Hello. Welcome to week

four of this course on research methods.

My name is Dorcus Nyamai,

a research assistant at the Institute for Housing and Urban Development Studies and,

under the Urban Competitiveness and Resilience Specialization.

I'm delighted to take you through this session on research methods.

In the previous week,

you learned various research strategies including surveys,

experiment, and case study.

This week, we will learn about data and data collection methods.

At the end of this week,

you will understand the difference between primary and secondary data,

and between qualitative and quantitative data.

You will also learn about

qualitative and quantitative data collection methods and sampling methods.

So, what is data?

Data is information collected together for the purpose of analysis.

It is an integral path to research and the core that

ties theory to existing empirical reality.

Data can take the form of facts or statistics,

but also opinions, perceptions, and feelings.

Data is generally classified into qualitative and quantitative data.

Quantitative data is numeric by nature.

In other words, it is quantifiable.

For example age, population size,

and frequency of certain behaviors.

Qualitative data, on the other hand,

has no numeric meaning and is not quantifiable.

It could be in the form of words, or text,

photographs, sound recording, or videos and so on.

Data about opinions, perceptions,

explanations of behavior, and feelings are qualitative in nature.

The distinction between quantitative and qualitative data

isn't as hard as often is argued.

In a way in social sciences,

most quantitative data is used for statistical analysis and therefore,

to test or explain certain relationships,

while qualitative data gives new insights into some time still unknown variables.

Your research question, objective,

and strategy define whether you need qualitative or quantitative data.

It is good to know,

however, that in some cases,

qualitative data can be described and manipulated numerically.

Let me give you an example.

Suppose you are conducting a research on the perception of housing standards in a city.

You can interview inhabitants and collect

qualitative data on their opinions and perceptions.

But you may also choose

a quantitative approach using questions that can be answered on a scale

of say 1 to 5 where

positive perception is measured by the increase in value from 1 from 5.

Perception, in most cases, is qualitative.

However, by using a like at skills such as this one,

gives it a numerical value therefore making it quantitative.

Quantitative research is used to quantify variables of any form by generating

numerical data or data that can be transformed into usable statistics.

It uses measurable data to formulate facts,

to uncover patterns in research and generalize results from a large sample population.

Qualitative research, on the other hand,

is normally used to gain an understanding of reasons,

opinions, processes, and perceptions.

It uses a relatively smaller sample and is non statistical.

A mixed method using quantitative and qualitative methods is very common,

especially with case studies,

and can be helpful in the triangulation of data required to achieve validity.

Now, if we want to use quantitative data for statistical analyses,

data can be measured at different levels.

There are typically four levels of measurements namely;

nominal, ordinal, interval, and ratio.

We will look at each of them beginning with the nominal data.

Nominal data includes categorical values that cannot be ordered due to their nature.

For example, religion, ethnicity, and so on.

An example of a nominal variable is the variable religion.

The variable has a number of attributes for example Roman Catholic,

Protestants, Islam, Hinduism, Judaism, and so on.

For purposes of analyzing the results of this variable,

we arbitrarily assign the values one, two,

three and so on to the attributes or categories.

The level of measurement describes the relationship among these values.

In this case, we simply are using the numbers as

shorter placeholders for the lengthier text terms.

We do not assume that higher values means

more of something and low numbers signifies less.

The values are only used as a shorter name for the attribute.

This level of measurement is what we call nominal.

Another example from Richard's questionnaire categorizes

individuals from form of employment into public service employees,

private company employees, and self-employed.

He assigned the values one,

two and three to each of their responses.

Just as in the example of religion,

being self-employed does not mean you are

three times better than the public service employee,

or being a public service employee

has a higher priority over the private company employee.

Knowing the level of measurement helps you to integrate the data.

When you know that a measure is nominal,

like the one just described,

then you know that the numerical values are just short codes for the longer names.

Another level of measurement is ordinal.

Ordinal data has fixed number of categorical values that are ordered,

but cannot be compared in numeric size and differentiated between values.

For example, education level.

Again from Richard's questionnaire,

he defined five levels of education,

namely: no education, primary,

secondary, A-levels, master's degree.

In this example, a respondent with some education is better educated than one with

no education and a respondent with

master's degree is better educated than one with primary education.

Also, responses with categories like agree,

neutral, disagree, as well as age group are considered ordinal.

A third level of measurement is interval.

In interval data, the distance between attributes does have meaning.

For example, when we measure temperature in Fahrenheit,

the distance from 30 to 40 is same as the distance from 70 to 80.

The interval between values is interpretable.

Because of this, it makes sense to compute an average of an interval variable,

where it does not make sense to do so for ordinal scales.

But know that in interval measurement,

ratios do not make any sense.

Meaning, 80 degrees is not twice as hot as 40 degrees,

although the attributes value is twice as large.

Interval data lacks the absolute zero point.

What do I mean by this?

Well, zero means lack of in scales of measurement.

However, the zero in interval scale is

arbitrary and does not present lack of temperature,

but rather extremely cold in Fahrenheit and freezing in Celsius.

Finally, measurement of data can also be on a racial level.

In ratios, there is always an absolute zero that is meaningful.

This means that you can construct a meaningful fraction or ratio with a ratio variable.

Weight is a ratio variable.

In applied social research,

most count variables are ratio.

For example, the number of clients in the past six months.

Why? Because, you can have zero clients and because it is meaningful to say that,

we had twice as many clients in the past six months as we did in the previous six months.

Well, this was our introduction to week four of this course on research methods.

In this video, we discussed the different types of data,

both qualitative and quantitative,

as well as the various level of measurement of the data.

Remember, that the main differences between

qualitative and quantitative data consists: one,

in respect to the purpose of research, two,

the necessary data sample size, three,

the methods of data collection and four,

data analysis being either statistical or qualitative.

And finally, with regard to outcomes,

whether the results are generalizable to a large population or in-depth.

In this week, you will learn about

quantitative and qualitative data collection methods

and sampling. Thank you for watching.