Professor Felix Famoye

8/12/2019 Professor Felix Famoye

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Research Methodology and Data

Analysis

Felix Famoye

Central Michigan University, USACurrently a Fulbright Scholar, Unilag

Presented by Felix Famoye3/30/2014 1


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Thanks to Workshop Organizers

Thanks for the Invitation

My research area is Statistics

Additional thanks to Dean, SPGS, Unilag



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Outline of the talk

Introduction

Research Types

Sub-sections of Research Methodology Data Analysis

Conclusion/Final Comments



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Introduction

The methodology section shows how your

research questions will be answered.

It must be appropriate for your research type.

Describe in detail what methodology and

materials, if any, that you will use to carry out

your research.

This section may have some of the following

sub-sections:



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Some sub-sections:

Conceptual and/or Theoretical Framework

Models and/or Theorems Formulation

Research/Experimental Design

Sampling Method

Measurement Instruments

Materials and Experiments

Data Collection Method

Data Analysis

Sub-sections choice depends on research type.

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Research Types

Broadly speaking, we have qualitative and

quantitative research studies.

These two broad methods have further been

divided into different types.

Boundaries between the research types may

not be that clear.



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Qualitative method:

In this approach, narrative data is collected in

order to study the topic of interest.

It is also called ethnographic (investigating

cultures) or anthropological research.

The data analysis includes coding and

production of verbal synthesis.

No statistical procedures or other means of

quantification is involved.



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Types of Qualitative Research:

Historical research, allows one to discuss past

and present events. The method investigates

the whyand howof decision making. An

example: Factors that led to the creation of

more states (or more universities) in Nigeria.

Qualitative research is involved in the study of

current events rather than past events.

Examples: A case study of how students solvealgebraic equations.



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Quantitative method:

In this approach, data (both numerical and

non-numerical) is collected in order to

describe, predict and/or control phenomena

of interest.

The data analysis is mainly statistical.

Quantitative research can be used to verify

such hypotheses formulated through

qualitative research. Consider the example onhow students solve algebraic equations.



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Quantitative research generally includes:

Development of models, theories and

hypotheses.

Development of instruments and methods to

collect data.

Experimental control and manipulation of

variables.

Collection of empirical data.

Modeling and analysis of data.

Evaluation of results.



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Statistics is widely used in quantitative

research.

Quantitative method can be divided into four

types:odescriptive research

ocorrelational research

ocausal-comparative research

oexperimental research



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Jokes:

[http://www.btinternet.com/~se16/hgb/statjoke.htm]

How many statisticians does it take to change alight bulb? 1-3, alpha = 0.05.

There is no truth to the allegation thatstatisticians are mean. They are just yourstandard normal deviates.

Did you hear about the statistician who inventeda device to measure the weight of trees? Itsreferred to as the log scale.

Did you hear about the statistician who wasthrown in jail? He now has zero degrees offreedom.

http://www.btinternet.com/~se16/hgb/statjoke.htmhttp://www.btinternet.com/~se16/hgb/statjoke.htm


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Sub-sections of Research Methodology

Research/Experimental Design: This depends on your research type.

Is it descriptive, correlational, causal-

comparative or experimental research? For example, one may want to compare two

teaching methods. One possible design is to

have three groups of subjects (method 1,method 2, and a control; pre/post tests).



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Sampling Methods:

For surveys or any research in which you plan

to collect data, define your population.

What is the sampling design? (or How will you

select your subjects?)

How many subjects will you select?

For example, to estimate proportion:

n= Npq/[(N1)B2

/4 +pq], where N=population size, B= error bound,p= 0.5]



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Sampling Methods continued:

To generalize your result, use a probability

sampling method.

Among the probability sampling methods are

simple random sample; systematic random

sample; stratified random sample; cluster

sample.

Among the non-probability sampling methods

are voluntary response sample; convenience

sample.



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Measurement Instruments (and/or Materials):

Are you using a survey designed by you or

someone else? Give reference, if other(s).

Address the reliability of the instrument.

In the biological or medical sciences, address

the materials that will be used.



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Data Collection Methods:

If you do not have adequate training in thisarea (or in data analysis), seek help before

you begin to collect your data.

Quite often, researchers collect inadequate

data or data that are not properly recorded.

You want to be sure that the data you collect

can be used to answer your questions or test

your hypotheses.



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Data Collection Methods continued:

Data from surveys and experiments are calledprimary data.

Data obtained from a source are calledsecondary data.

For examples, humanities, social sciences,public health, law and education are mostlikely to use surveys.

Also for examples, agriculture, physical andbiological sciences, medical sciences are mostlikely to conduct experiments.



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Some data collection methods are:

Personal interviews

Telephone interviews

Direct observation

Self-administered questionnaires (Mailed orhanded out, especially in convenience sample)



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Example of Model Developments sub-section:

R-squared measures will be developed.

The R-squared measures will be adjusted for

both the sample size and the number of

independent variables.

Power-divergent statistics will be developed.

A detailed simulation study will be conducted

to compare the log-likelihood ratio, R-squared,

and the power-divergent statistics.



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Data Analysis

Will data be analyzed qualitatively or

quantitatively?

The choice will depend on data collectionmethods and the sample size.

Describe the types of data analysis ormodeling that will be done.

Address each research question by describing

the type of statistical tests that will beperformed.

Include the name of the software used.



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Data Cleaning:

This is the process where you detect and

correct the errors.

Some could be from typing errors during data

entry or coding error.

For detection of errors-

Obtain descriptive statistics like frequency

counts, minimum, maximum, means, range,

and standard deviation. Obtain graphs like

histogram or scatter plot.



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More Jokes: The only time a pie chart is appropriate is at a

baker's convention.

Old statisticians never die, they just undergo atransformation.

How do you tell one bathroom full ofstatisticians from another? Check the p-value.

Did you hear about the statistician who made

a career change and became a surgeonspecializing in ob/gyn? His specialty washisterectograms.



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Data Types

Generally speaking, statistical techniques areoften determined based on the type of data.

The two major types of variables are

qualitative and quantitative variables. Qualitative variables:The data values are non-

numeric categories. Measurement scales are

Nominal- data are non-numeric group labelsOrdinal- values are ranked categories



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Quantitative variables: The data values are counts or numerical

measurements. It can be discrete/continuous.

The measurement scales are-

Interval- data values ranged in a real interval.

The difference, but not the ratio, of two values

is meaningful. Interval data has no absolute

zero.

Ratio- Both the difference and ratio of two

values are meaningful.



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Statistics (descriptive and inferential)

Descriptive statistics (Numeric and Graphic):

These includes summary statistics (mean,median, standard deviation, frequency) andgraphic tools (pie charts, bar charts, histograms,box plots, scatter plots)

For nominal data: Use frequency, crosstabs, barcharts and pie charts.

For ordinal data: Use frequency, crosstabs,

summary statistics, bar charts and pie charts.For continuous data: Use summary statistics,

histograms, box plots, and scatter plots.



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Estimation and Tests (Inferential statistics):

This is used to make comparisons betweentwo or more groups or study relationships.

These include point estimation, confidence

interval or interval estimation, and hypothesistesting.



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If you are interested in comparing group effects

For nominal or ordinal data: Use crosstabs (chi-square)

For continuous data (First, check for normality): For two group comparison, use independent t-test.

For three or more group comparison, use one-wayanalysis of variance (ANOVA).

For two or more factors, use multi-way ANOVA. If there are factors and covariates, use analysis of

covariance (ANCOVA).

If the same subject is measured more than one time, it

is a paired t-test for two time periods and it is arepeated measure ANOVA for more than two periods.



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If you are interested in the relationship between

two variables

For nominal data, use crosstabs, and choose

proper tests for nominal data.

For ordinal data, use crosstabs (chi-square

test), bivariate correlation such as Spearman

correlation coefficient.

For continuous data, use bivariate correlation

such as Pearson correlation.



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If you are interested in modeling a response

variable using predictor variables

For nominal data, use Logistic regression model ifthe response is a binary variable (that is only twopossible values such as yes or no). If the responsehas more than two categories, use multinomiallogistic regression.

For count data, use Poisson regression model ifthe response follows a Poisson distribution. In

general, one can use log-linear models for ordinaldata.

For continuous data, use regression analysis.



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Assumptions:

Most of statistical techniques require certain

assumptions.

Typically, for continuous response, the

assumptions may include:

Normality of the response variable.

Homogeneity of variance.

The relationship between Y and Xs is linear.

When assumptions do not hold, use

transformation or a non-parametric method.



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Some Nonparametric Methods

Chi-squaretests

For two independent samples comparison, use Mann-Whitney U or Kolmogorov-Smirnov Z. This is similar toindependent t-test.

For K independent samples comparison, use Kruskal-Wallis Hor Median. This is similar to ANOVA.

For two related samples, use Wilcoxonor Signtest forquantitative data; McNemarfor binary data andMarginal Homogeneity for multinomial data. This issimilar to paired t-test.

For K related samples, use FriedmanorKendalls W

measure of agreement or Cochrans Q for binary data.This is similar to Repeated Measure ANOVA.



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If you are interested in reducing the data

dimension

Use Cluster Analysis or Factor Analysis.

Cluster analysis can be applied to group variables

or cases. The cluster analysis for variables will

group the variables into small number of subsetsof variables based on the similarity of cases.

Factor analysis combines similar variables

together into a dimension that can be interpretedfrom the qualitative aspects of the study.



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Conclusions/Final Comments

You bought an expensive clothing material.

Do you look for an apprentice tailor to sew thematerial for you?

You look for an experienced tailor who is very

knowledgeable. When you decide to seek help for your data

collection and/or data analysis, you should notsettle for less (anybody).

Look for someone with adequate training instatistical methodology.

Mathematics Dept Statistical Consulting Unit



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Conclusions/Final Comments continued:

When test shows a significant effect, a common

misunderstanding is that the hypothesis has beenproven.

In a statistical test, if the outcome is inconsistentwith the research hypothesis, then the

hypothesis is rejected.

If the outcome is consistent with the researchhypothesis, the data is said to support the

hypothesis. Hypothesis is never proven but rather only

supported by the analyzed data.



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More Jokes:

A statistician can have his head in an oven and his feetin ice, and he will say that on the average he feels fine.

Numbers are like people; torture them enough andthey will tell you anything.

Statistics in the hands of an engineer are like a

lamppost to a drunk-they are used more for supportthan illumination. (Bill Sangster, Dean of Engineering,Georgia Tech.)

The statistics on sanity are that one out of every fourNigerians is suffering from some form of mental illness.Think of your three best friends. If they are okay, thenit is you. (Rita Mae Brown, for Americans)



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Thanks for your attention

This is the end of the presentation


Professor Felix Famoye

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