1. Quantitative Research 2
Dr N L Reynolds

2. Lecture Objectives
To define what causal research is and provide examples of its use in business
To understand how to identify appropriate statistical tests
To understand the requirements of statistical analysis techniques

3. Importance of… Causal research
Exploratory research can create hypotheses to test
Descriptive research can show that variables are associated
Only causal research can provide support, or not, for whether one variable is the cause of another
Data analysis
Analysis obtains meaning from the collected data. All previous steps in the research process have been undertaken to support the search for meaning. The specific analysis that can be used is closely related to the preceding steps, and the careful analyst will remember this when designing the other steps
Statistical testing allows you to state how likely it is that the results you have found are also found in your population

4. Key Issues
What is the difference between description and demonstrating causality
How is causal research done and what are its uses in Business/Management research
Why use statistics?
What are the stages of data analysis
How do you choose the right data analysis method?

5. Key Issue 1: Demonstrating causality
Concomitant variation
A predictable statistical relationship between two variables
Time order of occurrence
A change in the independent variable must occur before a change in the dependent variable
Elimination of other possible causal factors

6. Key Issue 2: Conducting experiments
Assigning subjects to groups
Control groups
Making interventions
Making observations
Internal and external validity
Types of experiment
Field or laboratory
True and quasi-experimental designs

7. Error caused by type of environment High Low
Malhotra & Birks (2000)
Factor
Laboratory
Field
Type of environment
Artificial
Realistic
Error caused by type of environment
High
Low
Control
Responses guided
Internal validity
External validity
Time
Short
Long
Number of units
Small
Large
Ease of implementation
Cost

8. Key Issue 2: Causal questions in B/M research
Which of three advertisements works best at increasing awareness of the MA Management at Bradford University with Brazilian undergraduates?
Which of two methods of training is more effective at eliminating bullying behaviour among factory workers?
Is written communication of new auditing standards better at ensuring their understanding and correct application than verbal communication of standards?
Do different sampling regimes affect the speed at which faults on a production line are detected?
Which of three methods of preparing salespeople for overseas assignments produces the fastest acceptance of the new working environment?

9. Key Issue 3: What do statistics do?
The world before analysis
The world after analysis
Data interpretation
Data collection
Data organisation and manipulation
Kachigan (1991)

10. Key Issue 3: Why do statistics?
To reduce a data set to a manageable summary (e.g., measures of central tendency, measures of spread)
To determine the degree of confidence in the accuracy of the measurements we take (including whether two measurements differ)
To identify associations or relationships between and among sets of observations
Kachigan (1991)

11. Key Issue 3: Statistics are tools
Methods of determining the answer to the research question.
By knowing the analysis technique, its strengths and weaknesses, you can use it to solve the problem: apply the tool to the problem, do not manipulated the problem to fit a tool you are comfortable using.

12. Key Issue 4: Stages of data analysis
Designing the measurement methods and collecting the data
Preparation of the data
Coding, data entry, etc.,
Describing the data
Answering your research question

13. Key Issue 4: Variables, scale types and samples
Dependent and independent variables
Levels of measurement
Nominal (assignment)
Ordinal (assignment and order)
Ordinal interval or assumed interval
Interval (assignment, order and distance)
Ratio (assignment, order, distance and origin)
Impact of sample size

14. Key Issue 4: Variables, scales and relationships
Measures of central tendency and spread
Distribution and frequency tables
Scales
Multi-item scales and reliability (and validity)
Multi-dimensional scales
Relationships
Cross-tabulation and 2 tests
Correlations
Between groups

15. Key Issue 5: Parametric or non-parametric?
Start
Nominal or ordinal
Use
non-parametric statistics
Level of measurement
Interval or ratio
Use
parametric statistics*
Population distribution?
Normal
Other
* Assuming additional assumptions are satisfied
Small
Sample size?
Large
Source: Diamantopoulos, 2000

16. Key Issue 5: Type I and Type II Error
Type I error
Occurs when the sample results lead to the rejection of a null hypothesis when it is in fact true (also called alpha error)
Significance is the probability of making a type I error
Type II error
Occurs when the sample results lead to acceptance of the null hypothesis when it is in fact false (also called beta error)
Power is the probability of rejecting the null hypothesis when it should be rejected

17. Key Issue 5: But which test?
What is the question you want answered?
How many variables are you using?
Independent? Dependent?
What is the level of measurement?
How many samples are you dealing with?
What is the relationship between the samples?

18. Causal Research and Data Analysis: Their contribution to your dissertation
Most will read papers that claim causality when the methods used do not support those claims
Most will read papers that use causal research
A few will develop research questions that are best answered by causal research
Data analysis
All will read papers that use quantitative data analysis
Almost all will need to analyse (either qualitative or quantitative) data
Many will need to summarise large data sets that cannot easily be interpreted
Many research objectives should be tested statistically