LECTURE 5 RESEARCH METHODOLOGY

LECTURE 5

RESEARCH METHODOLOGY

Unit of analysis Sample selection and size Time series, cross sectional and panel

data Correlation and regression

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Research Methodology

 Methodology is the systematic, theoretical analysis of the methods applied to a field of study. It comprises the theoretical analysis of the body of methods and principles associated with a branch of knowledge. Typically, it encompasses concepts such as paradigm, theoretical model, phases and quantitative or qualitative technique (Irny & Rose, 2005)

 It is also a process used to collect information and data for the purpose of making business decisions .

 The methodology may include publication research, interviews, surveys and other research techniques, and could include both present and historical information.

 The choice of the method is either quantitative or qualitative research method. Sometimes it may use both methods (multiple methods).

■ Source: http://www.businessdictionary.com/definition/research-methodology. html

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Quantitative research method

■ Aliaga and Gunderson (2000): defining quantitative research methods as ‘explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particular statistics)’.

■ It examines relationship between variables which are measured numerically and analysed using a range of statistical techniques (Saunders et al, 2012)

■ This method is usually associated with a deductive approach where the focus is on using data to test theory.

■ It may also incorporate an inductive approach where data are used to develop theory.

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Qualitative research method

■ Qualitative research looks at the “why” and “how”. Qualitative research produces observations, notes, and descriptions of

behavior and motivation.

■ Research methods in this category include:

■ Interviews: either a series of structured questions, or allowing a subject to narrate their experience

■ Focus groups: soliciting observations from groups of people who share a similar attribute (for example, a group of women over 40) to give opinions on a topic

■ Reviews: combing through scholarly literature or other

published writings to determine attitudes towards a subject

■ Observation: researchers watch people on their daily routine and make notes or recordings documenting their behaviour

Source: http://www.aiuniv.edu/blog/october-2012/qualitative-vs-quantitative-research

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Multiple research methods

■ The principle of triangulation proposes that the researchers should seek to ensure that they are not over reliant on a single research method and should instead employ more than one measurement procedure when investigating a research problem (Bryman, 2014).

■ Multi method research entails the application of two or more sources of data or research methods to the investigation of a research question or to different but highly linked research questions.

■ The discussion of multi method research has increasingly been stretched to include the collection of qualitative as well as quantitative data.

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The research ‘onion’

Source: © Mark Saunders, Philip Lewis and Adrian Thornhill 2011

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Methodological choice

Source: © Mark Saunders, Philip Lewis and Adrian Thornhill 2011

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UNIT OF ANALYSIS

One of the most important ideas in a research project is the unit of analysis. The unit of analysis is the major entity

that you are analyzing in your study. For instance, any of the following could be a unit of analysis in a study:

■ Individuals/households

■ Organizations/firms

■ Government/federal/state/local authority

■ social artifacts (the products of social beings or their behavior like books, photos, newspapers)

■ geographical units (town, state, country)

■ social interactions (racial relationship, divorces, arrests) For different analyses in the same study you may have

different units of analysis.

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Sample selection and size

■ Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.

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Population, sample and individual cases Source: Saunders et al (2012)

Sampling techniques

Sample size

■ Sample size is determined by choosing the number of observations or replicates to include in a statistical sample. It is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.

■ The minimum sample size assumes that data will be collected from all cases in the sample and is based on;

 How confident you need to be that the estimate is accurate

 How accurate the estimate needs to be (the margin of error that can be tolerated)

 The proportion of responses you expect to have some particular attribute

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Data : Cross sectional, time series and panel data

■ Cross sectional data are observations that belong to different unit of analysis at the same period of time. For example, the profit of 20 commercial banks in Malaysia in 2015.

■ Time series data are observations collected at usually

discrete and equally spaced time intervals. For example, the profit of Maybank from 2000 until 2015.

■ Panel data refers to data sets consisting of multiple observations on each sampling unit. This could be generated by pooling time-series observations across a variety of cross- sectional units including countries, states, regions, firms, or randomly sampled individuals or households. For example, the profit of 20 commercial banks from 2000 until 2015.

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■ The usage of any types of the above data can be traced by checking the regression

equations used in the study. Subscript t indicates time series data being used,

subscript i for cross sectional and subscript it for panel data.

■ Consider an equation for economic growth (G) that is a function of investment (I), interest rate (R) , trade performance (T) and labours productivity (L). Thus,

■ G

= β

+ β

I

+ β

R

+ β

T

+ β

L

+ Ɛ

.

■ G

= β

+ β

I

+ β

R

+ β

T

+ β

L

+ Ɛ

.

■ G

= β

+ β

I

+ β

R

+ β

T

+ β

L

+ Ɛ

.

■

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Technique of analysis: Correlation

■ Correlations are used when you want to know about the relationship between two variables. For example, you want to know consumers’ willingness to pay and their ratings for the product quality.

■ If the correlation coefficient is 1, meaning the willingness to pay and the ratings for the product quality are completely positively correlated.

■ If the correlation coefficient is 0, meaning there is no correlation between these two variables.

■ If the correlation coefficient is -1, it shows they are completely negatively correlated, meaning the higher one variable, the lower the other variable.

■ If the absolute value of the coefficient is bigger than 0.5, the variables have stronger relationship.

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Correlation Coefficient

■ A measure of association between two variables is the correlation coefficient, r.

■ The correlation coefficient is calculated as:

■ Where the (sample) covariance of the variables divided by the product of their (sample) standard deviations.

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r = -1 r = +1 r = 0

Y Y Y

X X X

Correlation: example

■ Student population and sale o f pizza

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Technique of analysis: regression

■ In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modelling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.

■ Regression analysis is widely used for prediction and forecasting

■ The types of regression to carry out data analysis include linear regression, simple regression, multiple regression, robust regression, logistic regression, stepwise regression etc.

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What does regression mean?

■ Regression is a statistical technique for obtaining the line that best fits the data points according to an objective statistical criterion, Salvatore (2004).

■ Specifically, the regression line is the line obtained by minimizing the sum of the squared vertical deviations of each point from the regression line.

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Regression models involve the following variables:

■ The unknown parameters, denoted as β, which may represent a scalar or a vector .

■ The independent variables, X.

■ The dependent variable, Y.

In various fields of application, different

terminologies are used in place of dependent and independent variables.

■ A regression model relates Y to a function of X and β.

■ Y = f (X, β )

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■ In simple linear regression for modelling data points there is one independent variable: x

, and two parameters, β

and β

:

■ straight line:

■ In multiple linear regression, there are

several independent variables or functions of independent variables.

■ Yi = β

+ β

X1 + β

X2 + β

X3 + εi, i = 1,2,

…..n.

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Simple Linear Regression: Ordinary least square method

■ Y

= a + b X

+ 

where t is for time & _t forerror term

■ To find “best fitting” line



(observed Y – estimated Y)



= [Y

- a - b X

]

.

■ Min

= [Y

- a - b X

]

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_ X Y

_ Y a

DY DX

X

Solution Methods: softwares

■ Spreadsheets - such as Excel

■ Statistical calculators

■ Statistical programs such as – Eviews

– Stata – SPSS – Minitab – SAS

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