Introduction of Research Methods

What is Research?

Everywhere knowledge is incomplete and problems are waiting to be solved. We address the void in our knowledge and those unresolved problems by asking relevant questions and seeking answer to them. The role of research is to provide a method for obtaining those answers by inquiringly studying the evidence within the parameters of the scientific method.

Research is the systematic investigation into existing or new knowledge. It is used to establish or confirm facts, reaffirm the results of previous work, solve new or existing problems, support theorems, or develop new theories. Research is not mere information gathering and not mere the transportation of facts from one location to another location. It is not also the rummaging for information and is not a catch word used to get attention.

Research is getting to know a subject by reading up on it, reflecting, playing with the ideas, choosing the areas that interest we and following up on them. Research is the way we educate ourselves. Some definitions of research are;

1.      Shuttleworth, M. (2008), "In the broadest sense of the word, the definition of research includes any gathering of data, information and facts for the advancement of knowledge."

2.      Creswell, J. W. (2008), "Research is a process of steps used to collect and analyze information to increase our understanding of a topic or issue". It consists of three steps: Pose a question, collect data to answer the question, and present an answer to the question.

Additionally, research articulates the genuine and research problem, adopts the scientific process and contributes the body of research. There are several forms of research: scientific, humanities, artistic, economic, social, business, etc

1.     Scientific research relies on the application of the scientific method, a harnessing of curiosity. This research provides scientific information and theories for the explanation of the nature and the properties of the world. Scientific research is a widely used criterion for judging the standing of an academic institution, such as business schools, but some argue that such is an inaccurate assessment of the institution.

2.     Research in the humanities involves different methods such as for example hermeneutics and semiotics, and a different, more relativist epistemology. An example of research in the humanities is historical research, which is embodied in historical method.

3.     Artistic research, also seen as 'practice-based research', can take form when creative works are considered both the research and the object of research itself.

Purpose of Research

Purpose of the research can be discussed in five categories follows,

1.     Basic Research: collection and analysis of data to develop or enhance theory. Some examples of learning theories are; constructivism, mastery learning etc.

2.     Applied Research: Collection and analysis of data to examine the usefulness of theory in solving the practical educational problems. For example; examining the effectiveness of a computer based algebra program developed around a mastery learning approach.

3.     Evaluation Research: The collection and analysis of data to make decisions related to the merit or worth of a specific program. A critical appraisal or assessment; a judgment of the value, worth, character, or effectiveness of something; measurement of progress. There are two types of evaluation research. a) Formative evaluation is designed to inform and improve a program while it is being developed or implemented. b) Summative evaluation, is designed to make decisions regarding the overall quality of the program being evaluated

4.     Research and Development: Research and development is often scientific or towards developing particular technologies and is frequently carried out as corporate or governmental activity.The development of effective products for the use in schools. For example, the development of smart board to enhance a teacher’s use of technology in the classroom.

5.     Action Research: The collection and analysis of data to provide a solution to the practical, valued program of educators within own school or organization. For example, how can the disciplinary policies are enforced consistently on our schools.

Major Steps of Conducting Research

Research is often conducted using the hourglass model structure of research (Trochim, W.M.K, 2006). The major steps in conducting research are:

1.         Identification of research problem

2.         Literature review

3.         Specifying the purpose of research

4.         Determine specific research questions or hypotheses

5.         Data collection

6.         Analyzing and interpreting the data

7.         Reporting and evaluating research

Generally, research is understood to follow a certain structural process Though step order may vary depending on the subject matter and researcher, the following steps are usually part of most formal research, both basic and applied:

What is not Research?

Different ideas regarding the understanding ‘what is not research’ can be elaborated as,

Research is not mere information gathering and not mere the transportation of facts from one location to another location. It is not also the rummaging for information and is not a catch word used to get attention.

Elements of Research

1.      Research originates with a question or problem like; why, what is cause of that, what does it mean. For instant; what might be the cause of securing lower marks in mathematics by girls of 10 class students?

2.      Research requires a clear articulation of a goal like; what we intended to do is a goal of the research. For example; to identify the underlying causes of getting lower marks in math might be the goal of research.

3.      Research follows the scientific procedure or plan like; the logically designing and planning of the research is a must crucial effort of any research. Like, as per our research problem, where are the data of marks in math of girls we can get? Do these data address the research problem? How we can access the data? What procedure we adopt to collect the data? etc.

4.      Research always divides the principal problem in to more manageable sub-problems. For example; what is the class room attendance percentage of girls in math class? How many hours of everyday class is being conducted for math subject in class 10?, these might be the some sub problems of the research.

5.      Research is guided by the scientific research problem, question or hypothesis. For example; there is less scores in math by the girls of 10 class students of a school as mentioned above. We begin to construct a series of reasonable guesses (i.e. hypothesis) for the cause of the failure (getting lower number in math) in mathematics. Like; are all girls attending the class regularly? Is the mathematics teacher regular in teaching the students?

6.      Research requires the collection and interpretation of data in attempting to resolve the problem that initiated the research. Thus, research is cyclical or, more exactly, helical.

Data, Information and Knowledge

The terms data, information and knowledge are frequently used for overlapping concepts. The main difference is in the level of abstraction being considered. Data is the lowest level of abstraction, information is the next level, and finally, knowledge is the highest level among all three (Mitra, A., 2011). Data on its own carries no meaning. For data to become information, it must be interpreted and take on a meaning. For example, the height of Mt. Everest is generally considered as "data", a book on Mt. Everest geological characteristics may be considered as "information", and a report containing practical information on the best way to reach Mt. Everest's peak may be considered as "knowledge".

Data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

Information is data that has been given meaning by way of relational connection. This "meaning" can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.

Knowledge is the appropriate collection of information, such that its intent is to be useful. Knowledge is a deterministic process. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which we can take knowledge and synthesize new knowledge from the previously held knowledge.

Wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). Wisdom is, the process by which we also discern, or judge, between right and wrong, good and bad.

Knowledge is dichotomous: It always brings the contrast meaning like; yes or no, right or wrong, good or bad.

The Ways of Knowing

In this context, arguments about “ways of knowing” are really arguments about “ways of defending knowledge claims.” If the best findings of science suggest the Earth is over four billion years old, while the Bible strongly implies that it is less than ten thousand years, then among educated people it is the Bible that must yield. When dealing with empirical claims about nature, science is a far more reliable way of knowing than is reading the Bible.

We will be discussing about the different ways of knowing something as follows,

1. Personal experience: It means relying on one’s knowledge of prior experience
2. Tradition: It means the way of doing things they have always been done
3. Expert or Authorities: It means relying on the expertise or authority of others
4. Deductive reasoning: It means reasoning based on the rules or laws
5. Inductive reasoning: It means reasoning based on the observation or experience

Deductive and Inductive Reasoning

Inductive and deductive reasoning are two methods of logic use to arrive at a conclusion based on the information assumed to be true. Both are used in research to establish hypothesis. Arguments based on experience or observation is best expressed inductively, while arguments based on laws or rules are best expressed deductively.

Deductive approach begins with a general idea such as theory, laws and principles. An inductive approach begins with specific things, observation of individual cases. A deductive argument is an argument whose premises make its conclusion certain. Inductive argument is an argument whose premises make its conclusion likely.

Research Paradigms

There are two major research designs: qualitative research and quantitative research. Researchers choose one of these two tracks according to the nature of the research problem they want to observe and the research questions they aim to answer:

1.      Qualitative research: Understanding of human behavior and the reasons that govern such behavior. Asking a broad question and collecting word-type data that is analyzed searching for themes. This type of research looks to describe a population without attempting to quantifiably measure variables or look to potential relationships between variables. It is viewed as more restrictive in testing hypotheses because it can be expensive and time consuming, and typically limited to a single set of research subjects. Qualitative research is often used as a method of exploratory research as a basis for later quantitative research hypotheses.

2.      Quantitative research: Systematic empirical investigation of quantitative properties and phenomena and their relationships. Asking a narrow question and collecting numerical data to analyze utilizing statistical methods. The quantitative research designs are experimental, correlational, and survey or descriptive (Creswell, J. W., 2008). Statistics derived from quantitative research can be used to establish the existence of associative or causal relationships between variables.

3.      Mix Method: It uses both the qualitative and quantitative methods. Pragmatism worldwide philosophy followed this paradigm. Researcher need to establish a purpose for their mixing; rationalize for the reason why both qualitative and quantitative data need to be mixed in the first place. Mixed method study can apply elsewhere like; socio-cultural, historical, economic, political thus it is theoretical lens that reflective of social justice and political aim.

THE BASIC TERMINOLOGIES

Data: The terms data, information and knowledge are frequently used for overlapping concepts. The main difference is in the level of abstraction being considered. Data is the lowest level of abstraction, information is the next level, and finally, knowledge is the highest level among all three (Mitra, A., 2011). Discrete data are set in nominal, ordinal scale for example age of students is16, 17, 19, 20, 21, 22 years. Continuous data are set in certain categories like interval scale e.g. age from 10-19, 20-29, 30-39 etc.

Measurement Scale

A scale is a tool which is used to distinguish the individual on the basis of variables interest in our study. Therefore, we use different scales to measure different variables on the basis of their degree of precision. They are explained as

1)      Nominal scale

A nominal scale classifies objects into different categories based on some defined characteristics and then the number of objects in each category is counted.  In other words, it is a classification system that places people, object, or other entities into different categories and counting their frequency of occurrence.  Thus, the categories must be non-over lapping or mutually exclusive. For instance, a respondent’s race classified as black cannot be classified as white.  Therefore, nominal data are not graded, ranked or scaled for qualities such as worse or better, more or less, more or less and high or low but they are always grouped into separate categories  to indicate the sameness or difference with respect to the given quality or characteristics. It only tells us to which group a subject belongs, but they do not provide any quantitative information about the subject. For instance, the gender variable may tell us that subjects are males while others are females. But it doesn’t tell us that some subjects posses more of a specific characteristic relative to others.

.Gender, ethnicity, hair color are some of the example of variables measured on a nominal scale. The important point is that there is no logical ordering of the categories in a nominal measurement but shows only the difference.

Properties:

·         Data categories are mutually exclusive: that is, an object can belong to only one category.

·         Data categories have no logical ordering

2)      Ordinal scale

An ordinal scale not only shows classification of objects or characteristics but also shows logical order of categorised objects. In other word, ordinal measurement provides information about the ordering of categories, but doesn’t indicate the magnitude difference between numbers. The objects can be out into order on the basis of some characteristics but they cannot be given values that represent the degree of differences on that attribute.  Thus, the values of ordinal scale represent a hierarchy or order of levels with respect to the construct of effectiveness i.e. the agent ranked “1” is perceived as being more effective that the agent ranked “2”, and agent ranked “2” is more effective that one ranked “3” and so forth.

 For example, in a letter grading system (as A, B, C, D and F) A has higher level of achievement than B. But still, we cannot infer that the difference between an A and a B is the same as between a B and a C.  The properties of ordinal data are:

·         Data categories are mutually exclusive

·         Data categories have some logical order

·         Data categories are selected according to the particular characteristics they possess.

3) Interval Scale

Interval scale not only tells us about the ordering of categories, but also shows the exact distance between them i.e. position and distance. Therefore, it reflects equal differences in the characteristics measured, that is equal unit is established in the scale. Hence, it not only groups individuals according to certain categories but it also measure the magnitude differences n the performances among the individuals. The important point is that in all interval scale 0 is just another point on the scale, that is, 0 is not necessarily the starting point of the scale, e.g., temperature. The properties of the interval data are:

§  Data categories are mutually exclusive

§  Data categories have some logical order

§  Data categories are selected according to the particular characteristics they possess

§  Equal differences in the characteristic are represented by equal differences in the numbers assigned to the categories

§  The point 0 is just another point on the scale

4. Ratio Scale:

It represents an absolute true zero point that reflects an absence of the characteristics. It not only measures the equality of the differences between any two points or variables on the scale but also measures the proportional amounts of the attributes which the two objects possess. The properties of ratio data are:

§  Data categories are mutually exclusive

§  Data categories have some logical order

§  Data categories are selected according to the particular characteristics they possess

§  Equal differences in the characteristic are represented by equal differences in the numbers assigned to the categories

§  The point 0 reflects an absence of the characteristics.

In nutshell, Nominal scale only classifies the variables without ordering them whereas ordinal scale not only shows the classification but also the orders of the objects. In the same way, interval scale categorizes, orders and established an equal unit in the scale but on the other hand ratio scale also categorizes, orders, establishes an equal but contains a true zero point. Therefore we have to be clear of how variables are measured in the study.

Types of data

There are different types of variables as Discrete, Continuous and Binomial. Discrete variables generally take integer value. That is to say it takes only certain values of objects as whole numbers, for example, 1, 2, 3, 4, 5 or the number of children, or family size. That is why discrete variables are always measured across a set of fixed values, such as age in years. On the other hand, Continuous data can take any value. It means a variable that is not restricted to particular values, for instance, height or weight, 60-61, 60.1, 60.2, and 60.3.  That is why continues variables are measured along a continuous scale which can be expressed or divided into fractions or in decimals. While Binomial can take only two value, for example 1 or 0, yes/no

Parametric and Non-parametric Statistics

Parametric statistics are more powerful and more flexible than nonparametric used with interval and ratio variables and normality of population. We can use to describe the data with some commonly used parameters, particularly the mean and standard deviation.

            Non-parametric statics are not based on the estimation of population parameters. It means it often uses ordinal or nominal variables meaning is it doesn’t depend upon numerical but it depends on a ranking or order of things, for instance, consumers demanding ranges from like to dislike i.e. ordinal data.

Variable

A variable refers to some specific characteristic of a subject that assumes one or more different values. It is characteristic of person, thing, groups or objects. Thus variable is a symbol to which numerals or values are assigned. In other words, a variable is any entity that can take on different values. Therefore, an attributes is a specific value on a variable. For example, the variable sex or gender has two attributes or values as male and female, productivity and job satisfaction both are variables which have different values. A variable thus is defined as anything that can take on differing or varying values.

There are different types of variables as

a.       Dependent variable:

A variable is called dependent variable if its values depend upon the other variables. It means the value of the dependent variable always depends upon another variable i.e. on the independent variable. Therefore, the dependent variables are affected by the independent variables.   

For instance, education (training)                                     income (productivity)

Independent variable                          Dependent variable

Thus, dependent variable is used to describe or measure the problem under study. It is always explained.

b.      Independent variable:

A variable is called independent variable if it is not influenced by another variable under study. But it influences the dependent variable. So, the independent variable is what you or nature manipulates.  Any changes in the dependent either positive or negative are due to change in the independent variable.  So, the independent variables are used to as the basis of predication whereas the dependent variable is being predicted. Independent variables are used to describe or measure the factors which are thought to cause or at least to influence the problem.  It is always explanatory.

3. Intervening variable

It is  third construct variable that influences the nature and degree of relationship between independent and dependent variables i.e. if agriculture output is increased by irrigation facility then fertilizer, improved seed, can be other intervening variables to help increase thee agriculture output.

Descriptive Statistics

Statistics consists with collecting and describing a set of data to yield meaningful information. Some of the descriptive statistics are bar diagram, pie chart and graph or curve. Each of them are discussed below

1. Bar diagrams: They represent the data by means of bars of rectangles of equal width. The length of the bars represents the given figures and the width may be of any size. So they are also called one dimensional diagram. We will consider four types of bar diagrams.

(i)                 Simple bar diagrams.

(ii)               Multiple bar diagrams.

(iii)             Sub-divided bar diagrams.

(iv)             Percentage bar diagrams.

Bar diagrams: They represent the data by means of bars of rectangles of equal width.  The length of the bars represents the given figures and the width may  be of any size. So they are also called one dimensional diagram.  We will consider four types of bar diagrams. (i)                 Simple bar diagrams (ii)               Multiple bar diagrams (iii)             Sub-divided bar diagrams (iv)             Percentage bar diagrams

2.  Pie chart: Take a circle and divide it into several parts or sectors to represent the total magnitude and the various parts into which it is broken up. Draw a circle to represent the whole and divide it into sectors to represent each of its components. Such a diagram is called a pie diagram.

Sex ratio of the respondents.

3. Graphs:  Graphs are pictorial forms to represent the collection of data. They can be categorized into different types on the basis of different criteria. In the graphs one variable is presented on the X-axis and another variable on the Y-axis.

1.      Measures of Central Tendency:

Any measure indicating the centre of a set of data, arranged in increasing or decreasing order of magnitude is called a measure of central tendency. It is also called measure of central location. Mean median and mode are measures of central tendency. Their main purpose is to get the single value that describes the characteristics of entire group and to facilitate comparison.

Different Measurements of Central tendencies

	N	Range	Minimum	Maximum	Mean		Std. Deviation	Variance
	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Statistic
Reading Score	200	48.00	28.00	76.00	52.2300	.72499	10.25294	105.123
Writing Score	200	36.00	31.00	67.00	52.7750	.67024	9.47859	89.844
Valid N	200

1.  Mean:

Arithmetic mean is very powerful measures of central tendency. It is defined as the sum of all items divided by the number of items.

The common formula of arithmetic mean:

x¯ is symbol of mean.

 X  is number(value).

N isnumber(total).

∑  is summation of numbers.

X=x_{1+ X2}..............X_n/n

Descriptive Statistics
	N	Mean	Std. Deviation	Variance
Reading Score	200	52.2300	10.25294	105.123
Valid N	200

2. Median:

The middle item is known as the median. Median is the value which divides the distribution into two equal parts when the data are arranged in increasing or decreasing order. For example, in the series 10, 15, 20, 25, 30 the value arranged in ascending order and 20 is the middle most item. So 20 is the median. For calculation of median in individual series,

Median = N+1/2

Where N is the number of item

3.  Mode:

Mode of a set of observations is that value which occurs with a greatest frequency. The mode may not exist, and even if it does exist, it may not be unique. When observation occurs with the same frequency, mode does not exist.

4.  Range:

It is defined as the difference between the largest (maximum) and the smallest (minimum) items in the distribution. Thus, it is defined by following formula.

Range = L-S,

Where, L= Largest item

S = Smallest item

Range

	N	Range	Minimum	Maximum
Reading Score	200	48.00	28.00	76.00
Valid N	200

5. Coefficient of Variation: 

The relative measure of dispersion based on standard deviation is called coefficient of standard deviation. Thus,

                      Coefficient of standard deviation = σ / Х

 100 times coefficient of standard deviation is called coefficient of variation. It is denoted by C.V. Thus,   C.V. = σ / Х x100 %

6. Standard Deviation:

It is the positive square root of the arithmetic mean of the squares of the deviations of the given values from their arithmetic mean. It gives greater weight to extremes values. Therefore, it is regarded as the best and the most powerful measure of dispersion.

7. Measures of Dispersion:

The degree to which numerical data tend to spread about an average value is called measures of dispersion. They are called measures of variation. Range, variance and standard deviation are measures of dispersion.

Statistics: It is a science of collecting, summarizing, presenting, and interpreting data and of using them to test hypothesis. Statistics can roughly subdivide into two areas, which are given below.

Descriptive statistics includes the collection, presentation and description of numerical data. Inferential statistics means technique of interpreting the values resulting from descriptive techniques and then using them to make decision and in order to generalize them. It describes the nature of data according to sample population. Normal curve is the foundation base of inferential statistics.

Distribution

Distribution of data is always observed in data analysis process. It is used to analysis in inferential statistics. Normal distribution (symmetrical distribution) and skeweness (left skewed distribution) and right skewed distribution pattern always happen in distribution.

Normal distribution

Normal distribution of data can study under bell shape, Norma curve. In this curve, fifty percent of the score fall above the mean and fifty percent fall below the mean, mean/median/mode are the same value, so most participant scores falls nearer the mean. Basic parameter of normal curve are ± 1sd = 68%, ±2sd = 95.5%, ± 3sd =99.7%. Every numeric variable has approximately normally distributed. Main importance of normal distribution is checking the normality of data whether they are normally distributed or not. In inferential statistics, it can help to interpretation of data according to given parameters.

Skewness distribution means lack of symmetry or unsymmetrical distribution of data’s. Skewness is present if the value of mean, median and mode do not coincide, i.e. mean ≠ median ≠ mode, frequencies are not equally distributed at points of equal deviation from the mode etc. Data is not always distributed in normal curve symmetrically thus skeweness refer to distributing data not symmetrically or either distributed in positive skewedness (Mo> Md > M) or in negative skewedness (M> Md>Mo).

Kurtosis distribution observes height, pickness and flatness of the distribution. There are always three types of pickness. Pickness with normal curve (mesokurtic), High peakness (Lapto kurtic) and low peak (platykurtic). Skeweness and kurtosis are both for measurement for shape under inferential statistics because they can help to analysis data under given parameters and shapes.

Population and sample: 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. The group you wish to generalize to is often called the population in your study. This is the group you would like to sample from because this is the group you are interested in generalizing to. The listing of the accessible population from which you'll draw your sample is called the sampling frame. The sample is the group of people who you select to be in your study. The distribution of an infinite number of samples of the same size as the sample in your study is known as the sampling distribution.