CBR221 Introduction to Survey Data Analysis

with Excel

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Workshop Objectives

Use Excel to help you: – Organize data for analysis – Systematically work with data – Analyze data – Graphically display analysis

We’ll Examine

– Types of variables used in analysis – Types of measurement scales used in analysis – How to describe data with Frequency counts,

Descriptive Statistics, Histograms, and Pivot Tables – How to create charts

Survey

Questions:

1. Area of the city child lives in?

2. Number of colds child had last year?

3. Gender: Male_____ Female _____

4. Age

5. Describe cold symptoms __________

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Analysis

Helps describe, conclude, recommend

Systematic exploration for interpreting data

Survey Data

Answers can be in text or numbered formats

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Statistics

Systematic method of converting and analyzing data by using numbers

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Excel

Support tool for statistical methods

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Start Excel

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Moving from Model to Excel Data Analysis

• Assumption, hypothesis, or model • Collect data • Organize data for analysis

Example Model or Assumption

• “West area children get fewer colds than central area children.”

• Want proof• Analysis: Find mean (average) number of colds by area

When Data Fit Model

• Data cluster as expected • Findings support assumption or model

For 33 west area children,

the mean is 5 colds.

For 31 central area children,

the mean is 6 colds.

The results indicate west area children do have fewer colds than central area children.

Table Illustrates

Zone Participants n

Colds

West 33 5

Central 31 6

Total 64

Table 1. Colds by City Area

x

Graph Illustrates

Central

Mean Colds

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Figure 1. West area children had a lower mean number of colds than central area children.

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West

Pie Chart Illustrates

West, 5Central, 6

Figure 1. West area children had a lower mean of five (5) colds than central area children who had a mean value of six (6).

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Data Organizing Tips

As required, ensure: • All questions answered • Questions needing to be skipped, were skipped• Split multiple choice into 1 answer per column • Open File 1

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Coding Open Ended Questions

What cold symptoms did your child have? • Take first 100 answers • Group similar answers together • You define what is similar • Reduce to 10-20 codes or fewer if useful• Pilot test

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Exercise: Create 3 codes

Question: Describe child’s symptoms?

• Response 1 Stuffy nose• Response 2 Sinus congestion, Runny nose• Response 3 Difficulty breathing through nose• Response 4 Phlegm, Body ache, Runny nose • Response 5 No energy, Cough• Response 6 No energy• Response 7 Sore throat• Response 8 Cough • Response 9 No energy

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Exercise: Code Symptoms

Code

Block

Expel

Pain

Description

Stuffy nose, Sinus congestion, Difficulty breathing through nose, No energy

Cough, Phlegm, Runny nose

Body Ache, Headache, Sore throat

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Assign Values to Codes

Code

Block

Expel

Pain

Description

Stuffy nose, Sinus congestion, Difficulty breathing through nose, No energy

Cough, Phlegm, Runny nose

Body Ache, Headache, Sore throat

Value

Yes=1

No=0

Yes=1

No=0

Yes=1

No=0

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I entered Code Variables and Values on new Excel sheet

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I entered Responses with values on new Excel Sheet

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To Enter Data

A B C

row 1 ID Location Block

row 2 1 1 0

row 3 2 2 1

row 4 3 1 0

• Row 1 has label for each variable • Enter data 1 survey at a time• 1 row per ID, work left to right• 1 answer per column

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To Enter Data

• Enter 1 survey at a time • For each question, work left to right across single Excel

row • Look at File 1b

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Practice

Enter answers on File 1b, sheet 3

Tip: Split answers across 3 columns

Question 2 • ID#1 Answer a)Block X b)Expel c)Pain• ID#2 Answer a)Block X b)Expel X c)Pain• ID#3 Answer a)Block X b)Expel c)Pain• ID#4 Answer a)Block X b)Expel X c)Pain X• ID#5 Answer a)Block X b)Expel X c)Pain

Compare your results with Data sheet

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Other Possible Open-EndedQuestion Codes

1 = positive comment

2 = negative comment

3 = neutral comment, positive and negative

Verbatim codes:

e.g., “ache”, “congested”, “cough”

Code only those related to research question

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Text or Numbers

• Text codes often easier to remember, fewer entry errors

• e.g. M for male and F for female

• But numbers often faster entry

• Easier to work with numbers in Excel

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Check for Response Accuracy

• See File 1b, Accuracy Sheet

• Take 1 question at a time - i.e. pick out single column and check answers

• ID numbers at left

• For unanswered question, create blank cell (pivot tables count blanks)

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Use Excel

When have larger number of respondents

Makes manual calculations easier

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Organize Data

• Make sure data are entered into Excel in such a way that mathematical transactions can be performed on them

• E.g., If studying gender, let male equal 1 and female equal 2. Can then count the 1s and 2s in your study.

To Explain Findings

Use common terms e.g., Variable

Use accepted methods of analyses

(Certain variables and measurements scales use certain tests)

Variable

An object or human characteristic that:

• Is observable • Can be subject to variation• Can be classified according to a type (discrete,

continuous as well as dependent, independent)

Variable and Measurement Scale

• Certain variables also use certain scales

• Can do frequency test for all variables

• But variable and scale type may also further inform with additional statistical test e.g., descriptive statistics

Discrete vs Continuous Variable

Continuous• Infinite values

• Valid values in-between

• E.g., distance, height, age

Discrete• Finite values• No valid values

in-between

• E.g., male/female

full/part-time/co-op

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Discrete or Continuous Variable

Hypothesis: City areas have different water temperatures.

Discrete Variable: e.g., West, Central, East areaYes or No Value, no values in-between, no equi-distance

Continuous: e.g., water temperatureInfinite number or range of possible values in-between

Dependent vs Independent Variable

Dependent• Assumed to change

Independent • Assumed to influence or does

not change

Hypothesis:

City areas have different water temperatures

Dependent

Water temperature Independent

City area

Measurement Scales

How do we measure what we are working with?

Types:1. Nominal2. Ordinal3. Interval4. Ratio

Nominal Scale

• Finite values• No values in-between • No logical order• Yes/No answer• E.g., East, West, Central• E.g., colour, gender

Ordinal Scale

• Finite values• No values in-between • Yes logical order• Yes/No answer • E.g., letter grades

Grade B

Grade C Grade Grade AA

Interval Scale

• Infinite values • Logical order• Values in-between• Equal distance between data points• How much? Numerical value• No natural zero, keeps going • No meaningful ratio between

numbers• E.g., temperature, • 20 degrees NOT twice as hot as 10

Ratio Scale

• Infinite values • Logical order• Values in-between• Equal distance between data points• Comparing how much? Numerical values• “0” value means something• Meaningful ratio between numbers • E.g., AGE

Adult earns $50K; Teenager earns $25K Adult earns twice as much as teenager

:

Variable Type Review Variable Type Values

Infinite

Range

Values in-between

Value in order

Values Equidistant

Scale *

Discrete(Yes/no)

X X X X Nominal

Discrete Grade (Yes/no) A B C

X X √ X Ordinal

Continuous(How much?)

Temperature or $$, Age

√ √ √ √ Interval or Ratio

Dependent Assumed changes due to an independent variable

Independent Assumed does not change

* Certain variables lend themselves to using certain types of measurement scales

Areas

Measurement Scale ReviewScale Values

finite in a range

Values have order

Values

in-between

Values are equidistant

Test *

Nominal(yes/no), AREAS

√ X X X Frequency

Ordinal A,B,C,(yes/no)

√ √ X X Frequency

Interval/ Ratio(how much for

1 sample?) Age

X √ √ √ Descriptive Statistics

Interval/ Ratio (how much for comparing 2 or more samples?)

G1 age G2 age

X √ √ √ Inferential Statistics e.g. t-test

Note: * Certain scales lend themselves to using certain statistical tests.

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Statistical Tests• Frequency count for discrete data (nominal, ordinal scale) *

- quantity

• Descriptive Statistics for continuous data (interval, ratio scale)– mean, median, mode– Characteristics of single sample

• Inferential Statistics for continuous data (ratio scale) – t-test – Comparison of two or more samples– Making inference from samples to populations

Note: Can do frequency count for all data types

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Methodology

• “By using a discrete/continuous independent variable called area (for city areas West, East, and Central), this study examined the discrete/continuous, dependent variable (with its possible multiple range of values) namely, water temperature.”

• “To measure the independent variable, (to indicate if someone lived in the West, East, or Central area), a nominal/ordinal/interval/ratio scale was used.”

• “To measure the dependent variable, to indicate how much the water temperature is), a(n) nominal/ordinal/interval/ratio scale was used.”

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Methodology

• “By using a discrete independent variable called area, this study examined the continuous, dependent variable namely, water temperature.”

• “To measure the independent variable, a nominal scale was used.”

• “To measure the continuous dependent variable, an interval scale was used.”

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Calculations

The statistical calculations we would conduct with Excel would be:

• Frequency counts for each of the 3 city areas • Descriptive statistics of mean, median, mode for water

temperatures

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Review of Descriptive Statistical Terms

• Mean – average

• Median – where 50% of scores lie above a certain score and where 50% lie below a certain score

• Mode – score that results most often

Format Cells

Open Booklet

Open File 1c)