Bj research session 8 gathering quantitative data

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Session 7: Quantitative methods

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FREE BOOK: Download it!
http://www.b2binternational.com/b2b-blog/free-ebook-
questionnaire-design/
Also see http://pareonline.net/pdf/v10n12.pdf

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Session Contents
 How to Make Your Questionnaire Great !!!!
 Who Should you Send it To?
 What Type of Questions Are There?

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Foundation
Definition of Questionnaires
Techniques of data collection in which each person
is asked to respond to the same set of questions in
a predetermined order
Adapted from deVaus (2002)

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Types of questionnaires
Types of questionnaire
Saunders et al. (2009)
Figure 11.1 Types of questionnaire

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Should I Choose On-line or Face to Face?
Consider
 Characteristics of the respondents and access
 Respondents answers not being contaminated or distorted
 Size of sample required for analysis
 Type and number of questions required
 Available resources including use of computer software

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How to get a Good Response Rate
http://www.cartoonstock.com/directory/q/questionnaire.asp

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How to Encourage a Good Response Rate
 Careful design of the questionnaire
 Clear & pleasing layout
 Clear statement of the purpose of the questionnaire
 Clear questions
 Pilot testing
 Careful planning and execution

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Design of the Questionnaire
 Cover Page / Letter
 Directions (What to do)
 Page Design
 Order of Questions
 Grouping of Questions
 Navigational Path
 Survey Length

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Cover Page
 Good quality paper:
 Official letterhead / logo (obtain permission):
 Clear Title:
 Date:
 Greeting:
 1st key message: Purpose
 2nd key message: Value your response & ‘x’ mins
 3rd key message: Confidentiality
 4th key message: Results
 Contact point for return / queries

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First Set of Questions:
 Apply to everyone
 Easy to answer in a few seconds
 Easy to read, understand & respond to (CLOSED)
 Interesting
 Connect to the purpose of the survey

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Question Groupings
 Group by content, user can focus & organise thoughts
 Group by type of question (e.g. all rating questions
together)
 Colour to establish groupings
 Objectionable questions at the end

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Question Layout
 Short and easy to answer
 Avoid double barreled questions
 Dark print for questions & light print for answer options
 Consistent in layout
 E.g. Scales go the same way
 E.g. The Phrasing of the questions is consistent
 People like putting ‘X’ in boxes

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Navigational Path
 If everyone does not need to answer all questions make it clear where they
should carry on.
 E.g. Do you use a Mac computer at work?
 (check one)
 Yes Skip to question 9
 No

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Types of Question

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Types of Questions
Classification Information that can be used to group
respondents to see how they differ one
from the other - such as age, gender,
social class, location of household, type
of house, family composition.
Behavioural Factual information on what the
respondent is, does or owns. Also the
frequency with which certain actions are
carried out.
Attitudinal What people think of something. Their
image and ratings of things. Why they do
things.

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Classification Questions
 Gender.
 Female
 Male
 •Household status.
 - Head of household ( )
 - Housewife ( )
 - Other adult ( )
 •Marital status. This is usually asked by simply saying "Are you ....."
 - Single ( )
 - Married ( )
 - Widowed ( )
 - Divorced ( )
 - Separated ( )

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Examples of Attitudinal questions:
• What do you think of ........?
• Why do you ........?
• Do you agree of disagree ........?
• How do you rate ........?
• Which is best (or worst) for ........?

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Examples of Behavioural questions:
• Have you ever ........?
• Do you ever ........?
• Who do you know ........?
• When did you last ........?
• Which do you do most often ........?
• Who does it ........?
• How many ........?
• Do you have ........?
• In what way do you do it ........?
• In the future will you ........?

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Examples of question types (1)
Open questions
6 Please list up to three things you like about your job
1…………………………………………
2…………………………………………
3…………………………………………
Useful for Attitudes

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List questions
7 What is your home city?
Please tick  the appropriate box
Dalian 大连  Shanghai 上海 
Chongqing 重庆  Beijing 北京 
Chengdu 成都  Hong Kong 香港 
Hangzhou 苏杭  Guangzhou 广州 
Changsha 长沙 
Nanjing 南京 
Useful for Classification & also Behaviours

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Category questions
8 How often do you visit the shopping centre?
Interviewer: listen to the respondent’s answer and tick  as
appropriate
 First visit
 Once a week
 Less than fortnightly to once a month
 2 or more times a week
 Less than once a week to fortnightly
 Less often

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Ranking questions
9 Please number each of the factors listed below in order
of importance to you in choosing a new car. Number the
most important 1, the next 2 and so on. If a factor has no
importance at all, please leave blank.
Factor Importance
Carbon dioxide emissions [ ]
Boot size [ ]
Depreciation [ ]
Price [ ]
Adapted from Saunders et al. (2009)

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Rating questions
10 For the following statement please tick the box that
matches your view most closely
Agree Tend to agree Tend to disagree Disagree
I feel employees’    
views have
influenced the
decisions taken
by management

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Rating Categories
 Agreement:
 Strongly agree / agree / neither agree nor disagree / disagree / strongly
disagree
 Amount:
 Far too much / too much / about right / too little / Far too little
 Frequency:
 Nearly all the time / frequently / sometimes / rarely / practically never
 Likelihood:
 Very / good / reasonable / slightly / not at all

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Quantity questions
14 What is your year of birth?
(For example, for 1988 write: )
1
1
9
9 8 8

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 CLASSIFICATION : SEX / AGE / SALARY ETC.
 OPEN WHAT DO YOU ENJOY …
 LIST TICK WHICH ARE RELEVANT
 RANKING LIST FROM MOST IMPORTANT
TO LEAST
 RATING STRONGLY AGREE TO STRONG
DISAGREE
 QUANTITY

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Who Should I Send it To? : Selecting Samples

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Selecting samples
Population, sample and individual cases
Source: Saunders et al. (2009)
Figure 7.1 Population, sample and individual cases

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The importance of response rate
Key considerations
 Non- respondents and analysis of refusals
 Obtaining a representative sample
 Calculating the active response rate
 Estimating response rate and sample size

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The need to sample
Sampling- a valid alternative to a census when
 A survey of the entire population is impracticable
 Budget constraints restrict data collection
 Time constraints restrict data collection
 Results from data collection are needed quickly
 30+ in each category is a useful rule of thumb

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Sample size
Choice of sample size is influenced by
 Confidence needed in the data
 Margin of error that can be tolerated
 Types of analyses to be undertaken
 Size of the sample population and distribution

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Overview of sampling techniques
Sampling techniques
Source: Saunders et al. (2009)
Figure 7.2 Sampling techniques

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Probability sampling
The four stage process
1. Identify ALL the possible cases from research objectives
2. Decide on a suitable sample size (larger the sample to
lower the chance of error)
3. Select the appropriate technique and the sample
4. Check that the sample is representative (if 60% of the
sample are x then 60% population are x)

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 Simple random: Next 10 people in the room
 Systematic: People whose student number ends in ‘3’
 Quota sampling : Random but 80% Female in BFSU
 Snowball sampling: Subject 1 identifies ‘3’ other people
 Self-selection sampling: “we are interviewing on ... Come along...”
 Convenience sampling: Grab & Go
Sampling techniques

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Sampling Summary:
 Choice of sampling techniques depends upon the
research question(s) and their objectives
 Factors affecting sample size include:
- confidence needed in the findings
- accuracy required
- likely categories for analysis

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End of the sampling detour !!!
All choices depend on the ability to gain access to
organisations

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Designing individual questions (2)
 Right level of detail?
 Do they have the right knowledge?
 Have you avoided jargon?
 Could your question cause offence?
 Could your question be shorter?
 Are you asking more than one question?
 Does your question imply the right answer?
 Is your question likely to embarrass the respondent?

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Summary:
 Data validity and reliability and response rate depend on
design, structure and rigorous pilot testing
 Wording and order of questions and question types are
important considerations
 Closed questions should be pre-coded to facilitate data
input and analysis

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Summary:
 Important design features are a clear layout, a logical
order and flow of questions and easily completed
responses
 Questionnaires should be carefully introduced and pilot
tested prior to administration
 Administration needs to be appropriate to the type of
questionnaire

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Analysing quantitative data

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Quantitative data analysis
Key points
 Data must be analysed to produce information
 Computer software analysis is normally used for this
process (Microsoft Excel, SPSS etc.)
 Present, explore, describe & examine relationships

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Examples of basic chart
Pie chart
Figure 12.8 Pie chart

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More advanced work requires Statistical analysis
 Establishing the statistical relationship between two
variables (e.g. If I am in this group I am have a %
probability of doing X).
 If you need to do this then see:
 http://www.statsoff.com/textbook
 http://oli.web.cmu.edu/openlearning/forstudents/
freecourses/statistics

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Quantitative data analysis: Main Concerns
 Preparing, inputting and checking data
 Choosing the most appropriate statistics to
describe the data
 Choosing the most appropriate statistics to
examine data relationships and trends

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Type of Data: category data
 Example: Number of cars hatchback / saloon /
estate
 Can’t measure it, just simply count occurrences
 Focus on one discrete variable (i.e. Hatchback)
 Dichotomous data (e.g. either Male or Female)
 Ranked data (how strongly you agree with
statement X)

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Type of Data: numerical data
 Example: height of students
 Quantifiable data that can be measured
 Interval data e.g. Degrees Celsius [zero
degrees is not actually ZERO]
 Ratio (calculate the difference) data e.g.
Profits up 34% for a year

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Type of Data: continuous data
 Example: height of students
 Can be any value [within a range]

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Level of Precision
Category Numerical Continuous
LESS MORE
Precise data can be grouped to make it less precise
(e.g. Mark of 85% grouped into a ‘Very Good’ category but
Not the other way round)

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Exploring Data: Tukey’s (1977) exploratory data
analysis approach focus on tables & diagrams
Great Tables & Diagrams Need:
Clear & Distinctive Title
Clearly stated units of measurement
Clearly stated source of data
Abbreviations explained in notes
Size of the sample is stated “n = 43”
Column / Row / Axis Labels
Dense shading for smaller areas
Logical Sequence of columns & rows

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Exploratory Analysis: Individual unit of data
 Highest and lowest values
 Trends over time
 Proportions (relative size)
 Distributions (number in a group)
Sparrow (1989)

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What Do You Want To Show?
 Highest / Lowest: Bar Chart / Histogram for Categories
 You can reordered it for Non-continuous data


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 Frequency: Again a Histogram / Bar Chart (reorder it
to make it clearer)
 Perhaps a pictogram


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 Trend: Line Chart or histogram


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 Proportion: Pie chart or bar chart


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Distribution of values

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Normal Distribution
Sample of 100+ people should
produce a normal curve.
Standard deviation shows how wide
the spread of results are.
Low standard deviation shows a narrow range of values
High standard deviation shows a wide range of values

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How to calculate it:
 Consider a population consisting of the following eight values:
 2, 4, 4, 4, 5, 5, 7, 9
 Calculate the Mean (2, 4, 4, 4, 5, 5, 7, 9) / 8 = 5
 Calculate the difference between each individual data point and
the mean. Then square each one
 Calculate the average of these values (i.e. 32 / 8 = 4)
 Find the sqaure root of this number (square root of 4 is 2)
 http://www.statsoff.com/textbook
 http://oli.web.cmu.edu/openlearning/forstudents/freecourses/statistics

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Exploring and presenting data (4)
Comparing variables to show
 Specific values and independence
 Highest and lowest values
 Proportions
 Trends and conjunctions

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Exploring and presenting data (5)
Comparing variables to show
 Totals
 Proportions and totals
 Distribution of values
 Relationship between cases for variables

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Describing data using statistics (1)
Statistics to describe a variable focus on
two aspects
 The central tendency
 The dispersion

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Describing the central tendency
 To represent the value occurring most frequently
 To represent the middle value
 To include all data values

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Describing the dispersion
 To state the difference between values
 To describe and compare the extent by which values
differ from the mean

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Examining relationships, differences and
trends
Using statistics to
 Test for significant relationships and differences
 Assess the strength of relationship
 Examine trends

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Summary:
 Data for quantitative analysis can be collected and
then coded at different scales of measurement
 Data type constrains the presentation, summary and
analysis techniques that can be used
 Data are entered for computer analysis as a matrix
and recorded using numerical codes
 Codes should be entered for all data values
 Existing coding schemes enable comparisons

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Summary:
 Data must be checked for errors
 Initial analysis should use both tables and diagrams
 Subsequent analyses involve describing data and
exploring relationships by using statistics
 Longitudinal data may necessitate different
statistical techniques

Bj research session 8 gathering quantitative data

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