired Samples T-Test Comparison of means of 2 paired measures Differences in pain levels within groups Pre and post measurement, repeated measurement

under different conditions

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Comparing Groups

Sort dataset by variable you are comparing

Click Tools Data analysis

Three options for t-tests

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Is there a difference in age between gout and non-gout patients?

Highlight age data for first group (no gout)

Highlight age data for second group (gout)

Output to new worksheet

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Output

No gout Gout

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Paired Samples T-Test Procedure

Tools Data Analysis T-test: Paired Two Sample for Means Input 1 Range: DV at time 1 Input 2 Range: DV at time 2 Output Options

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Exercise 5

Using Descriptive Statistics in Analysis ToolPak, compare patients with no gout versus gout according to:

Uric Acid levels

BMI

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Exercise 6

Using Descriptive Statistics in Analysis ToolPak to answer the following question:Is there a difference in mean pain before and after surgery?

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Associate - Correlation

Allows an examination of the association between variables

Range: 0 to +1

Information about the strength of association

Information about the direction of the association (positive or negative)

A correlation coefficient of 0 =no relationship

A correlation coefficient of +1= positive relationship

A correlation coefficient of -1= negative relationship

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Evaluating Correlation

Continuous variables

Columns:side-by-side

Click Tools Data Analysis

Click Correlation

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What is Correlation Between Age and Uric Acid Level?

Highlight 2 columns

Group by columns

Labels in first row

Output to new worksheet

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Output

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Exercise 6

Using Descriptive Statistics in Analysis ToolPak, evaluate the correlation between:

Uric Acid and BMI

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Thank You!