Research, Design and Data Collection [1] 2018

BML246

Research Skills Session 3:

Research Design and Data Collection:1 Understanding Your Data Tutors: Dr Andy Clegg and Dr Jorge Gutic

Learning Outcomes Aims: 혰 To discuss and contextualise the key elements of the research

design and data collection process

혰 To discuss and consider the differences between different types of

data

혰 To demonstrate how different types of data influence the type of

analysis that can be executed

혰 To map out different types of advanced statistical analysis and

demonstrate how the choice of statistical analysis is influenced by the type of data

Step 1: 1: Step Decide Your Decide on Research your Topicresearch

topic

1

Crafting Research

What?

Why?

• What puzzles/intrigues me?

• What do I want to know more about/understand better?

• What are my key research questions?

• Why will this be of enough interest to others to be published as a thesis, book, paper, guide to practitioners or policymakers?

• Can the research be justified as a ‘contribution to knowledge?’

How – Conceptually?

How – Practically?

• What models, concepts and theories can I draw on/develop to answer my research questions?

• How can these be brought together into a basic conceptual framework to guide my investigation?

• What investigative styles and techniques shall I use to apply my conceptual framework (both to gather material and analyse it)?

• How shall I gain access to information sources/date

Step 2: Research Aims & Objectives

2

Step 3a: Linking Data Types to Analysis

3a

Step 3a: Linking Data Types to Analysis Planning the Journey 3a

Assessment Criteria

— Evidence of clear research aims and objectives informed by background research — Clear extrapolation of answers and analysis based on the use of the appropriate use of either qualitative or quantitative approaches (e.g. SPSS)

Data & Data Sources Principal Forms of Data: Quantitative: the observations or responses are expressed

numerically

Qualitative:

use of comments, case studies or observations with responses often subsequently assigned to categories

Ideally aim for a balance of both quantitative and qualitative

methodologies/data (mixed methods)

Data Types

NOIR

Data Types Nominal:

NOIR

List of categories to which objects can be attributed; objects

can be counted but not be measured numerically (classed as qualitative data)

No assumption on their order, only that objects in different

categories are “different”

Classed as non-parametric data Examples:

Which supermarket do you normally shop at?: Asda (1), Sainsburys (2), Tesco (3), Morrisons (4) Gender: Male (1), Female (2)

Data Types

Types of Data Ordinal:

NOIR

List of categories but this time ordered or ranked; differences

are in relative magnitude (greater than; less than)

No assumption is made on the ‘distance’ between categories Classed as non-parametric data

Examples: How would you rate the quality of service provided by your mobile phone company?: 5 – Excellent to 1 – very poor University student populations ranked by size

Data Types Data Types Types of Data Interval:

NOIR

Observations are made on a scale comprising equal intervals

but the zero value is arbitrary

Classed as parametric data

Examples:

Fahrenheit or Celsius scale to measure temperature.

Differences make sense but ratios do not (20o/10o is not twice as hot!)

Data Types Ratio:

NOIR

Observations are made on a scale comprising equal intervals

with a true zero point

Classed as parametric data

Examples:

e.g. age, height, weight, response time, grade

Data Types – Further Definitions

QUANTITATIVE DATA

Discrete

Continuous

No. of people in the class

Distance traveled: 12.654 miles

INTEGER

Data Types – Further Definitions

NOIR NOMINAL

ORDINAL

NON-PARAMETRIC

INTERVAL

RATIO

PARAMETRIC

Linking Data Types to Analysis Linking Data Types to Analysis Nature of the Question

NOMINAL ORDINAL Type of Data

INTERVAL RATIO

Type of Analysis DESCRIPTIVE INFERENTIAL

Linking Data Types to Analysis

General Purpose

Description (only)

Specific Purpose

Summarise Data

Compare Groups

Finds Strengths of Association, Relate Variables

Descriptive

Difference

Associational

Descriptive Statistics (e.g. mean, percentage, range)

(e.g. t-test, Mann Whitney)

(e.g. correlation)

Type of Question/Hypothe sis General Type of Statistic

Explore Relationship Between Variables

[Source: Morgan, G. et al (2011), IBM SPSS for Introductory Statistics, Routledge, London, p. 6]

Linking Data Types to Analysis

General Purpose

Description (only)

Specific Purpose

Summarise Data

Compare Groups

Finds Strengths of Association, Relate Variables

Descriptive

Difference

Associational

Descriptive Statistics (e.g. mean, percentage, range)

(e.g. t-test, Mann Whitney)

(e.g. correlation)

Type of Question/Hypothe sis General Type of Statistic

Explore Relationship Between Variables

[Source: Morgan, G. et al (2011), IBM SPSS for Introductory Statistics, Routledge, London, p. 6]

Linking Data Types to Analysis

Question: What grade do you expect to get?

Type of Data =

Type of Analysis

Linking Data Types to Analysis

Question: What grade do you expect to get?

Type of Data = NOMINAL

Type of Analysis

Linking Data Types to Analysis

Type of Analysis

Tabular What grade do you expect to get for the module?

2011/2012

2010/2011

%

%

Grade A

6

8.5%

3

12%

Grade B

17

23.9%

16

32%

Grade C

37

52.1%

19

38%

Grade D

11

15.5%

11

22%

Grade E

0

0%

1

2%

Total

71

100%

50

100%

Linking Data Types to Analysis

Type of Analysis

Graphical Expected(Grade(for(BML224(

F"(<40%):" 2%"

A"(70%+):" 6%"

D"(40,49%):" 22%"

B"(60,69%):" 32%"

C"(50,59%):" 38%"

Linking Data Types to Analysis

Type of Analysis

Graphical Expected)Grade)for)BML224) 40%

35%

30%

Percentage)(%))

25%

20%

15%

10%

5%

0% A)(70%+):

B)(60169%):

C)(50159%):

Expected)Grade)

D)(40149%):

F)(<40%):

Linking Data Types to Analysis

Question: How confident do you feel about starting this module?

Type of Data =

Type of Analysis

Linking Data Types to Analysis

Question: How confident do you feel about starting this module?

Type of Data = ORDINAL

Type of Analysis

Linking Data Types to Analysis

Type of Analysis

Tabular How confident are you about starting this module?

2011/2012

2010/2011

%

7 - Very confident

0

0.0%

0

0.0%

6 - Quite Confident

4

5.60%

1

2.0%

5 - Confident

16

22.50%

10

20.0%

4 - Uncertain

28

39.40%

26

52.0%

3 - Anxious

14

19.70%

7

14.0%

2 - Quite Anxious

4

5.60%

4

8.0%

1 - Very Anxious

5

7.00%

2

4.0%

Uncertain to very anxious

51

72%

39

78%

Sample (n)

71

50

%

Linking Data Types to Analysis

Type of Analysis

Tabular How confident are you about starting this module?

Average confidence level 2010/2012: 3.82 Average confidence level 2011/2012: 3.82

2011/2012

2010/2011

%

7 - Very confident

0

0.0%

0

0.0%

6 - Quite Confident

4

5.60%

1

2.0%

5 - Confident

16

22.50%

10

20.0%

4 - Uncertain

28

39.40%

26

52.0%

3 - Anxious

14

19.70%

7

14.0%

2 - Quite Anxious

4

5.60%

4

8.0%

1 - Very Anxious

5

7.00%

2

4.0%

Uncertain to very anxious

51

72%

39

78%

Sample (n)

71

50

%

Linking Data Types to Analysis

Type of Analysis

Graphical Student&Confidence&Levels&2011&

Very&Anxious:!

4%!

Quite&Anxious:!

8%!

Quite&Confident:!

2%!

Confident:!

20%!

Anxious:!

14%!

Uncertain:!

52%!

Linking Data Types to Analysis

Question: Attitudes to Statistics

Type of Data =

Type of Analysis

Linking Data Types to Analysis

Question: Attitudes to Statistics

Type of Data = ORDINAL

Type of Analysis

Linking Data Types to Analysis

Type of Analysis

Tabular

Attitudes Towards Statistics

This is my first ever statistics class I am worried about this module If I could avoid taking this module I would I've never enjoyed maths Passing is my main goal for this module I do not see the relevance of this module

Strong Agree [5]

Agree [4]

No Opinion [3]

Disagree [2]

Strongly Disagree [1]

32%

37%

4%

14%

13%

17%

26%

18%

32%

7%

14%

32%

25%

20%

9%

10%

24%

24%

30%

13%

32%

38%

17%

10%

1%

7%

9%

24%

39%

21%

Linking Data Types to Analysis

Type of Analysis

Graphical Student$Attitudes$to$Statistics$ I,do,not,see,the,relevance,of,this,module

7%$

9%$

Statement$

Passing,is,my,main,goal,for,this,module

24%$

39%$

32%$

21%$

39%$

17%$

10%$ 1%$

Strongly,Agree Agree

I've,never,enjoyed,maths

10%$

24%$

24%$

30%$

13%$

No,Opinion Disagree Strongly,Disagree

If,I,could,avoid,taking,this,module,I,would

14%$

I,am,worried,about,this,module

32%$

17%$

This,is,my,first,ever,statistics,class

25%$

25%$

18%$

32%$

0%

20%

20%$

32%$

37%$

40% Percentage$

4%$

60%

14%$

80%

9%$

7%$

13%$

100%

Linking Data Types to Analysis

Type of Analysis

Graphically Attitudes$to$Statistics$

I+do+not+see+the+relevance+of+this+module

4$

Response$

Passing+is+my+main+goal+for+this+module

Ranking$Scale$ + 1+=+Strongly+Agree+ 2+=+Agree+ 3+=+No+opinon+ 4+=+Disagree+ 5+=+Strongly+Diagree+ +

2.5$

I've+never+enjoyed+maths

3.4$

If+I+could+avoid+taking+this+module+I+would

3.2$

I+am+worried+about+this+module

3$

This+is+my+first+ever+statistics+class

2.3$

0

1

2

3 Mean$Rank$

4

5

Linking Data Types to Analysis

Type of Analysis

Graphically Attitudes$to$Statistics$

Plotting the mean score (rank) for each response

I+do+not+see+the+relevance+of+this+module

4$

Response$

Passing+is+my+main+goal+for+this+module

Ranking$Scale$ + 1+=+Strongly+Agree+ 2+=+Agree+ 3+=+No+opinon+ 4+=+Disagree+ 5+=+Strongly+Diagree+ +

2.5$

I've+never+enjoyed+maths

3.4$

If+I+could+avoid+taking+this+module+I+would

3.2$

I+am+worried+about+this+module

3$

This+is+my+first+ever+statistics+class

2.3$

0

1

2

3 Mean$Rank$

4

5

Linking Data Types to Analysis

Question: Business Turnover in 2010

Type of Data =

Type of Analysis

Linking Data Types to Analysis

Question: Business Turnover in 2010

Type of Data = RATIO

Type of Analysis

Linking Data Types to Analysis Type of Analysis

Analytical Descriptive Statistics – Turnover 2010 Turnover 2010 Mean

£41,311.40

Median

£44,640.00

Mode

£44,760.00

Standard Deviation

£9191.0316

Linking Data Types to Analysis

Type of Analysis

Graphical

Distribution of the Data

Box plot

Linking Data Types to Analysis

Type of Analysis

Distribution of the Data

Graphical

Linking Data Types to Analysis

Type of Analysis

Tabular Size of Business in 2010 by Category of Turnover Turnover 2010

No. of Businesses

Percentage

£0 to £9,999

0

0

£10,000 to £19,999

0

0

£20,000 to £29,999

48

16

£30,000 to £39,999

73

24

£40,000 to £49,000

118

39

£50,000 to £59,000

59

20

£60,000

2

1

300

100%

Total

Linking Data Types to Analysis

Type of Analysis RECODING

RATIO

NOMINAL

Tabular Size of Business in 2010 by Category of Turnover Turnover 2010

No. of Businesses

Percentage

£0 to £9,999

0

0

£10,000 to £19,999

0

0

£20,000 to £29,999

48

16

£30,000 to £39,999

73

24

£40,000 to £49,000

118

39

£50,000 to £59,000

59

20

£60,000

2

1

300

100%

Total

Linking Data Types to Analysis Linking Data Types to Analysis Type of Analysis RECODING

NOMINAL

RATIO

Tabular Size of Business in 2010 by Category of Turnover Turnover 2010

No. of Businesses

Percentage

£0 to £9,999

0

0

£10,000 to £19,999

0

0

£20,000 to £29,999

48

16

£30,000 to £39,999

73

24

£40,000 to £49,000

118

39

£50,000 to £59,000

59

20

£60,000

2

1

300

100%

Total

Linking Data Types to Analysis

Type of Analysis

Graphical Size%of%Business%by%Turnover%Category% £60,000% 1%% £50,000%to%£59,000%% 20%%

£20,000%to%£29,999%% 16%%

£30,000%to%£39,999%% 24%%

£40,000%to%£49,000%% 39%%

Linking Data Types to Analysis

Question: % Change in Turnover 2008-2010

Type of Data =

Type of Analysis

Linking Data Types to Analysis

Question: % Change in Turnover 2008-2010

Type of Data = INTERVAL

Type of Analysis Same process of analysis for RATIO data would apply

Types of Data Summary TYPE OF MEASUREMENT

QUALITATIVE NOMINAL In nominal measurement the variables consists of named categories. The categories have no mathematical properties.

QUANTITATIVE VARIABLES ORDINAL

INTERVAL

RATIO

The scores indicate only rank order in terms of size. It is not correct to calculate means on the scores.

The steps between the scores are equal in size though there is no proper zero point.

The scores can be added, means calculated etc.

This is the same as ‘interval’ measurement but the scale has a proper zero point. Ratios can be calculated as a consequence

[Source: Howitt, D. and Cramer, D. (2011), Introduction to Research Methods in Psychology, Pearson, London]

Services

Research Design and Data Collection:

Linking Data Types to Statistical Analysis

Crosstabulations Definition Â&#x2014; A crosstabulation is a joint frequency distribution of cases based on

two or more categorical variables

Â&#x2014; Displaying a distribution of cases by their values on two or more

variables is known as contingency table analysis

Crosstabulations Â&#x2014; Examples: Length of Ownership by Response to Recession

Crosstabulations Â&#x2014; Examples: Length of Ownership by Response to Recession

Statistical Tests Used to make deductions/inferences about a particular data set or

relationships (differences/associations) between different data sets

Random sample of 50 households in two rural villages in West

Sussex:

Village A: mean income £17,650 Village B: mean income £22,220

A test can be used to determine if there is a ‘real difference’ or

whether the difference occurred ‘purely by chance’

Statistical Tests Â&#x2014; Parametric Tests:

data conforms to normal distribution and is of interval or ratio in nature

Â&#x2014; Non-Parametric Tests:

data does not conform to normal distribution â&#x20AC;&#x201C; use ordinal data

Parametric Tests Independence of observations (except where the data is paired) Random sampling Interval scale measurement for the dependent variable A minimum sample size of 30 per group is recommended Equal variances of the population from which the data is drawn Hypotheses are usually made about the mean of the population

Non-Parametric Tests Independence of randomly selected observations except when paired Few assumptions concerning the distribution of the population Ordinal or nominal scale of measurement Ranks or frequencies of data are the focus of tests A minimum sample size of 30 per group is recommended Hypotheses are posed regarding ranks, medians or frequencies Sample size requirements are less stringent than for parametric tests

Research Design and Data Collection:

Student T-Test

Student T-Test Â&#x2014; Scenario Â&#x2014; As part of the bidding process to Tourism South East for

future tourism funding, local tourism officers have to demonstrate if there is a difference in profit levels between businesses in the Arun and Chichester Districts

Student T-Test: Data Requirements

Grouping Variable [Independent Variables]

Test Variables [Dependent Variables]

Student T-Test: Data Requirements

Grouping Variable [Independent Variables]

Test Variables [Dependent Variables]

It is a variable that stands alone and isn't changed by the other variables you are trying to measure

A variable that depends on other factors

Student T-Test: Data Requirements

Grouping Variable [Independent Variables]

Test Variables [Dependent Variables]

Nominal (Categorical) [2 Levels]

Ratio or Interval (Continuous)

Student T-Test: Data Requirements Â&#x2014; Scenario Â&#x2014; As part of the bidding process to Tourism South East for

future tourism funding, local tourism officers have to demonstrate if there is a difference in profit between businesses in the Arun and Chichester Districts

Profit Test Variable Ratio (continuous)

Student T-Test: Data Requirements Â&#x2014; Scenario Â&#x2014; As part of the bidding process to Tourism South East for

future tourism funding, local tourism officers have to demonstrate if there is a difference in turnover between businesses in the Arun and Chichester Districts

Area Code Grouping Variable Nominal (categorical) 2 Levels [Chichester District 1 / Arun District 2]

Student T-Test: Data Requirements

Grouping Variable [Independent Variables]

Test Variables [Dependent Variables]

Nominal (Categorical) [2 Levels]

Ratio or Interval (Continuous)

Nominal (Categorical) [2 Levels]

Nominal (Categorical) [2 Levels]

Identify potential variables for use in a Student TTest from the dataset guide

Choosing the Right

One Categorical and One Continuous

Mann Whitney Â&#x2014; Scenario Â&#x2014; Tourism South East are developing a new e-tourism strategy

and they want to establish if there is any difference between e-strategy motives (e-commerce adopters and non-adopters) and business attitudes to the web-based customer relationship management systems TSE offer

Mann Whitney: Data Requirements

Grouping Variable [Independent Variables]

Test Variables [Dependent Variables]

Mann Whitney: Data Requirements

Grouping Variable [Independent Variables]

Test Variables [Dependent Variables]

Nominal (Categorical) [2 Levels]

Ordinal

Mann Whitney: Data Requirements Â&#x2014; Scenario Â&#x2014; Tourism South East are developing a new e-tourism strategy

and they want to establish if there is any difference between e-strategy motives (e-commerce adopters and non-adopters) and business attitudes to the web-based customer relationship management systems TSE offer TSECMS Test Variable Ordinal (continuous)

Mann Whitney: Data Requirements Â&#x2014; Scenario Â&#x2014; Tourism South East are developing a new e-tourism strategy

and they want to establish if there is any difference between e-strategy motives (e-commerce adopters and nonadopters) and business attitudes to the web-based customer relationship management systems TSE offer E-Strategy Grouping Variable Nominal (categorical) 2 Levels [E-Commerce Adopters 1 / E-Commerce Non-Adopters 2]

Mann Whitney

Grouping Variable [Independent Variables]

Test Variables [Dependent Variables]

Nominal (Categorical) [2 Levels]

Ordinal

Nominal (Categorical) [2 Levels]

Nominal (Categorical) [2 Levels]

Identify potential variables for use in a Mann Whitney Test from the dataset guide

Learning Outcomes Aims: 혰 To discuss and contextualise the key elements of the research

design and data collection process

혰 To discuss and consider the differences between different types of

data

혰 To demonstrate how different types of data influence the type of

analysis that can be executed

혰 To map out different types of advanced statistical analysis and

demonstrate how the choice of statistical analysis is influenced by the type of data