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 QuestionsClassification Information that can be used to group

respondents to see how they differ onefrom the other - such as age, gender,social class, location of household, typeof house, family composition.

Behavioural Factual information on what therespondent is, does or owns. Also thefrequency with which certain actions arecarried out.

Attitudinal What people think of something. Theirimage and ratings of things. Why they dothings.

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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 AttitudesSaunders et al. (2009)

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List questions7 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 oftenSaunders et al. (2009)

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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: )


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1

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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 samplesPopulation, 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 sampleSampling- 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 techniquesSampling techniques

Source: Saunders et al. (2009)

Figure 7.2 Sampling techniques

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Probability samplingThe 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 TitleClearly stated units of measurementClearly stated source of dataAbbreviations explained in notesSize of the sample is stated “n = 43”Column / Row / Axis LabelsDense shading for smaller areasLogical 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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What Do You Want To Show?

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 widethe 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

Education

Transcript of Bj research session 8 gathering quantitative data