ONLINE DISCUSSION/QUIZ-7-11-2022–ECO. 591-Research Methods

1.Researchers have argued that decision-making is the process of selecting the best alternative from the available set of alternatives. Again, management is chiefly concerned with decision-making and its implementation. However, these decisions should be based on appropriate studies, evaluations and observations. As policy makers, research provides us with knowledge and skills needed to solve the problems and to meet the challenges of a fast paced decision-making environment. As the Special Adviser to Mr. President on Research Methods and Policy Analysis, you have been invited to the Centre for Policy Research and Economic Analysis to present a paper on “The Critical Role of Research in Decision Making and Policy Formulation in Nigeria”. Not forgetting the various activities that occur in decision making and the critical role of research, what would you tell your audience?
answer:
While decision making is been regarded as the cognitive process resulting in the selection of a course of action among several possible alternatives, all the possible alternatives as well as their inherent implications have be explored using a scientific method called “Research”.
Findings from research form the bases on which decision makers understand the issue at hand after which options are framed appropriately and considerations are made as to the next course of action. decision making is complex, both because it is context dependent and because it is often influenced by the need to act quickly in sometimes less than ideal situations with relatively littles access to information. Recognizing this complexity, evidence-informed decision making has been described as an approach that aims to ensure that decisions are influenced by the best available research evidence, while acknowledging the other factors that influenced it. Oxman et al, (2009)
THE DECISION MAKING PROCESS
Decision making process involves the following
IDENTIFY THE DECISION: To make a decision, you must first identify the problem you need to solve or the question you need to answer. Clearly define your decision. If you misidentify the problem to solve, or if the problem you’ve chosen is too broad, you’ll knock the decision train off the track before it even leaves the station. If you need to achieve a specific goal from your decision, make it measurable and timely.
GATHER RELEVANT INFORMATION: Once you have identified your decision, it’s time to gather the information relevant to that choice. Do an internal assessment, seeing where your organization has succeeded and failed in areas related to your decision. Also, seek information from external sources, including studies, market research, and, in some cases, evaluation from paid consultants. Keep in mind, you can become bogged down by too much information and that might only complicate the process.
IDENTIFY THE ALTERNATIVES: With relevant information now at your fingertips, identify possible solutions to your problem. There is usually more than one option to consider when trying to meet a goal. For example, if your company is trying to gain more engagement on social media, your alternatives could include paid social advertisements, a change in your organic social media strategy, or a combination of the two.
WEIGH THE EVIDENCE: Once you have identified multiple alternatives, weigh the evidence for or against said alternatives. See what companies have done in the past to succeed in these areas, and take a good look at your organization’s own wins and losses. Identify potential pitfalls for each of your alternatives, and weigh those against the possible rewards.
CHOOSE AMONG ALTERNATIVES: Here is the part of the decision-making process where you actually make the decision. Hopefully, you’ve identified and clarified what decision needs to be made, gathered all relevant information, and developed and considered the potential paths to take. You should be prepared to choose.
Take action: Once you’ve made your decision, act on it! Develop a plan to make your decision tangible and achievable. Develop a project plan related to your decision, and then assign tasks to your team.
Your Decision: After a predetermined amount of time which you defined in step one of the decision-making process take an honest look back at your decision. Did you solve the problem? Did you answer the question? Did you meet your goals? (Lucid chart)

2.During one of his interactive sessions with the members of the Parliament, Mr. President made the following conspicuous statement, “To produce a globally- accepted empirical research paper that can be published in a high-ranking impact factor journal, the research problem must be clinically defined, with well laid out objectives and matching methodology. And of course without any dilly-dally or shilly-shally, the literature review should be analytical, rigorous, argumentative, theoretical, empirical, integrative and comparative”. As the Special Adviser to Mr. President on Research Methods and Development Strategy, clearly discuss and analyze the above statement.
Answer:
For research paper to be globally accepted and meet all the necessary requirements that will deem it fit to be published in a high ranking journal, the research process should;
HAVE A CLINICALLY DEFINED RESEARCH PROBLEM: the first stage of the research process is primarily concerned with a clear cut definition of the research problem. His section is concerned with the unambiguous and clear painting of the picture of the research problem.
A clinically defined research problem is important because is serve as the foundation on which every other process of research is based on.
A WELL LAID OUT OBJECTIVES: objectives have been defined as the goal of the researcher, it is important to note that an objective with no clear cut scope will be impossible to accomplish and this brings us to what is referred to as the broad and specific objective. Will the broad objective is concerned with the overall thrust of the research work, a specific objective is concentrated on that actual part of a whole that the research is going to understudy.
A MATCHING METHODOLOGY: The methodology is like that tool that will be used in accomplishing the task of the research, while different tools serve different purposes, a research is tasked with the job to identify a used the methodology that will best accomplished the purpose of the research.
A THEORETICAL, EMPIRICAL AND CONCEPTUAL LITERATURE REVIEW: A well conducted research paper is one that its literature review is not just a list of papers, it is one that has a literature review is analytical, rigorous, argumentative, theoretical, empirical, integrative and comparative

3Outline the various sections that will appear in an MSc Thesis Proposal in your Department.
Answer:
The various sections that will appear in an M.Sc. thesis proposal in economics department are:
Title: the title of the research work
Abstract: this is a compressed version of whole research work. The abstract gives information about the type of data used, scope of the research, type of data analysis used, findings, conclusions and recommendations
Introduction (chapter one): introduction is comprised of
Background of study:
Statement of the research problem
Research question
Objective of the study
Hypothesis of the study
Scope of study
Literature review (chapter two): this section comprises of:
Conceptual literature review
Theoretical literature review
Empirical literature review
Methodology (chapter three): this section is comprised of:
Research design
Model specification
Method of data analysis
Source and type of data
Result presentation and discussion (chapter four). This section is comprised of:
Data analysis
Result presentation and interpretation
Discussion and findings
Summary, conclusion and recommendations (chapter five) the last section which contains the following:
Summary
Conclusion
Policy implication and recommendation
Suggestion for further studies

4. One of the preliminary pages in the MSc thesis is the abstract. Suppose the title of your MSc thesis is “Macroeconomic Determinants of Industrial Output in ECOWAS sub-region”. Prepare a sample abstract on this title.
Answer:
The research work understudies the Macroeconomic determinants of Industrial output in ECOWAS sub-region particularly five Anglophone and five francophone member states. Panel data on GDP, Export, Investment, government expenditure on education, from 1991 to 2021 from Nigeria, Senegal, Ghana, sierra Leone, Togo cote d’Ivoire, Liberia, Mali and Niger was collected and analyzed using OLS and it was found out that export, export exerts significant evert on the industrial output all the ECAWAS countries, also, export, investment and government expenditure on education also exert positive effect on the industrial output of the examined countries.

5. Following the 6th edition of the APA referencing style, provide a sample reference list containing: a book with one author; a book with ten authors; fifth edition of a book with three authors; a book chapter; a journal article with two authors; two journal articles published by the same author in a given year; an MSc thesis; a conference paper; and a document on the internet.
Answer
Information from the table below is used for a reference list containing: a book with one author; a book with ten authors; fifth edition of a book with three authors; a book chapter; a journal article with two authors; two journal articles published by the same author in a given year; an MSc thesis; a conference paper; and a document on the internet. Notes that the text in the table are not arranged in an alphabetical order.
Author(s)name
Year of publication
Book/journal Title
Publisher
Edition
Editors
Book chapter

Osuala, A.E.
2020
Commodity and Derivative Markets
Great Digital Printing Press, Nigeria

Sekpe, H.A., kpesue, J. F., Omah, M.C., Ike, L., Abdul, I., Orji, T., … Mba, C.I.
2022
The Strength in Unity
Enugu, Nigeria: Evans Brothers.

Huleji, P., Dangara, J. s., Mark, D.
2021
The Nigerian Economy
Lokoja Nigeria: Confluence Prints.
(5th Ed)

Huleji, J.
2022
Ten laws of Love
Lokoja, Nigeria: Confluence Press.

Samson, J.J., & Eze, C.
Selflessness

Sokwo, J. & Stephens, J.
2019
Tax law in Nigeria
The Nigerian Judicial Review, 5(3), 10-15.

Kalu, M.
(2020a)
Capital market Volatility
Journal of Economics and Applied Research, 5(5). 69-72

Kalu, M.
(2020b))
An Appraisal of CBN-CENEF, UNEC
Journal of Economics and Applied Research, 7(9). 5-10

Odoma, M.
2015
The Nigerian Capital Market Capitalization
M.Sc. Thesis

Akpesue, J.F.
2018
Then Effect of the Naira Redesign on Nigeria’s Foreign Exchange Reserve
Conference proceedings of CBN-CENEF Enugu Campus(pp. 20-45)

Jude, S.
2022
A guide on how to trade bitcoin
retrieved on line from: http://www.xyz.com

Reference List
Akpesue, J.F. (2018). Then effect of the naira redesign on Nigeria’s foreign exchange reserve.

Conference Proceedings of CBN-CENEF Enugu Campus (pp. 20-45).

Huleji, J. (2022). Selflessness. Samson, J.J., & Eze, C. (Eds). Ten laws of Love. Lokoja, Nigeria:

Confluence Prints

Huleji, P., Dangara, J. s., Mark, D. (2021). The Nigerian Economy (5th Ed). Lokoja,

Nigeria: Confluence Prints.

Jude, S. (2022). A guide on how to trade bitcoin. retrieved on line from: http://wwww.xyz.com

Kalu, M.(2020a). Capital market Volatility. Journal of economics and applied research, 7(5). 69-72

Kalu, M.(2020b.). An Appraisal of CBN-CENEF, UNEC. journal of economics and applied research,

7(9). 5-10.
Odoma, M. (2015). The Nigerian capital market Capitalization (Master’s Thesis). University of

Nigeria Nsukka,Enugu, Nigeria.

Osuala, A.E. (2020). Commodity and Derivative Markets. Nigeria: Great Digital Printing Press.

Sekpe, H.A., Akpesue, J. F., Omah, M.C., Ike, L., Abdul, I., Orji, T. … Mba, C.I. (2022). The strength

in unity. Enugu, Nigeria: Evans Brothers.

Sokwo, J. & Stephens, J. (2019). Tax law in nigeria. Journal of Nigerian judicial

review, 5(3), 10-15

6. Research is an endeavor to discover answers to intellectual and practical problems through the application of scientific method. It is a systematized effort to gain new knowledge or to provide information for solving a problem It is also a careful inquiry or examination to discover new information or relationships and to expand and to verify existing knowledge. Discuss this comprehensively.

7. Comprehensively discuss the characteristics and types of research.
Answer:
Though not exhaustive, the following are the characteristics of a scientific research:
Valid and verifiable: scientific research should be such that conclusions are drawn from correct and verifiable facts. A good research should produce the same result when conducted by another research using the same research design and parameters.
systematic and precision: a scientific research should be carried out following a fixed plan or system in the process of collecting, analyzing and presenting findings. It should also be precise; this connotes that result so obtained should be representative of the population studied
empirical and rigorous: a scientific research should hang on evidence gathered through observation and experiment. It must also have a good theoretical backup.
Purposeful: a scientific research should be an endeavor that has a clearly stated objective which it aims to achieve.
Generalizability: conclusions and finding of scientific research should have a broad scope Applicable not just in one area or domain.
Objectivity: a scientific research should be free from all form of biases; result should be based on hard evidence from empirical analysis of the case study.

Kutari R. (2004) opined that research can be looked at from three different perspectives, these perspectives or viewpoints are;
Mode of application of findings
Mode of inquiry and
Objective of the study
He when further to say that this classification of the types on the basis of these perspectives is not a mutually exclusive one, that a research study classified from the perspective of mode of application of findings can also be classified from the perspective or viewpoint of mode of inquiry as well as objective of the study.
In light of the above, we can identify the following types of research
Type of research based on the of application of findings
theoretical research: this type of research is also called basic or pure research and it undertaken for the main purpose of adding to existing knowledge. It aims at unravelling issue surrounding a particular phenomenon, making use of scientific procedures to get to proven conclusions that become laws and theories. Most of the theories in the various field of studies are product of theoretical research.
Applied research: this is a research carried out to solve real or practical problems. It relies on findings from basic research in the forms of existing theories, principles and law to critically analyze the said problem and proffering solution. A research that investigates ways to treat or cure a particular disease is a good example of applied research as it seeks to find a solution to a problem.
Type of research based on mode of inquiry
Quantitative research: this is a type of research where numeric data are collective and analyzed. It entails the use of scientific and systematic approaches to find patterns and develop models which can be used to make predictions and generalizations. A research carried out to investigate the average income of CBN-CPP lectures is said to be a quantitative research as it involves collecting and analyzing numerical data(salaries).
The approaches to quantitative research are:
Inferential approach: this approach entails collected sample from a population, analyzing the sample and making generalization about the population from sample result.
Simulation approach: here, a model or an artificial environment is constructed within which needed information and data generated and analyzed.
Experimental approach: this approach entails controlling the research environment where variables are manipulated in order to discover how they interact with one another.
Qualitative research: this the is the collection and analysis of non-numerical data; it is an endeavor that seek to describe or order or rank rather than measure a phenomenon. It deals with the subjective rather than objective evaluation of views, attitudes, opinions, behaviors and attributes. Quantitative research employs the following techniques of data collection:
Observation
Interview
Focus group
Survey
Mixed method research: mixed method research is an approach to inquiry involving collecting both quantitative and qualitative data, integrating the two data and using distinct designs that may involve philosophical assumptions and theoretical frameworks (Creswell,2013).
The two forms of data are integrated in the research design, through merging the data, explaining the data, building from one database to the other or embedding the database with a larger framework.
The three types of mixed method design are
Convergent design
Exploratory sequential design
Explanatory sequential design
Type of research based on objective
Descriptive research: this is a type of research that describes the state of affair as they exist, it attempts to scientifically describe or paint a picture of a situation, event, system or phenomena as well as fact findings of different kinds, for example, a descriptive research may information about the way of life of a tribe or a religion. The main purpose of this kind of research is to make available up to date information of an event or phenomena.
Explanatory research: explanatory research also known as cause and effect research attempts to explore how things are related by way of cause and effect. It tries to explain how one event or phenomena affect the other. An attempt to find out if higher income is translated to higher savings is a good example of explanatory research.
Exploratory research: this is a study into an area where little is known or to investigate the possibility of carrying a particular research study. Exploratory research entails investigation for better understanding of a problem without coming to any conclusion. Findings made from exploratory research serve as bases for further research.
Other types of research
Action research: Action research is a type of research that entails examining actions and evaluating or assessing the effectiveness of these actions on bringing about an expected outcome and choosing a course of action based on findings and conclusions of the research. Example: A farmer learns a new method of planting yam and he uses the method to plant his yam and at harvest, he discovered that his yield drop by 30 percent. The farmer will change the method adopted.
Field research: this form of research entails collecting data or analyzing phenomena in their natural setting, here, the researcher is involved in an onsite observation of the case study.
Longitudinal research and cross sectional research: longitudinal research is a type or research carried out to gauge changes over time without controlling or manipulating any variable. Some types of longitudinal research include:
trend study
cohort study and
Panel study
Cross sectional research on the other hand studies groups of homogenous character at one point.

8. At each operational step in the research process you are required to choose from a multiplicity of methods, procedures and models of research methodology which will help you to best achieve your objectives. In view of this clearly discuss the research process and what it entails.
Answer:
The research process entails eight distinct processes which must be carried out chronologically: these processes are:
Formulation of the research topic or problem: this first process entails clearly stating the issue that calls for the research work. This issue is usually reflected in the title or topic of the research problem. In formulating a research topic, the researcher needs to identify and select the problem and define the problem in more clear terms. some of the sources of this problems could be personal experiences, people and programs.
In the identification and selection of the problem, the researcher should look at internal factors such researcher’s competence, resources and time and also external factors such as the relevance of the research and its feasibility.
Having identified and selected the research topic, the researcher goes further to clearly define the research problem into clear research questions and objectives.
Developing objectives and formulation of hypothesis:
Developing of objectives: this part of the research process refers to questions to be answered of the stated problem must be solved or how the researcher intends to go about solving the stated problem. Objective is divided into main and specific objective. The main objective is the overall statement of the thrust of the study. While the specific objective is specific aspect of the topic that you want to investigate and it should be numerically listed.
Formulation of the hypothesis: the hypothesis is a tentative guess of relationship that exist before it is proven. There are two types of hypothesis. The null hypothesis which is usually on the negative states that no relationship exists and the alternative hypothesis state the opposite.
A good hypothesis must be
Conceptually clear
Must be specific (must have a scope)
Must be consistent
Testable
Objective
Must have theoretical backup
Techniques for testing it must be available.
Literature review: this is an extensive scrutiny of available past studies that a relevant to the research work (empirical literature review) on other to gain insight on how others have attempted the problem at and, it goes further to examine the various theories and principles in the area under investigation (theoretical literature review) and tries to with clarity define the various concepts in research work as well as their relationship with one another (conceptual literature review)
The main task of a literature reviews it to compare, criticize, synthesize and summarize.
Preparing research design: the research design is the conceptual structure that the research will be conducted. The research design is with respect to:
Sample design: this has to do with the method of selecting items to be investigated.
Operational design
Statistical design
Observational design.
Data collection: data collection is the process of gathering and measuring information on variable of interest in an established systematic fashion that enables the researcher to answer stated research questions, test hypothesis and evaluate outcome
The data so collected for the research may be primary or secondary data, the primary data can be collected through observation, interview and questionnaire.
Data analysis: In this stage of research process, the data collected is processed to ensure consistency across variables and locate omission if any. After the data has been processed, statistical techniques such as correlation analysis, regression analysis or any suitable statistic technique are applied on the data in order to find patterns, trends and relationships.
Interpretation and generalization: this stage is concerned with the interpretation of the of the data analyzed. After interpreting the result, meaning is made out of the result and generalization can be made from the findings.

9, Clearly discuss the Measurement Scales of Variable and common errors of measurement in research
Answer:
While a scale of measurement can be seen as the way in which variables are categorized based on their attributes, there are four measurement scales and they include;
Nominal scale: here, events are assigned number symbols that serve no other purpose than for identification, events are categorized onto categories that have no numerical attributes and because of this, this form of measurement is the least used for analysis of any kind.
An example of nominal scale is the matriculation number of students in a class. This matriculation number only serve as an identity and nothing more.
Ordinal scale: this scale of measurement aims to rank events, it categorizes events and object in to rank order with no aim to make the interval of the rank or order equal. A student who is asked to rate his lecturers will have to use a nominal scale.
Interval scale: apart from ranking or ordering objects and events, interval scale incorporate the concept of equality of this rank or order and is it is one of the most used measures in statistical analysis. An example of an interval scale is the score of students in a class, a student who score 100 is ranked higher than a student who scores 50 and the score of the first student is two times the score of the second student.
Ratio scale: In a ratio scale, in addition to order and equals of measurement, an absolute zero indicates the absence of the variable been measured (Jackson 2012). The difference between interval scale and ratio scale is that interval scale can range from negative to positive while the least value that a ratio scale can assume is zero.
The common errors of measurement in research are:
Random error: this is also residual error and it is an error caused by factors that cannot be explained by the researcher. It is caused by any factor that randomly effects measurement of variables across a sample. This type of error can be avoided by using statistical means to obtain best approximation of the true value.
Gross or Human error: gross or human error are error caused by human mistake in reading instrument, recording or calculating measurement result.
Systematic error: this is an error that is cause by factors that systematically affect the measurement of variables across the sample. There are three types of systematic error, the include
Instrumental error: this is an error due to the shortcoming or malfunctioning of the measurement instrument or the misuse of the instrument
Observational error: this is an error that is introduced by the user of the measurement instrument, it could be in the form of wrong scale reading.
Environmental error: this is an error due to external conditions that affect the measuring instrument. Such external condition could be effects of nature such as pressure, humidity, temperature etc.
Constant error: this occur when the result of measurement is in error by the same amount.
Absolute error: this the difference between an actual value and some approximation. Absolute error= true value – approximation
Relative error: relative error is the ratio of absolute error to true value of measurement. Relative error= absolute error/actual or true value.
Percentage error: percentage error is relative error measured in percent. Percentage error= relative error x 100.

10: Sampling is very vital in economic analysis. Discuss the two main types of sampling and their various components.
Answer:
The two main type of sampling are
Non probability sampling
Probability sampling
Non probability sampling: Non probability sampling method whereby samples are deliberately selected by the research. Here, the researcher selects the items to be included in the sample based on some assumption regarding the population interest which forms the basis of selection of elements in the sample.
There is always a danger of bias in the part of the researcher because items from the population do not have equal chances of being selected. There are two types of non-probability sampling, they include:
Purposive sampling: purposive sampling divided in two, they are:
I. judgement sampling: this is a form of purposive sampling whereby the researcher chooses the sample based on what he things is appropriate for his study
II. quota sampling: this type of non-probability sampling involves the segmentation of the entire population in to mutually exclusive groups, then the researcher deliberately selects samples from the groups. This form of sampling usually allows for equal representation of the group.
Convenience sampling: convenience sampling is also called opportunity or accidental sampling. Its drawing sample from the part of the population which is readily available convenient to the researcher. It is the least reliable of all the sampling designs.
Probability sampling: probability sampling provides a scientific technique of drawing samples from the population according to some laws of chance in which each unit in the universe has some definite probability of being selected in the sample (Gupta, 2012, p.15.15). this type of sampling technique gives every element equal chance of being selected. Some forms of probability sampling include;
Simple random sampling(SRS): simple random sampling a probability sampling technique where sample is drawn in such a way that each and every element in the population have equal and independent chance of being included in the sample.
Stratified sampling: this technique involves first dividing the population into mutually exclusive groups based on some shared characteristics and then randomly selected elements from each of the groups base on some proportion. Element in the same group have equal chance of being selected.
Systematic sample: this involves arranging the target population according to some ordering scheme and selecting elements at regular intervals after a random start. The interval to be used in determined by dividing the total population by the sample size.
Cluster sampling: A sampling technique that involves divided the total area covered the population into smaller non-over lapping areas or clusters, randomly selecting these small areas with the ultimate sample comprising of all or some elements of the cluster.
Multi-stage sampling: multi-stage sampling is a more complex form of cluster sampling in which two or more levels of units are embedded one in the other.
Multi-phase sampling: in multiphase sampling design, information is collected from a large sample of units, additional information is collected from the sub-sample of the whole sample either at the same time or at a later stage.

11. What are the various elements of model building in Economics?
Answer:
There are three basic elements incorporated in economic models, they are:
Optimization assumption: many economic models start with assumption that economic actors or agents are rational and aim at optimum utilization of resources. This element states that every economic agent is self-seeking.
Ceteris paribus: this element of model building connote “all other things being equal”. it holds all other variables constant except the variable under study. This is to enable model builders study the effect of variable A on variable B while all other variables that affect variable B are held constant.
Positive and normative economics distinction: positive economics describe economic phenomena as they actually are in reality based on empirical evidence. It is also referred to as “the what is”. Normative economics is focused on value base of economic fairness or the “what ought to be”.
12. Discuss Quantitative, Qualitative and Mixed Method Research in Economics and distinguish them with appropriate examples
Answer:
Quantitative research in economics: this is a research in the field of economics where numeric data on economic activities are collective and analyzed. It entails the use of scientific and systematic approaches to find patterns and develop models that can be used to manipulate economic variables to achieve certain economic goals.
Example; A research topic on relationship between economic growth and international trade in Nigeria is a good example of quantitative research. The research will entail collecting and analyzing data on Nigeria’s GDP, Export (and Import), Foreign Direct investment and other variables that affect growth.
Qualitative research in economics: this the is the collection and analysis of non-numerical economic data; through qualitative research, we are able to find out why economic agents behave the way they behave, it deals with the subjective rather than objective evaluation of views, attitudes, opinions, behaviors and attributes of economic agents
Example; A hypothetical research topic “Customer Loyalty; consumer Satisfaction versus Trend”. Here the research aims to find out whether consumers are loyal to a brand because of the satisfaction they derive or because the brand is the trending brand. The researcher will have to interview responded as to their reason for sticking to a particular product.
Mixed method research in economics: mixed method research in economics is an approach to inquiry involving collecting both quantitative and qualitative data, the two forms of data are integrated in the research design, through merging the data, explaining the data, building from one database to the other or embedding the database with a larger framework.

13. Clearly discuss the concept of Time Series Data, Cross Sectional and Panel Data, collection and transformation, including the ICT tools involved.
Answer:
Time series data: time series data is a set of observation collected on the same variable at different point in time. This observation may be collected at regular time interval and is usually presented in a chronological order. Statistical techniques are uses to analyze time series data to either make inference or to forecast future trend. Closing prices of Dangote Cement plc stock from October 1st to 31st is an example of time series data.
One major problems encountered with time series data is stationarity, a situation where by the mean and variance do no vary systematically over time and this come be overcome by differentiation.
Cross sectional data: this a data collected at the same point in time among usually homogenous variables. Here, no time sequence is involved as the aim is n0o to observe changes over time. Cross sectional variables can only be observed and not influenced or manipulated as is applicable in time series data. Cross sectional data can only describe characteristics the exist.
Cross sectional data may be faced with the problems of heterogeneity. A situation where by there exist diversity in the character under study.
Panel Data: A panel data is a data the is a combination of time series and cross sectional data. This involve the observation of many variables concurrently over a period of time, this data so collected help researchers to establish trend, make correlations and comparison. To conduct research on the topic “Macroeconomic Determinants of Industrial Output in ECOWAS sub-region”, both time series and cross sectional data will have to be collect. The time series aspect of the data will be the data on the economic variable that determine industrial output for say 30 years while the cross sectional aspect will be the collection of these data from ten different countries.
Data collection: data collection is the process of gathering both quantitative and qualitative information on specific variable of interest in an established systematic fashion for the purpose of analyzing and making informed decisions.
Data can be collected through:
Questionnaire: this is a written list of questions which a responded is expected to respond to. The respondent reads the question, and write down the answer.
Survey: this is a method of data collection that involves the presentation of oral/verbal stimuli and reply in terms of oral/verbal response. Interviews could be done through the telephone and personal interview.
Observation: observation is the purposeful, systematic and selective way of watching and listening to a phenomena or an interaction as they occur.
Data transformation: data transformation is the process of calculating new variables as faction of one or more variables. The forms of data transformation are;
Log transformation: this form of data transformation reduces very large values to very small units. This reduction helps to transform skewed data approximately conform to normality.
Square transformation: square transformation increases values greater than one and reduces values less than one
Reciprocal transformation: this form of data transformation reduces the large values even more than log transformation. Values less than one are increased in size.
Index number
Levels and growth rate
Nominal to real
The ICT tools involved in data collection are:
Personal Digital Assistant(PDA)
Mobile phone
Audio Computer Assisted Self Interviewing(ACASI)
Web based survey
Mapping Geographical Information(MGF)

14. Grant writing is very important for researchers globally. In view of this clearly discuss the Basic Elements of Grant Writing, identify key elements of successful grant writing and analyze how to determine the right funding for your proposal.
Answer
The Key Elements of a Grant Proposal writing are
1. Executive Summary: The summary is considered to be the most important element in the application because it offers a quick synopsis of what’s to come in the rest of the proposal. Do a good job here and the reviewers look forward to reading the rest of your application.
2. Organization Description: Your mission statement, vision statement, core values, history, organizational structure, board, list of programs, accomplishments and awards, community collaborators, volunteers: you likely have these pieces in your identity content already. Each grant application specifies which to include.
3. Statement of Need (Problem Statement): Use this section to explain what you do and why you do it. What is the problem you address? Who do you serve? What difference does your work make for them? Who else addresses this problem or why hasn’t this problem been addressed before? Why are you the one to address this problem? You can use the need statement to present both objective data (footnoted statistics that demonstrate the need) and subjective data (client stories that show your organization’s successful outcomes.)
4. Project Objectives: If you’re applying for project funding, what are your objectives? Use the S.M.A.R.T. formula to quantify them for the funder: “By May 31, we will have evaluated 50 enrolled after-school students and provided tutoring services so that at least 80% have improved reading or math scores by one letter grade.”
5. Project Description: In this section, get to the nuts and bolts about how you will carry out the project. Give details about your project in three ways.
Inputs: activities, resources, staff, and volunteers you need to operate the project.
Outputs: what you will achieve as a result of your program.
6. Evaluation: Here’s where your project objectives meet your program outputs to the funder that their investment produced results. Your proposal needs to explain how you will track both objective and subjective outcomes. You can include benchmarks along the way, too. This section includes the evaluation methods you’ll use (attendance numbers; testing; data) and tools you’ll use (such as software, reports, communications.)
7. Budget: If the grant is for general operating expenses, then the funder will ask you to submit your agency’s fiscal year budget. A budget can be as simple as one page, but you’ll increase chances for funding if you provide justification for each item, such as why your beneficiaries need subway tokens or why your program manager’s project expenses are 50% of their salary’s full time equivalent (nonprofit copywriter).

Reference List.
Creswell, J. (2013). Qualitative, quantitative and mixed method approach (5th Ed).

Sage Publishers.

Gupta, S.C. (2012). Fundamentals of statistics (7th Re. Ed). Mumbai, India:

Himalaya publishing house.

Jackson S, L (2012). Research methods and statistics: a critical thinking approach

(4TH Ed). Belmont, CA: Wadsworth, Cengage Learning.

Kutari R. (2004) Research Methodology (4TH Ed). Sage Publication.

Lucidchart. Decision making process. Retrieved from

http://www.lucidchart.com/blog/decision-making-process-steps
Non-profit writer. (ND). Key elements of grant proposal. Retrieved from

http://www.nonprofitwriter.com.

Oxman, A.D., Lavis, J.N., Fretheim, A., Lewin, S.(2009) Support tools for evidence

informed health policy making. Health research policy system.

1. Research have argued that decision making is the process of selecting best alternative from the available set of alternatives again, management is chiefly concerned with decision-making and its implementation however, these decisions should be based on appropriate studies, evaluations and observations as policy makers, research provide use of knowledge and skill needed to solve the problems and to meet challenges of a fast placed decision-making environment. As the special adviser, you have been invited to the center for research in decision making and policy formation in Nigeria. Not for getting the various activities that occur in decision making and the critical role of research, what would you tell the audience?

I will organize my talk to flow with these steps aimed to speak for 20 minutes
a) after introduction, I would salute everyone and welcome the audience to the presentation.
b) I would introduce the topic and provide a helpful literature review through story telling the audience with reasons why decision making in selecting the best alternative of management and implementation and remind them they should ensure the research entails appropriate precision of evaluation and observation as important criteria and thereafter say a few words about my background.
c) I would speak particularly on the relevance and main points and necessity of the research.
d) I would remind the audience that as policy makers the research talk is aimed as a lunch pad or spring pad for discursions in providing knowledge and skills needed to solve problems and meet the challenges of fast placed decision making environment.
e) finally, I will welcome feedbacks and thank the audience for their listening attention and time
2. During one of his interactions with the numbers of the parliament, Mr. President made the following conspicuous statement, ‘to produce a globally accepted empirical research paper that can be published in a high ranking impact factor journal, the research problem much be clinically defined with well laid out objectives and matching methodology and of course without any dilly daily or shillyshally, the literature review should be analytical, rigorous argumentative, theoretical, empirical, integrative and comparative’ as the special adviser to Mr. President on research methods and development strategy clearly discuss and analyze the above statement.
In discussing and analyzing the statement, I would point out that the exact research statement was improperly captured to determine
a) The interests and objectives are not properly stated and results to a time consuming research which might not be sustainable
b) The topic is large and cannot be managed with a given time frame preferable within a year
c) The research does not have a measurement or indicate concept of comparing countries globally acceptable empirical research e.g. FDI, financial portfolios
d) The level of capacity and expertise needed for an adequate level to conduct the research e) The usefulness of the research work
f) The knowledge available towards policy formation
g) The availability of data necessary to conduct the research needed
h) Ethical issues and conflict speaks of the interests such as benefits and effects of research here individual confidentiality and right to contribute should not be deceptive
i) The cost of the research should be visible so as to cut expenses.

3. Outline the various sessions that would appear in a MSc Thesis proposal in your department.
The five sessions which appears in an MSc Thesis proposal includes
(i) Review of the proposal from older studies to newer studies this can be achieved through the introduction, general description of area of study, problem analysis, purpose of study, relevance of research, statement of hypothesis, definition of terms, limitation of study, significance of the problem and references
(ii) the approaches showed in the different papers, thesis, including all articles this can be achieved in a clearer view by reviewing the literature, stating historical background, model/theories, current literature based on theories, summary and references
(iii) the central debate and focus on important studies and a paradigm shift to form a topic achievable through the methodology which will outline the chapter, statement of questions and hypothesis, research design and procedure, data collection, viability and reliability instruments, administration of data collection instrument, procedure for processing data, limitations of methodology and reference
(iv) what formation researches have on the topic and reason for further knowledge by presenting data analysis, introduction of chapter, criticism, presentation of report, presentation of data according to tested hypothesis, analyzing the data and references for the chapter
(v) draft of the literature which seeks on the arguments of the thesis statement which might result to combination of topics to create and overview of gaps and weakness to give a clear picture of understanding of the review and topic this done by giving a summary, conclusion and recommendation.

4. One of the preliminary pages in the MSc thesis is the abstract suppose the title of you MSc thesis is Macroeconomics determents of industrial output in ECOWAS sub region prepare a sample abstract on the title.
In preparing a sample abstract to a MSc thesis: Macroeconomic determinates of output in ECOWAS. The essence of the study is to discover the significant factors such as Foreign Direct Investment, foreign exchange, economic development, trade openness, human capital, financial liberations, and governance and government policy affecting industrial output of ECOWAS countries. In order to fill this gap a combination of factors including entrepreneurial ambitions, government policy, labour savings interventions, and demand for goods and services have been developed using panel sampling technique in relation exchange rate, government spending, imports of goods and services and real energy prices currently in phase three.
This MSc thesis investigates the relationship between inflation and interest rate where statistically insignificant however, significant and positive relationship between gross domestic product of previous years and unemployment and manufacturing of other preliminary evaluation of data was done using descriptive statistics and stationary evaluations the variables indicates not all variables re normal and no short run association between output and gross domestic product. Thus far, an initiated finding recommends the ECOWAS should continue in stabilizing the macroeconomic environment of ECOWAS in order to achieve a holistic growth.

5. Following the 6th edition of the APA referencing style, provide a sample reference list containing a book with one author, a books with 10 authors, fifth edition of a book with 3 authors’, a book chapter, a journal article with 2 authors, 2 journal articles published by the same author in a given year, an MSc thesis, a conference paper and a document on an interview.
• For one author with a book
Last Name, Initials (Year). Title
of Chapter. In Editor Initials Last Name
(Ed. Or Eds), Book Title (pp. page range).
Publisher DOI of available
DOI =Digital Objective Identified
• For ten authors
First Author
and then ‘etal. First
6 Authors Names, then
insert 3 Ellipsis points
and add the Last Authors
Name in the Reference list
• For Fifth Edition
Authors, Date, Title, City of Publication,
Publishers Name

1). Research Is The Systematic Method Consisting Of Articulating The Problem, Formulating A Hypothesis, Collecting The Facts Or Data, Analyzing The Facts And Reaching Certain Conclusions Either In The Form Of Solution Towards The Concerned Problem Or In Certain Generalizations For Some Theoretical Formulation (Kothari, 2004).
Critical Role Of Research In Decision Making & Policy Formulation.
Research is Helpful at every point in Planning and Decision making process,from competitor analysis to identify the size of your market,the trends in your industry,who the main competitors are and what their proposition Is.Market and social research provides accurate and timely information on the needs,attitudes and motivations of a population.RESEARCH Plays a Vital and Essential Social Role ,Assisting the Government and Businesses to Develop Services,Policies,and products that are responsive to an identified need for the Populace.
Policy Makers And Practitioners Use Research In Various Ways Including Instrumental,Conceptual,Political,Imposed And Process Uses.Increased Knowledge Of These Nuances Will Enable Researchers To Produce More Useful Work And Better Engage With Policy Makers,Practitioners,And Intermediaries.
Good Policy research provides evidence that is robust, ethical ,stands up to scrutiny and can be used to inform policy making. It Should Adhere to Principles of Proffessionalism,Transparency,Accountability and Audibility.
The Main Critical Role/ Purpose of Research is to inform action,gather evidence for theories, and contribute to developing knowledge in a field of study.The Utilization of research for decision making is mediated by social and political factors.
The Stages Of Research Policy Process Are Usually Conceptualized As Sequential Parts,and These Are Also The Specific Activities in Policy Formulation, They are ;1)Problem Emergence,2)Agenda Setting 3)Considering Of Policy Options 4) Decision Making 5) Implementation And Evaluation.(Jordan And Adelle,2012).
2.The Research Problem should be Clearly Defined with well laid out Objectives and Matching Methodology,Because Defining Research Objectives at the Beginning of a Project can serve as a guiding Light throughout the research process and help ensure that client needs are satisfied by asking the right questions to the right people,the right way.
The Research Problem must also have Clear Objectives and Matching Methodology Because they Establish the Scope and Depth of your project,to avoid unneccesary research.
For easy evaluation of Research ,methods and Coclusion.It Drives a study mand targeted,in depth research ,as you need to know what others have done in that area of the topic to help set up and define a problem that exists in the research.Also to eliminate or reduce uncertainty
Research Objectives and Matching Methodology ALSO Describes Concisely what the Research is trying to Achieve.They Summarize the accomplishments a researcher wishes to Achieve through the Project and Serves as a Guide,Providing Direction to the Study.
The literature review should be analytical,Rigorous,argumemtative, theoretical,empirical,integrative and Comparative, as it’s Purpose is to gain an understanding of the existing research and debates relevant to a particular topic or area of study,and to present that knowledge in the form of a written report.It Helps to build your knowledge in Your Field. It Allows you to gain familiarity with the current knowledge in your chosen field,as well as the boundaries and limitations of that field.
The Literature Review focuses on identifying the need for an additional research,Justifying your research,to also Identify the relationship of works in context of its contribution to the topic,and to other works. It Places the researchers research within the context of existing literature making a case for why further study is needed.
The Focus of a Literature review However is to summarize and synthesize the arguments and ideas of others without adding new contributions.
A literature review consists of an overview, a summary, and an evaluation of the current state of knowledge about a specific area of research, could include a discussion of methodological issues and suggestions for future research.The Steps in writing a literature review are Planning,Reading and Research,Analyzing,Drafting,and Revising.

3. Various sections that will appear in an MSC THESIS PROPOSAL.
A. INTRODUCTION/BACKGROUND OF THE STUDY
Introduces the topic in broad terms
States how you came to be interested in the topic
Briefly summarizes the context and significance of the project,
Creates reader interest in the project
B.PROBLEM STATEMENT
Frames the problem or question that your thesis will be addressing.
-The Problem is an issue that arise out of the scholarly literature,theory or practice that necessitates further study
-Answers Plainly and clearly, the question, ’Why does this research need to be conducted?
C.RESEARCH OBJECTIVES
To identify your main objectives and specific objectives that are Specific,measurable,realistic and time bound,SMART.
D.RESEARCH QUESTION
Based on your objectives from the research question for each specific research question using the what,how,why,or other question formats.
E. Literature Review
Explains How your project will extend, revise, or complicate what is already known.
Relates the Proposed project to specific questions and areas of inquiry within the field
For a Proposal, the Literature review should be relatively brief ,and cite only most appropriate sources for establishing the work that’s been done on the subject.
F.RESEARCH METHODOLOGY
-Should include a statement that Begins with,’The purpose of this study is.
-Provides a clear and succinct synopsis of the project.
-identifies the method of study, Defines the central concepts of the study.
G. CONCLUSION/BIBLIOGRAPHY
4.Abstract for ‘’macro economic determinants of industrial output in Ecowas Sub-Region.
The Main Purpose Of This Study Is To Identify Macroeconomic Determinants Of Industrial Output In The Ecowas Sub-Region. The data used includes manufacturing sector output, Interest rate, exchange rate,real wage among other macro economic variables.
The Methodological Approach adopted is the survey method of research.
Literature review was done from works done by eminent scholars,proffessionals and other marketing practitioners and secondary data was sourced too. Primary data was gathered from questionnaires sample and oral interviews administered to industrial users which was tested.
The data collected were tabulated and analysed using spearman rank order correlation coefficient (RHO)rs as test stastistic.
The Findings Of This Research Shows That Domestic Determinants Include Investment,Government Consumption, Population Size, Human Capital,N Atural Resources,Human Resources And Infrastructure,Inflation ,Exchange Rates,Petroleum Prices, Among Other Macro Economic Determinants Of Industrial Output.There is need for government to maintain secure monetary and fiscal policies in order to fight Inflation,Which has a negative effect on industrial output.

5.APA STYLE, SAMPLE REFERENCE LIST
A BOOK WITH ONE AUTHOR
Book – one author
Collier, A. (2008). The world of tourism and travel. Rosedale, New Zealand: Pearson Education New Zealand.
BOOK WITH MANY AUTHORS
Reference list
Multiple Authors
If a source has only one or two authors, name the authors and publication date each time the source is cited.
If a source has 3 to 5 authors, name all the authors in the first in-text citation; but in all other in-text citations, give only the name of the first author, followed by et al. (no italics) and the publication date..

.For example, a work with the following four authors: Faiza Adhama Mukhtar; Aminu Muhammad Mustapha; Henry Jack; and Ngozi Ndubuisi Adewale will feature on a reference list entry like this . . .
5TH EDITION OF A BOOK WITH 3 AUTHORS
We cite all authors the first time and from then on include only the last name of the first author followed by the word et.al

A BOOK CHAPTER
Palmer,F.(2007).Treaty Principles and Maori sport:Contemporary Issues.in C. Collins& Jackson(EDs.),Sport in Aotearoa/New Zealand Society(2nd ed.,pp.307-334).South Melbourne,Australia:Thomson.
A JOURNAL ARTICLE WITH 2 AUTHORS
Gabbet,T.,Jekins,D.,&Abernethy,B.(2010)Physical collisions and injury during professional rugby league skills training.Journal of science and medicine in sport,13(6),578-583.
2 JOURNAL ARTICLES BY THE SAME AUTHOR IN A YEAR
Reference list
Examples
Serial / journal article (print)
Mark, P. (2010). The Relationship between Public Expenditure and Human Capital Development in Nigeria: Journal of Economics, 10(7), 2-20.

Serial / journal article (online from a database – e.g. EBSCO or Newztext)
Musa, A., John, C., Amin, S., & Bashir, A. (2019). Analysis of the Relationship between Inflation and Economic Growth in Nigeria. Dutse Journal of Social Sciences and Economic Research, 2(4), 1543-1610. Retrieved from http://www.djse.com/bw/journal.asp?ref=0243-1057

MSC THESIS
Thesis (online)
Mann, D. L. (2010). Vision and expertise for interceptive actions in sport (Msc dissertation, The University of New South Wales, Sydney, Australia). Retrieved fromhttp://handle.unsw.edu.au/1959.4/44704

CONFERENCE PAPER
Conference paper (online)
James, I. (2014). Nexus between Human Capital Development and Economic Growth: ARDL Approach. Conference proceedings of the National Open University Nigeria, Abuja.Retrievedfromhttp://www.noun.ed.ng/conferences/ `papers/Proceedings_201 4.pdf
.
INTERNET DOCUMENT
WITH NO AUTHOR.
Pet Therapy.(n.d.).Retrieved from http://www.holistic online.com/stress/stress_pet-therapy.htm

6 .Research discovers answers to intellectual and Practical Problems through the application of scientific method. It IS A SYSTEMATIC EFFORT to gain new knowledge or provide information for solving a problem. It is also a careful inquiry or examination to discover new information or relationships and to expand and to verify existing knowledge.
Research is a wonderful Way of Inquiring and Getting Knowledge,Discovering new information,to answer intellectual and practical problems.
7. CHARACTERISTICS AND TYPES OF RESEARCH
CHARACTERISTICS OF SCIENTIFIC RESEARCH
Purposeful
Rigorous and Empirical
Testability and Critical
Valid and Verifiable (Replicability)
Systematic and Precision
Logical and Objective,
Generalizability and Parsimony
Purposeful
The purpose of scientific research is clear
Gathering new data or using existing data for a new purpose.
Directed towards the solution of a problem.
Rigorous and Empirical
It must based on good theoretical base and sound methodology.
You must be careful and scrupulous in ensuring that the procedures followed in finding answers to questions are relevant, appropriate and justified.
Empiricism implies that conclusions are drawn from hard evidence gathered from information/data collected from real life experiences or observations and subjected to experimentation and analysis.
This entails objectivity and absence of personal opinion , biases, feelings and idiosyncrasies.
Here we test theoretical model by simulation or experiment

Testability and Critical
Any research carried out undergoes some tests that validates the findings of the research.
This is like a hypothesis that can be tested to validate the result or nullify it.
Critical scrutiny of the procedures used and the methods employed is crucial to a research enquiry.
The process and procedures of investigation must be free from draw backs ( Avoid irrelevant methodologies)

Valid and Verifiable (Replicability)
Your conclusions must be correct and verifiable
This also implies replicability: That means the same relationship stands repeatedly under the same design parameters.
If another researcher in another part of the world repeats the same process of research and experimentation, similar or exact result will be obtained.
This guides against fraud in research
Systematic and Precision
Systematic in procedures of planning, collection of data, analysis and reporting. It follows clearly stipulated procedures.
this implies that the procedure adopted to undertake an investigation follow a certain logical sequence.
The different steps cannot be taken in a haphazard way.
Some procedures must follow others.
Precision means that result obtained must at least be a true representation of the population that is studied.
Parsimony and Generalizability
Parsimony means that the research procedure must be simple to understand
Generalizability: Scope of applicability
The wider, the better.
TYPES OF RESEARCH
The types of research are dependent on the problem, purpose, its nature, procedure or other general classifications. Based on this approach, research can be divided into
1. Theoretical and Applied Research
2. Qualitative and Quantitative Research
3. Descriptive and Explanatory Research
4. Exploratory research and conclusive Research
5. Conceptual Research and Empirical Research
6. Experimental research
7. Evaluative research and Predictive Research
8. Historical Research
9. Survey Research
The types of research are dependent on the problem or purpose. Based on this approach, research is divided into
Theoretical Research
This can also be called basic research. It is a type of research whereby its results forms the basic or foundation for future occurrences in that particular field or subject. Examples include law of demand and supply.
This type of research is aimed at knowing the issues that surround a particular event. It is conducted to gain knowledge even it there is not particular problem in focus.
The knowledge produced through pure research is sought in order to add to the existing body of research methods.

Applied Research:
This type of research is carried out in order to proffer a solution to an existing problem that an industry or a society is facing
It is use of basic research or past theories, knowledge and methods for solving an existing problem. It deals with practical problems.
The types of research are dependent on the problem or purpose. Based on this approach, research is divided into
Applied Research:
Applied research is done to solve specific, practical questions; for policy formulation, administration and understanding of a phenomenon.
It’s undertaken to solve practical problems rather than to acquire knowledge for knowledge sake
Applied research can be carried out by academic or industrial institutions.
General classification
Quantitative Research
A quantitative research is a systematic and scientific study that applies quantitative features and their relationships.
The objective of quantitative research is to develop mathematical models, and to test hypotheses.
It thus integrates purposes and procedures that are deductive, objective and generalized
Emphasis is laid on the construction of general theories which are applied universally. Well controlled procedures with large number of cases are followed in conducting the studies.
Quantitative approach can be further sub-classified into:
inferential approach;
experimental approach;
and simulation approach.
The purpose of inferential approach research is to form a data base from which to infer characteristics or relationships of population.
This usually means survey research where a sample of population is studied (questioned or observed) to determine its characteristics, and it is then inferred that the population has the same characteristics.
Experimental approach is characterised by much greater control over the research environment and in this case some variables are manipulated to observe their effect on other variables.
Simulation approach involves the construction of an artificial environment within which relevant information and data can be generated.
This permits an observation of the dynamic behaviour of a system (or its sub-system) under controlled conditions.
The term ‘simulation’ in the context of business and social sciences applications refers to “the operation of a numerical model that represents the structure of a dynamic process.

Given the values of initial conditions, parameters and exogenous variables, a simulation is run to represent the behaviour of the process over time.” Simulation approach can also be useful in building models for understanding future conditions.
Qualitative research on other hand, are the studies conducted within the perspective of critical theory and interpretive paradigms.
By using induction as a research strategy, qualitative research creates the theory and discovery through flexible, emergent research designs.
It tries to evolve meaning and interpretation based on closer contacts between researchers and the people they study. Thus qualitative research consists of purposes and procedures that integrate inductive, subjective and contextual approaches.
It deals with the subjective assessment of attitudes, opinions and behaviour of respondents in the field. Results are generated either in non-quantitative form or in a form which are subjected to relatively less rigorous quantitative treatment.
Various techniques like group discussions, projective techniques, in-depth interviews etc., are used.
Qualitative research on other hand, are the studies conducted within the perspective of critical theory and interpretive paradigms.
By using induction as a research strategy, qualitative research creates the theory and discovery through flexible, emergent research designs.
It tries to evolve meaning and interpretation based on closer contacts between researchers and the people they study. Thus qualitative research consists of purposes and procedures that integrate inductive, subjective and contextual approaches.
It deals with the subjective assessment of attitudes, opinions and behaviour of respondents in the field. Results are generated either in non-quantitative form or in a form which are subjected to relatively less rigorous quantitative treatment.
Various techniques like group discussions, projective techniques, in-depth interviews etc., are used.
Classification of research based on its nature (Descriptive and Explanatory Research)
Descriptive research: describes a situation, events or social systems. It aims to describe the state of affairs as it exists. Surveys and fact-finding enquiries of different kinds are part of descriptive research.
Survey methods of all kinds including comparative and correlational methods are used in descriptive research studies. A survey of socio-economic conditions of rural/urban workers is an area of descriptive research.
In descriptive research studies, the researchers have no control over variable. They can report only what has happened or is happening. Example, frequency of shopping, preferences of people, or similar data.
Classification of research based on its nature (Descriptive and Explanatory Research)
Explanatory research: aims at establishing the cause and effect relationship.
The researcher uses the facts or information already available to analyse and make a critical evaluation of the data/information. An example of explanatory research is:
‘whether increase in agricultural productivity is explained by improved rural roads?’

Analytical research often extends the descriptive approach to suggest or explain why or how something is happening. An important feature of this type of research is in locating and identifying the different factors (or variables) involved.
Other types of research
Exploratory research aims at developing the hypothesis rather than testing a pre-conceived hypothetical contention or notion.
Exploratory research is undertaken when few or no previous studies exist.
The aim is to look for patterns, hypotheses or ideas that can be tested and will form the basis for further research.
Typical research techniques would include case studies, observation and reviews of previous related studies and data.
Formalized or conclusive research studies are those with substantial structure and with specific hypotheses to be tested.
Formalised research studies deal with a definitive structure within which specific hypotheses are tested.
Other types of research
Conceptual research is related to abstract ideas or theory.
It is generally used by philosophers and thinkers to develop new concepts or to reinterpret the existing theories.
It is thus related to some abstract ideas or theory.

Empirical research relies on experience or observation. It is data-based research, coming up with conclusions which are capable of being verified by observation or experiment.
This type of research is particularly useful when validation or verification of an aspect is required. We can also call it as experimental type of research.
Other types of research
Experimental research aims at identifying the causal factors by means of experiments.
Evaluative research: Here, the cost effectiveness of a programme is examined.
Such research addresses the question of the efficiency of a programme and are useful in taking policy decisions on issues like whether the programme is effective and/or needs modification or re-orientation? Whether it should be continued?
Action research is another type of research that deals with real world problems aimed at finding out practical solutions or answers to them. It gathers feedback which is then used to generate ideas for improvement.
Other types of research
Predictive Research:
The aim of Predictive research is to speculate intelligently on future possibilities, based on close analysis of available evidence of cause and effect, e.g. predicting when and where future economic recession might take place
One-time and Longitudinal (Cross Sectional and Time series):
From the point of view of time, we can think of research either as one-time research or longitudinal research. The former is confined to a single time-period, whereas in the latter case the research is carried on over several time-periods.
Other types of research
Historical research utilizes existing documents to study events or ideas of the past including the philosophy of persons and groups at any remote point of time.
Uncovers the unknown, and helps to understand the origin and development of variables of interest, their changing trends and impact on society.
Answers questions
Aids in understanding the culture of people.
Other types of research
Historical research
SOURCES OF DATA
(a) Public Records, Library Sources or Archival Sources
Historical information exists in libraries and the National Archives.
These are documents such as books, journals, newspapers etc which contain information on the research issue.

(b) Interviews
Data for historical researches can be collected from people who lived during the occurrence of the event being studied or participated in the development of policies being studied.
Example information for a historical study on Nigeria/Biafran war can be gotten from Gowon (or Ojukwu RIP) or your parents/ grand parents.
Other types of research
SURVEY RESEARCH
Surveys are a systematic way of asking people to volunteer information about their attitudes, behaviors, opinions and beliefs.
The success of survey research rests on how closely the answers that people give to survey questions matches reality that is, how people really think and act.

It uses observations, interviews, questionnaires and other methods to achieve this.
Relevance, validity and accuracy are the basic criteria for Questionnaire design

8. Steps in Research Process:
1. Formulating the Research Problem and Topic
2. Developing the objectives and Formulate hypothesis
3. Extensive Literature Review
4. Preparing the Research Design including Sample Design
5. Collecting the Data
6. Analysis of Data
7. Interpretation and Generalization
8. Preparation of the Report or Presentation of Results-Formal write ups of conclusions reached.

9. MEASUREMENT SCALES OF VARIABLE AND COMMON ERRORS OF MEASUREMENT IN RESEARCH.

Measurement Scales of Variable
(a) nominal scale; (b) ordinal scale; (c) interval scale; and (d) ratio scale.
Nominal scale
Nominal scale is simply a system of assigning number symbols to events in order to label them. The usual example of this is the assignment of registration numbers to students or numbers of basketball players in order to identify them.
Nominal scales provide convenient ways of keeping track of people, objects and events.
One cannot do much with the numbers involved. For example, one cannot usefully average these nominal numbers and come up with a meaningful value.
Neither can one usefully compare the numbers assigned to one group with the numbers assigned to another.
A nominal scale simply describes differences between things by assigning them to categories. Nominal data are, thus, counted data.
Ordinal scale
The lowest level of the ordered scale that is commonly used is the ordinal scale.
The ordinal scale places events in order, but there is no attempt to make the intervals of the scale equal in terms of some rule.
Rank orders represent ordinal scales and are frequently used in research relating to qualitative phenomena.
A student’s rank in his graduation class involves the use of an ordinal scale.
One has to be very careful in making statement about scores based on ordinal scales.
For instance, if Student A’s position in his class is 10 and Student B’s position is 40, it cannot be said that A’s position is four times as good as that of B.
The statement would make no sense at all. Ordinal scales only permit the ranking of items from highest to lowest. Ordinal measures have no absolute values, and the real differences between adjacent ranks may not be equal.
All that can be said is that one person is higher or lower on the scale than another, but more precise comparisons cannot be made.Thus, the use of an ordinal scale implies a statement of ‘greater than’ or ‘less than’ (an equality statement is also acceptable) without our being able to state how much greater or less.
The real difference between ranks 1 and 2 may be more or less than the difference between ranks 5 and 6.

Interval scale
Interval scales provide more powerful measurement than ordinal scales for interval scale also incorporates the concept of equality of interval.
As such more powerful statistical measures can be used with interval scales.
Mean is the appropriate measure of central tendency, while standard deviation is the most widely used measure of dispersion.
Product moment correlation techniques are appropriate and the generally used tests for statistical significance are the ‘t’ test and ‘F’ test.

Ratio scale
Ratio scale represents the actual amounts of variables. Measures of physical dimensions such as weight, height, distance, etc. are examples.
They have an absolute or true zero of measurement.
The term ‘absolute zero’ is not as precise as it was once believed to be.
We can conceive of an absolute zero of length and similarly we can conceive of an absolute zero of time.
For example, the zero point on a centimetre scale indicates the complete absence of length or height. But an absolute zero of temperature is theoretically unobtainable and it remains a concept existing only in the scientist’s mind.
A characteristic difference between the ratio scale and all other scales is that the ratio scale can have values in terms of multiples of fractional parts, and the ratios are true ratios.
For example, a metre is a multiple (by 100) of a centimetre distance; a millimetre is a tenth (a fractional part) of a centimetre. The ratios are 1:100 and 1:10.
Type of errors
Gross error/human Errors
Random Errors
Systematic Errors
Constant Errors
Absolute Errors
Relative Errors
Percentage Errors

Gross Error
caused by human mistakes in reading/using instruments
may also occur due to incorrect adjustment of the instrument and the computational mistakes
cannot be treated mathematically
cannot eliminate but can minimize
Eg: Improper use of an instrument.
This error can be minimized by taking proper care in reading and recording measurement parameter.
In general, indicating instruments change ambient conditions to some extent when connected into a complete circuit.
Therefore, several readings (at three readings) must be taken to minimize the effect of ambient condition changes.

2) Systematic Error
Systematic error is caused by any factors that systematically affect measurement of the variable across the sample.
Unlike random error, systematic errors tend to be consistently either positive or negative — because of this, systematic error is sometimes considered to be bias in measurement.
In general, systematic errors can be subdivided into static and dynamic errors.
Static – caused by limitations of the measuring device or the physical laws governing its behavior.
Dynamic – caused by the instrument not responding very fast enough to follow the changes in a measured variable.
5: Collecting the Data
Errors in measurement
2) Systematic Error
– 3 types of systematic error :-
(i) Instrumental error
(ii) Environmental error
(iii) Observational error
5: Collecting the Data
Errors in measurement
(i) Instrumental error – inherent while measuring instrument because of their mechanical structure (eg: in a D’Arsonval meter, friction in the bearings of various moving component,irregular spring tension, stretching of spring, etc)
– error can be avoided by:
(a) selecting a suitable instrument for the particular measurement application
(b) apply correction factor by determining instrumental error
(c) calibrate the instrument against standard
5: Collecting the Data
Errors in measurement
(ii) Environmental error
– due to external condition effecting the
measurement including surrounding area condition
such as change in temperature, humidity, barometer pressure, etc
– to avoid the error :-
(a) use air conditioner
(b) sealing certain component in the instruments
(c) use magnetic shields

(iii) Observational error
– introduce by the observer
– most common : parallax error and estimation error (while reading the scale)
– Eg: an observer who tend to hold his head too far to the left, while reading the position of the needle on the scale.
Random Error
– Caused by any factors that randomly affect measurement of the variable across the sample.
– Due to unknown causes, occur when all systematic
error has accounted
– can be avoided by
Use statistical means to obtain best approximation of true value.
Constant error
When the results of observation are in error by the same amount, the error is said to be a constant error.

e.g. if a scale of 15 cm actually measures 14.8 cm.
Then it is measuring 0.2 cm more in every observation.
This type of error will be same in all measurements done by the scale.

Three other ways of defining the error are:
-Absolute error
-Relative error
-Percentage error
CLASSIFICATION OF ERRORS
The following are general classifications for errors:
For consumer purposes, 5-10% error is acceptable

For engineering purposes, 1% error is acceptable

For scientific purposes, 0.1% error is acceptable
Possible Sources of error in Measurement
a. Respondent:
At times the respondent may be reluctant to express strong negative feelings or it is just possible that he may have very little knowledge but may noat admit his ignorance.
Transient factors like fatigue, boredom, anxiety, etc. may limit the ability of the respondent to respond accurately and fully.

b. Situation:
Situational factors may also come in the way of correct measurement. Example: if someone else is present or the respondent feels that anonymity is not assured, It can affect the responses.
c. Measurer: The interviewer can distort responses by rewording or reordering questions. His behaviour, style and looks may encourage or discourage certain replies from respondents

d. Instrument: Error may arise because of the defective measuring instrument. The use of complex words, beyond the comprehension of the respondent, ambiguous meanings, poor printing, inadequate space for replies, response choice omissions, etc.

10.Types of Sample Design
Sample designs are basically of two types viz., non-probability sampling and probability sampling.

Non-probability sampling
In this type of sampling, items for the sample are selected deliberately by the researcher.
Thus, there is always the danger of bias entering into this type of sampling technique.
But if the investigators are impartial, work without bias and have the necessary experience so as to take sound judgement, the results obtained from an analysis of deliberately selected sample may be tolerably reliable.
Types of Sample Design
There are two main types of nonprobability sampling design: convenience sampling and purposive sampling.

i).Convenience sampling is the least reliable of all sampling designs in terms of generalizability, but sometimes it may be the only viable alternative when quick and timely information is needed, or for exploratory research purposes.

Sometimes known as grab or opportunity sampling or accidental or haphazard sampling.
A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient.
The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.
For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week.
This type of sampling is most useful for pilot testing.
In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample.
Types of Sample Design
ii. Purposive sampling plans fall into two categories: judgment and quota sampling designs.
Judgment sampling, though restricted in generalizability, may sometimes be the best sampling choice, especially when there is a limited design population that can supply the information needed.
Quota sampling is often used on considerations of cost and time and the need to adequately represent minority elements in the population.
Although the generalizability of all nonprobability sampling designs is very restricted, they have certain advantages and are sometimes the only viable alternative for the researcher.

The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling.
Then judgment used to select subjects or units from each segment based on a specified proportion.
For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.
It is this second step which makes the technique one of non-probability sampling.
In quota sampling the selection of the sample is non-random.
For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years
Probability sampling includes:
Simple Random Sampling,
Systematic Sampling,
Stratified Random Sampling,
Cluster Sampling
Multistage Sampling.
Multiphase sampling
Probability sampling
Probability sampling is also known as ‘random sampling’ or ‘chance sampling’.
Under this sampling design, every item of the universe has an equal chance of inclusion in the sample.
We can measure the errors of estimation or the significance of results obtained from a random sample, and this fact brings out the superiority of random sampling design over the deliberate sampling design.
Random sampling ensures the law of Statistical Regularity which states that if on an average the sample chosen is a random one, the sample will have the same composition and characteristics as the universe.
This is the reason why random sampling is considered as the best technique of selecting a representative sample.
Types of Sample Design
ii. Probability sampling
Complex random sampling designs
Probability sampling under restricted sampling techniques, as stated above, may result in complex random sampling designs.
Such designs may as well be called ‘mixed sampling designs’ for many of such designs may represent a combination of probability and non-probability sampling procedures in selecting a sample. Some of the popular complex random sampling designs are as follows:
Types of Sample Design
ii Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.
Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size).
It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list.
A simple example would be to select every 10th name from the telephone directory (an ‘every 10th’ sample, also referred to as ‘sampling with a skip of 10’). Among other types of Probability Sampling.

11. Various elements of model building in Economics
Economists developed their models as aids to understanding economic issues.
Elements of Model Building
Economic models incorporate three common basic elements:
i. Ceteris paribus (all things being equal) assumption.
ii. Optimization assumption.
iii. Positive and normative distinction.

Economic models incorporate three common basic elements:
i. Ceteris paribus (all things being equal) assumption.
ii. Optimization assumption.
Many economic models start from the assumption that the economic agents are rational and aimed at optimizing satisfaction
iii. Positive and normative distinction. Positive economics describes and explains various economic phenomena or the “what is” scenario, and it is based on fact.
Normative economics on the other hand focuses on the value of economic fairness, or what the economy “should be” or “ought to be.”

12 QUANTITATIVE DATA ANALYSIS
In analysing the relations between variables we encounter one or more of the following.
A univariate, bivariate and multivariate analyses.
A univariate analysis can be established using a frequency distribution table; measures of central tendency such as the arithmetic mean, the median, and the mode from which normal distribution is when these measures are located at the same value; measures of dispersion such as the range, variance and the standard deviation.
A definition. It involves the collection of both qualitative (open-ended) and quantitative (closed-ended) data in response to research questions or hypotheses.

It includes the rigorous methods (i.e., data collection, data analysis, and interpretation) of both quantitative and qualitative data.
The two forms of data are integrated in the design analysis through merging the data, explaining the data, building from one database to another, or embedding the data within a larger framework

These procedures are incorporated into a distinct mixed methods design that indicates the procedures to be used in a study.

MIXED METHODS RESEARCH
Types of Mixed Methods Designs
The Convergent Design,
The Explanatory Sequential Design, and
The Exploratory Sequential Design
1.The convergent mixed methods design
This is probably the most familiar of the core and complex mixed methods approaches.
Researchers new to mixed methods typically first think of this approach because they feel that mixed methods only consists of combining the quantitative and qualitative data.
In this single-phase approach, a researcher collects both quantitative and qualitative data, analyzes them separately, and then compares the results to see if the findings confirm or disconfirm each other/
Data collection. The qualitative data can assume any of the forms, such as
interviews, observations, documents, and records.
The quantitative data can be instrument data, observational checklists, or numeric records, such as census data,
Ideally, the key idea with this design is to collect both forms of data using the same or parallel variables, constructs, or concepts.
Data analysis and integration.
Data analysis in a convergent design consists of three phases.
First, analyze the qualitative database by coding the data and collapsing the codes into broad themes.

Second, analyze the quantitative database in terms of statistical results.
Third comes the mixed methods data analysis.
This is the analysis that consists of integrating the two databases
Data analysis and integration. This integration consists of merging the results from both the qualitative and the quantitative findings.One challenge in this design is how to actually merge the two databases since bringing together a numeric quantitative database with a text qualitative database is not intuitive. There are several ways to merge the two database
Data analysis and integration.
1. The first approach is called a side-by-side comparison. These comparisons can be seen in the discussion sections of mixed methods studies.
The researcher will first report the quantitative statistical results and then discuss the qualitative findings (e.g., themes) that either confirm or disconfirm the statistical results.
Alternatively, the researcher might start with the qualitative findings and then compare them to the quantitative results.
Mixed methods writers call this a side-by-side approach because the researcher makes the comparison within a discussion, presenting first one set of findings and then the other. A good example of this can be seen in the
Classen and colleagues’ (2007) study
Data analysis and integration.
2.. Researchers can also merge the two databases by changing or transforming qualitative codes or themes into quantitative variables and then combining the two quantitative databases—a procedure in mixed methods research called data transformation.
The researcher takes the qualitative themes or codes and counts them (and possibly groups them) to form quantitative measures.
Some useful procedures that mixed methods researchers have used can be found in Onwuegbuzie and Leech (2006).
MIXED METHODS RESEARCH
Types of Mixed Methods Designs
Data analysis and integration.
3. A final procedure involves merging the two forms of data in a table or a graph.
This is called a joint display of data, and it can take many different forms.
It might be a table that arrays the themes on the horizontal axis and a categorical variable (e.g., different types of providers such as nurses, physician assistants, and doctors) on the vertical axis.

It might be a table with key questions or concepts on the vertical axis and then two columns on the horizontal axis indicating qualitative responses and quantitative responses to the key questions or concepts (Li, Marquart, & Zercher, 2000).
MIXED METHODS RESEARCH
Types of Mixed Methods Designs
Example of a Convergent Parallel Mixed Methods Design
Classen et al. (2007) studied older driver safety in order to develop a health promotion intervention based on modifiable factors influencing motor vehicle crashes with older drivers (age 65 and older).
It was a good example of a convergent mixed methods study.
The central purpose of the study was identified in the abstract:
This study provided an explicit socio-ecological view explaining the interrelation of possible causative factors, an integrated summary of these causative factors, and empirical guidelines for developing public health interventions to promote older driver safety.
Using a mixed methods approach, they were able to compare and integrate main findings from a national crash dataset with perspectives of stakeholders. (p. 677). MIXED METHODS RESEARCH
Types of Mixed Methods Designs
Example of a Convergent Parallel Mixed Methods Design
This purpose statement identified the use of both quantitative (i.e., a national crash data set) and qualitative
(i.e., stakeholders’ perspectives) data.
From one of the research questions in the study, we learned that the authors compared the qualitative stakeholder perspectives, needs, and goals for safe and unsafe driving with the quantitative results of the factors that influenced driving injuries.
So the expected outcome was to compare the findings.
The method section commented on the quantitative national data set, the statistical analysis of this data set, and then the qualitative data set and its analysis.
MIXED METHODS RESEARCH
Explanatory Sequential Mixed Methods Design
Explanatory sequential mixed methods is one in which the researcher first conducts quantitative research, analyzes the results and then builds on the results to explain them in more detail with qualitative research.
It is considered explanatory because the initial quantitative data results are explained further with the qualitative data.
It is considered sequential because the initial quantitative phase is followed by the qualitative phase.
This type of design is popular in fields with a strong quantitative orientation (hence the project begins with quantitative research), but it presents challenges of identifying the quantitative results to further explore and the unequal sample sizes for each phase of the study.

MIXED METHODS RESEARCH
Types of Mixed Methods Designs
Explanatory Sequential Mixed Methods Design
Example of an Explanatory Sequential Mixed Methods Design
In 2007, Banyard and Williams conducted an explanatory sequential mixed methods study examining how women recover from childhood sexual abuse.
The quantitative component of the study consisted of structured interviews (with 136 girls in 1990 and a subset of 61 girls in 1997) looking at resilience, correlates of resilience, over time across 7 years of early adulthood.
The qualitative aspect consisted of interviews with a subset of 21 girls about their life events, coping, recovery, and resilience. The intent of the mixed methods study was to use the qualitative interviews to “explore and make sense” of the quantitative findings (p. 277).
Multiple methods are used to examine aspects of resilience and recovery in the lives of female survivors of child sexual abuse (CSA) across 7 years of early adulthood.
Exploratory Sequential Mixed Methods Design
Exploratory sequential mixed methods is the reverse sequence from the explanatory sequential design.
In the exploratory sequential approach the researcher first begins with a qualitative research phase and explores the views of participants.
The data are then analyzed, and the information used to build into a second, quantitative phase.
The qualitative phase may be used to build an instrument that best fits the sample under study, to identify appropriate instruments to use in the follow-up quantitative phase, to develop an intervention for an experiment, to design an app or website, or to specify variables that need to go into a follow-up quantitative study.

Exploratory Sequential Mixed Methods Design
Example of an Exploratory Sequential Mixed Methods Design
A good example of an exploratory sequential study with an experimental test outcome is found in Betancourt et al. (2011).
This study used mixed methods research to adapt and evaluate a family strengthening intervention in Rwanda.
The investigators sought to examine the mental health problems facing HIV-affected children in Rwanda.
They first began with an exploratory, qualitative first phase of interviews with children and their caregivers.

From a qualitative thematic analysis of the data, they then performed an extensive review of the literature to locate standardized measures that matched their qualitative findings.

They found some measures and added some new ones to develop a survey instrument.

This instrument went through several refinements following rigorous procedures of instrument-scale development (e.g., backward and forward translations, a discussion of items, reliability and validity) to develop good construct validity for the measures.
13.QUESTION FOR TIME SERIES,CROSS SECTION AND PANEL DATA
Time series data
This is data collected at specific points in time.
It is a set of observations on the values that a variable takes at different times.
Such data may be collected at regular time intervals such as daily (like stock prices, whether reports etc.), weekly (like money supply figures), monthly (like consumer price index etc.) quarterly (like GDP), annually (like government budget etc.) or biannually etc.
Financial data measures phenomena such as changes in the price of stocks.
This type of data is collected more frequently than some others, for instance, daily or even hourly.
In all of these examples, the data are ordered by time and are referred to as time series data.
In economics, we commonly use the notation Yt to indicate an observation on variable Y (e.g. real GDP) at time t.
A series of data runs from period t = 1 to t = T.
“T” is used to indicate the total number of time periods covered in a data set.
To give an example, if we were to use annual real GDP data from 1980–2022 – a period of 42 years – then t = 1 would indicate 1980 up to t = 42 for 2022 and T = 42 the total number of years. Hence, Y1 would be real GDP in 1980, Y2 real GDP for 1981, etc.
Time series data is typically presented in chronological order.
With the advent of high-speed computers, data can now be collected over an extremely short interval of time, such as the data on stock prices, which can be obtained literally continuously (the so-called real-time quote).
Although time series data are used heavily in economics, they present special problems for econometricians.

Cross section data
Cross-section data are data on one or more variables collected at the same point of time.
A common example is census of population, data on the wage of all people in a certain company or industry
We use the notation Yi to indicate an observation on variable Y for individual i.
Observations in a cross-sectional data set run from individual i = 1 to N.
By convention, N indicates the number of cross-sectional units (e.g. the number of people surveyed).
Microeconomist may ask N = 100 managers from manufacturing companies about their profit figures in the last month.
In this case, Y1 will equal the profit reported by the first company, Y2 the profit reported by the second company, through to Y100, the profit reported by the 100th company.
With cross-sectional data, the ordering of the data typically does not matter (unlike time series data).
Just as time series data create their own special problems (because of the stationarity issue), cross-sectional data too have their own problems, specifically the problem of heterogeneity.

Panel data
Some data sets will have both a time series and a cross-sectional component.
This data is referred to as panel data.
Economists working on issues related to growth often make use of panel data.
For instance, GDP for many countries from 1980 to the present is available.
A panel data set on Y = GDP for 12 African countries would contain the GDP value for each country in 1980 (N = 12 observations), followed by the GDP for each country in 1951 (another N = 12 observations), and so on. Over a period of T years, there would be T \ N observations on Y.
We use the notation Yit to indicate an observation on variable Y for unit i at time t.
In the economic growth example, Y11 will be GDP in country 1, year 1, Y12 GDP for country 1 in year 2, etc.
Longitudinal or Micro Panel Data is a special type of pooled data in which the same cross-sectional (say a family or firm) is surveyed overtime.

14.
The Basic Elements of Grant Writing
Identify key elements of successful grant writing
Determine the right funding for your proposal
What the funder is seeking
Key elements of a Grant
To Submit Grant Proposal
-Obtain grant proposal guidelines
-Review eligibility requirements
-Know the submission deadline
-Determine personnel needs
-Check other administrative requirements
-Supporting Materials
-Letters of support/endorsement
-Resumes
-Signatures -Submission format -Delivery requirements.

How To Determine The Right Funding For Your Proposal
Develop your project idea
Assess your capability to secure funding
Research potential funding opportunities and get in touch with the funders
Develop credibility
What problems will the project solve?
What community needs will be met by the project?
Do you and your team have the expertise to execute the project?
What makes you competitive?
Are previous funders satisfied with your performance?
What is the capability of your staff?
Important Elements of Grant Proposal
Would you need the assistance of other people or organizations in the course of the project if funded?
What are your collaborative options?

MOSES OJONUGWA ODUMU
PG/CBN/MSC/21/0020
CENTRAL BANK OF NIGERIA
COLLABORATIVE POSTGRADUATE PROGRAMME
UNIVERSITY OF NIGERIA ENUGU CAMPUS
FIRST SEMESTER
DEPARTMENT OF ECONOMICS
ECO 591 – RESEARCH METHODS
ASSIGNMENT
Question 1:
Researchers have argued that decision-making is the process of selecting the best alternative from the available set of alternatives. Again, management is chiefly concerned with decision-making and its implementation. However, these decisions should be based on appropriate studies, evaluations and observations. As policy makers, research provides us with knowledge and skills needed to solve the problems and to meet the challenges of a fast-paced decision-making environment. As the Special Adviser to Mr. President on Research Methods and Policy Analysis, you have been invited to the Centre for Policy Research and Economic Analysis to present a paper on “The Critical Role of Research in Decision Making and Policy Formulation in Nigeria”. Not forgetting the various activities that occur in decision making and the critical role of research, what would you tell your audience?
Answer:
All protocol duly observed, I am here to present a paper on: “The Critical Role of Research in Decision Making and Policy Formulation in Nigeria”.
Research is the careful consideration of study regarding a particular concern or problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.
The primary goal of research is to guide action, gather evidence for theories, and contribute to the growth of knowledge in data analysis. Importance of research include:
i. Research helps to acquire knowledge effectively
ii. Research helps in problem-solving
iii. It provides the latest information
iv. It builds credibility
v. It helps in business success
vi. It helps discover and seize opportunities
Undoubtedly, decisions hold immense significance in an organization and in every country. For enhanced decision-making and problem-solving, research is pivotal. A decision can be made after analyzing all the relevant information, facts and data. This is where research proves utilitarian. Research is considered to be the most supportive means in management because it can help in resolving any uncertain or complex problem easily.
There are various reasons for which research can be significant in decision making and in policy formulation. These include:
a) Effective decision: Research helps to make better decisions in less amount of time.
b) Better Coordination: Research coordinates every ministry and department’s decision in the best way.
c) Enhanced Control: Research in decision making and in policy formulation helps to facilitate control of the subordinates by the policy makers.
d) Improves Productivity: Research aides policy makers in taking proper decisions regarding the selection, choices and their planning.
In conclusion, research results are needed to form the basis of policy formulation. Ideally from every research and development result can be used as government reference in formulating every policy.

Question 2:
During one of his interactive sessions with the members of the Parliament, Mr. President made the following conspicuous statement, “To produce a globally- accepted empirical research paper that can be published in a high-ranking impact factor journal, the research problem must be clinically defined, with well laid out objectives and matching methodology. And of course, without any dilly-dally or shilly-shally, the literature review should be analytical, rigorous, argumentative, theoretical, empirical, integrative and comparative”. As the Special Adviser to Mr. President on Research Methods and Development Strategy, clearly discuss and analyze the above statement.
Answer:
a) Research Problem: A research problem is a specific issue, difficulty, contradiction, or gap in knowledge that you will aim to address in your research. It is a specific statement relating to an area of concern and is contingent on the type of research.
b) Research Objectives: Research objectives are the outcomes that you aim to achieve by conducting research. Many research projects contain more than one research objective.
c) Methodology: Research Methodology explains the exact activities and processes of your research is, through what methods you will be measuring the data and how will you be interpreting your outcomes
d) Literature Review: A literature review is a comprehensive summary of previous research on a topic. It discusses and analyses published information in a particular subject area. The literature review should be analytical, rigorous, argumentative, theoretical, empirical, integrative and comparative:
e) Analytical: Literature review should be critical and analytical. A summary and synthesis of the current knowledge of a topic. Thus, it should compare and relate different theories, findings, among others, rather than just summarize them individually.
f) Rigorous: Literature review process should be rigorous, requiring a thorough evaluation of the quality and findings of each source discussed. Writing a literature review is a time-consuming process that requires extensive research and critical analysis.
g) Argumentative: Literature review should be argumentative in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint.
h) Theoretical: Literature review should be theoretical in order to concretely examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena.
i) Empirical: Literature review should be empirical or systematic which examines past studies to answer a particular research question. Rather than drawing information from theories or beliefs, empirical research relies on observations and measurements to arrive at conclusions.
j) Integrative: The integrative literature review is a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated.
k) Comparative: Comparative Literature is traditionally known as the study of two or more literatures in comparison and their multi-dimensional components which may encompass aspects such as the historical, gender, economic, cultural, social, philosophical, religious, and linguistic factors of the distinct cultures being analyzed.

Question 3:
Outline the various sections that will appear in an MSc Thesis Proposal in your Department.
A research proposal usually contains the following key elements:
i. Title
ii. Abstract
iii. Introduction: This consists of
a) Background to the study;
b) Statement of the problem;
c) Research questions
d) Objectives of the study;
e) Hypotheses of the study
f) Scope of the study
iv. Literature Review: This consists of
a) Conceptual review and conceptual framework;
b) Theoretical review and
c) Empirical Literature
v. Methods/Methodology
vi. Results and Discussion
vii. Summary, Conclusion and Recommendations

Question 4:
One of the preliminary pages in the MSc thesis is the abstract. Suppose the title of your MSc thesis is “Macroeconomic Determinants of Industrial Output in ECOWAS sub-region”. Prepare a sample abstract on this title.
Abstract:
This study investigates the macroeconomic determinants of industrial output in ECOWAS sub-region from 1990 to 2021. It employed annual time-series data from the databases of the International Monetary Funds (IMF) and the World Bank. Autoregressive distributed lag approaches were employed to examine the short- and long-run relationship and causality among variables. The empirical results revealed a positive and statistically significant relationship between industry value-added and gross fixed capital formation, number of workers, and foreign direct investment, respectively, while the relationship of the gross domestic product negative and statistically significant. This result is explained by other sectors that contribute more to growth, such as the tourism sector. Practical Implications: These findings provide a better understanding of the determinants of the ECOWAS manufacturing sector. The study would help policymakers to explore the most productive ECOWAS manufacturing industries and adopt economic policies that stimulate investment in the manufacturing sector to increase economic growth and exports.

Question 5:
Following the 6th edition of the APA referencing style, provide a sample reference list containing: a book with one author; a book with ten authors; fifth edition of a book with three authors; a book chapter; a journal article with two authors; two journal articles published by the same author in a given year; an MSc thesis; a conference paper; and a document on the internet.
i. A book with one author:
Orji, T. (2021). The economics of industrialization. Enugu, Nigeria: Pearson Education Nigeria
ii. A book with ten authors:
Orji, T., Odumu, M., Agaba, Y., Okpani, U., Ikpeba, I., Abbah, G., Obi, R., Peter, O., Daniel, M. & James, K. (2020). Impact of inflation on ECOWAS. Enugu, Nigeria: Pearson Education Nigeria
iii. Fifth edition of a book with three authors:
Peter, O., Daniel, M. & James, K. (2019). Economic growth and industrialization in sub-Saharan Africa (5th Ed). Enugu, Nigeria: Pearson Education Nigeria
iv. A book chapter:
Moses, T. (2021). The economics of industrialization. In Orji, T. Principles of economics (pp. 27-60). Enugu, Nigeria: Pearson Education Nigeria
v. A journal article with two authors:
Dudu, I. & Ogogo, K. (2020). Impact of inflation on ECOWAS. Journal of Economics, 12(7), 2-20.
vi. Two journal articles published by the same author in a given year:
Orji, T. (2021a). Impact of foreign direct investment in Nigeria. Journal of economic development, 10(7), 2-20.
Orji, T. (2021b). Empirical investigation of inflationary pressure in sub-Saharan Africa. Journal of economic development, 11(2), 2-20.
vii. An MSc thesis:
Orji, T. (2020). Macroeconomic sustainability in Nigeria (MSc Thesis, The University of Nigeria Nsukka, Enugu, Nigeria).
viii. A conference paper:
Odumu, M. O. (2021). Nexus between inflation and unemployment. Conference proceedings of the National Economic Society, Kano, Nigeria.
ix. A document on the internet:
Emmanuel, G. & Abraham, S. (2021). Navigating through inflation to economic development in ECOWAS. Journal of Economics, 10(7), 18-32. Retrieved on 10th November, 2022 from http://www.successtonics.com/pastordoctorprofessortony/90

Question 6:
Research is an endeavor to discover answers to intellectual and practical problems through the application of scientific method. It is a systematized effort to gain new knowledge or to provide information for solving a problem. It is also a careful inquiry or examination to discover new information or relationships and to expand and to verify existing knowledge. Discuss this comprehensively
Answer
Research is an endeavour to discover answers to intellectual and practical problems through the application of scientific method. It is a systematized effort to gain new knowledge. Research is the systematic process of collecting and analyzing information (data) in order to increase our understanding of the phenomenon about which we are concerned or interested.
The scientific method in research is an experimentation process used in exploring observations and answering questions. Not all scientists follow this process because some areas of science are more straightforward to test than others. For instance, for scientists to hypothesize how dinosaurs digested their food, they cannot fast-forward time and run hypotheses to come to logical conclusions. There are different versions of scientific research conducted by scientists, but the goal is the same at the end of the day. This is to discover the cause and effect of something.
The scientific method in research is iterative, and it follows a series of steps. New information always causes a scientist to back up and repeat some steps in the process.
Scientific research helps us understand the world around us better. Without it, we would be forced to rely on intuition. Unlike intuition and personal experience, in scientific research, knowledge is empirically grounded in tangible evidence that can be observed continually.
Process of Scientific Research
The scientific method follows six necessary steps to test ideas. The steps are as follows:
i) Observe a phenomenon and come up with a question on it. This is where the scientific method starts, and you ask questions on something you observe.
ii) Form a hypothesis that will answer your question or suggest a possible solution. Instead of starting from scratch, you can do background research and be savvy to not repeat the same mistakes in the past. After your research, you can then form a hypothesis which is an educated guess or attempt to answer your question.
iii) Experiment to test your hypothesis. After the hypothesis, experiment, because intuition is unacceptable in scientific circles. This is to assert whether your prediction is true or false. During your investigation, change only one variable at a time and keep all the other conditions the same.
iv) Analyze the results. If your hypothesis is correct, find more evidence and counter-evidence. If your premise is false, develop a new hypothesis and start over again. Repeat the experiment severally to ensure the results were not accidental.
v) Draw your conclusion and repeat the process. No scientific approach is considered perfect.
vi) After completing the experiment, collect the results, come up with a conclusion, and see if it supports your hypothesis. Sometimes scientists find that their hypotheses were unfounded, and in such cases, they have to go back to the experiment and come up with a new inference and prediction based on the latest information learned.
Why the Scientific Method is Important in Research?
Modern Scientific research separates the inquiries of the mind from factual conclusions. Most of the aspects in science were once subjective and could not be directly measured. Scientists relied on self-reporting and observations only. The scientific method’s dawn made it possible to analyze different kinds of data and standardize the approach used.
This method allows data to be replicated and verified by different scientists under different circumstances. By repeating the findings, scientists can broaden the theories and reduce margins of error. Theories can be further tested to give further understanding instead of just being conjectures. Good research is ethical as it acts as a method of preservation of hallmarks of humanity in academia.
Question 7:
Comprehensively discuss the characteristics and types of research
Answer
Research is categorized into two sections these are basic and Applied research:
A) Basic Research: You can perform such a type of investigation for collecting the facts for increasing knowledge about a particular field. For instance, Research performs an investigation for identifying the trends in the market.
B) Applied/Experimental Research: It is a type of research that you can perform for examining and resolving issues in real life. In such a type of research, you can use scientific techniques for resolving the issue. Applied research helps analyze the ways things are changed.
Types of Basic and Applied Research
The basic and applied research can be categorized into three types these are:
1. Qualitative Research: It is an inquiry conducted for gathering information on a specific topic. By conducting Qualitative research, you will be able to acquire a huge volume of information. This type of investigation will help you in developing a good understanding of the problem.
Qualitative research allows you to analyze and sees things in a natural setting. Observing things in the natural setting is very much crucial for generating accurate results.
The few most popular techniques which are utilized for gathering facts related to the subject are surveys, interviews, focus groups, questionnaires, observation, etc. In short, Qualitative research is a proper technique for the accumulation and analysis of facts done to produce meaningful conclusions.
2. Quantitative Research: In this type of investigation, the statistical and mathematical techniques you can use for gathering and analyzing numerical data. You need to include a large population of Quantitative research.
Close-ended questions are generally included in the Questionnaire. The few most popular methods which are utilized for the collection and analysis of Quantitative data include polls, online surveys, and questionnaires. Descriptive research: Such type of investigation. Mainly concentrates on explaining the specific cause.
3. Mixed Research: It is a type of investigation which mainly involves a mixture of Qualitative and Quantitative research. It includes a mixture of images and words.
4. Exploratory Research: It is an investigation conducted for exploring the hidden facts. The biggest drawback of such a type of investigation is that you might face difficulty in reaching a specific conclusion.
5. Correlational Research: It is a type of investigation that students can perform to the identification of the relationship between different variables of the study. It is a kind of research that you can execute for developing an understanding of the relationship between cause and effect. For example, Investigation was conducted for analyzing the effect of social media on children’s studies.
6. Descriptive Research: It provides the researcher with ease in describing the attitude of people selected as participants. In descriptive research, only one variable is needed for facilitating an investigation on a particular topic.
7. Ethnographic Research: It is research conducted on people having different cultural backgrounds. The main objective of such type of investigation is to accumulate, describe and analyze information for creating a new hypothesis related to cultural behaviour.
8. Explanatory Research: The objective of the study is to analyze the influence of one variable of the study on others. For instance, the Effect of the installation of new machines for creating goods on the productivity of the firm.
9. Problem-Solving Research: Such type of research is performed by a special team in firms for analyzing and solving business issues
10. Problem-Oriented Research: This type of research is executed for developing the understanding of the nature of the issue Problem-oriented research is also performed for identifying a suitable way of resolving the specific problem. The issue here refers to the selection of one option from several available choices.
11. Causal Research: Such types of investigation are performed for developing an understanding of the relationship between cause and effect.
Characteristics Of Research
i) A systematic technique for capturing correct data is used in good research. When making observations or concluding, researchers must follow a code of ethics and conduct.
ii) The analysis is based on logic and employs both inductive and deductive reasoning techniques.
iii) Actual observations in natural environments are used to generate real-time data and knowledge.
iv) There is a thorough examination of all data obtained to ensure that there are no irregularities. It paves the way for new questions to emerge.
v) Existing data aids in the development of new research prospects. It is analytical and makes use of all available facts to ensure that inferences are clear.
vi) One of the most important parts of research is accuracy. The data must be exact and complete. Laboratories, for example, provide a controlled setting in which to collect data.
vii) The accuracy of the instruments employed, the calibrations of instruments or tools, and the result of the experiment are all factors to consider.

Question 8:
At each operational step in the research process, you are required to choose from a multiplicity of methods, procedures and models of research methodology which will help you to best achieve your objectives. In view of this clearly discuss the research process and what it entails.
Answer
Research writing involves organization and a logical flow of ideas, so understanding the logic of the research process before beginning to write is essential.
Tips to consider while selecting a research methodology
i) A clear objective should be in mind before beginning any research, as a good research question will be precise, applicable, and valuable. This indicates that you can fully address the issue and base judgments thereon.
ii) For the study to be reliable, the researcher must ensure proper representation of the relevant population. You must create inclusive surveys and questionnaires to ensure proper representation.
iii) Your interview questions should gather the necessary data and subtly address your research questions. Building rapport with the subjects is essential to accomplish this.
iv) Another consideration is the time you have available. If there are time restrictions, consider methods like convenience or random sampling and tools that enable data collection in a few days. In-person interviews and observations are options for data collection if more time is available.
During the research, the researcher employs several methods to explain the ideas; we will study the various types in this article. The selection of the method is entirely up to the researcher, and there are no restrictions on the type.
Different Types of Research Methodology and their Advantages
a) These various methodologies help attain the aim of the research, which is effectively analyzing the subject data. They provide verification and fact-checking, which is vital in a critical evaluation. It also presents different perspectives to view a matter.
b) Surveys are quantitative methods that help obtain numerical statistics regarding research. With the help of numerical surveys, researchers can skip elaborate theses and achieve the necessary results without much hassle.
c) Qualitative methods, including case studies, help to gain insight into the study. The researcher can delve deeper into the subject and study the behavioral pattern directly.
d) The different methods help validate the research and improve its reliability. It also helps generalize the analysis for the majority of the audience.
Conclusion
The researchers make developments in all the fields, and their studies are helpful to society in general. Understanding the research methods is essential to verify and validate the research in different fields. Additionally, it offers a thorough plan that aids in keeping researchers on the course, facilitating a simple, efficient, and manageable process.
Question 9:
Clearly discuss the Measurement Scales of Variable and common errors of measurement in research
Answer
Measurement Scales of Variable
In Statistics, the variables or numbers are defined and categorised using different scales of measurements. Each level of measurement scale has specific properties that determine the various use of statistical analysis. In this article, we will learn four types of scales such as nominal, ordinal, interval and ratio scale.
Nominal Scale
A nominal scale is the 1st level of measurement scale in which the numbers serve as “tags” or “labels” to classify or identify the objects. A nominal scale usually deals with the non-numeric variables or the numbers that do not have any value.
Characteristics of Nominal Scale
i) A nominal scale variable is classified into two or more categories. In this measurement mechanism, the answer should fall into either of the classes.
ii) It is qualitative. The numbers are used here to identify the objects.
iii) The numbers don’t define the object characteristics. The only permissible aspect of numbers in the nominal scale is “counting.”
Ordinal Scale
The ordinal scale is the 2nd level of measurement that reports the ordering and ranking of data without establishing the degree of variation between them. Ordinal represents the “order.” Ordinal data is known as qualitative data or categorical data. It can be grouped, named and also ranked.
Characteristics of the Ordinal Scale
i) The ordinal scale shows the relative ranking of the variables.
ii) It identifies and describes the magnitude of a variable.
iii) Along with the information provided by the nominal scale, ordinal scales give the rankings of those variables.
iv) The interval properties are not known.
v) The surveyors can quickly analyse the degree of agreement concerning the identified order of variables.
Interval Scale
The interval scale is the 3rd level of measurement scale. It is defined as a quantitative measurement scale in which the difference between the two variables is meaningful. In other words, the variables are measured in an exact manner, not as in a relative way in which the presence of zero is arbitrary.
Characteristics of Interval Scale:
i) The interval scale is quantitative as it can quantify the difference between the values.
ii) It allows calculating the mean and median of the variables.
iii) To understand the difference between the variables, you can subtract the values between the variables.
iv) The interval scale is the preferred scale in Statistics as it helps to assign any numerical values to arbitrary assessment such as feelings, calendar types, etc.
Ratio Scale
The ratio scale is the 4th level of measurement scale, which is quantitative. It is a type of variable measurement scale. It allows researchers to compare the differences or intervals. The ratio scale has a unique feature. It possesses the character of the origin or zero points.
Characteristics of Ratio Scale:
i) Ratio scale has a feature of absolute zero.
ii) It doesn’t have negative numbers, because of its zero-point feature.
iii) It affords unique opportunities for statistical analysis. The variables can be orderly added, subtracted, multiplied, divided. Mean, median, and mode can be calculated using the ratio scale.
iv) Ratio scale has unique and useful properties. One such feature is that it allows unit conversions like kilogram – calories, gram – calories, etc.
Designing a research project takes time, skill and knowledge. If you don’t go into the process with a clear goal and methods, you’ll likely come out with skewed data or an inaccurate picture of what you were trying to accomplish.
5 common errors in the research process:
1) Population Specification Errors
Population specification errors occur when the researcher does not understand who they should survey. This can be tricky because there are multiple people who might consume the product, but only one who purchases it, or they may miss a segment looking to purchase in the future.
2) Sampling and Sample Frame Errors
Survey sampling and sample frame errors occur when the wrong subpopulation is used to select a sample, or because of variation in the number or representativeness of the sample that responds, but the resulting sample is not representative of the population concern.
Unfortunately, some element of sampling error is unavoidable, but sometimes, it can be predicted. For instance, in the 1936 presidential election between Roosevelt and Landon, the sample frame was from car registrations and telephone directories. The researchers failed to realize that the majority of people that owned cars and telephones were Republicans, and wrongly predicted a Republican victory.
3) Selection Error
Selection error is the sampling error for a sample selected by a non-probability method. When respondents choose to self-participate in a study and only those interested respond, you can end up with selection error because there may already be an inherent bias. This can also occur when respondents who are not relevant to the study participate, or when there’s a bias in the way participants are put into groups.
4) Non-responsive Error
Nonresponse error can exist when an obtained sample differs from the original selected sample. This may occur because either the potential respondent was not contacted or they refused to respond. The key factor is the absence of data rather than inaccurate data.
5) Measurement Error
Measurement error is generated by the measurement process itself, and represents the difference between the information generated and the information wanted by the researcher. Generally, there is always some small level of measurement error due to uncontrollable factors.

Question 10:
Sampling is very vital in economic analysis. Discuss the two main types of sampling and their various components.
Answer
A sample is a part of the population that is subject to research and used to represent the entire population as a whole. What is crucial here is to study a sample that provides a true picture of the whole group. Often, it’s not possible to contact every member of the population. So, only a sample is studied when conducting statistical or marketing research.
There are two basic types of sampling methods:
i) Probability sampling
ii) Non-probability sampling
Advantages of probability sampling:

i) A comparatively easier method of sampling
ii) Lesser degree of judgment
iii) High level of reliability of research findings
iv) High accuracy of sampling error estimation
v) Can be done even by non-technical individuals
vi) The absence of both systematic and sampling bias.
Disadvantages of probability sampling:
i) Monotonous work
ii) Chances of selecting specific class of samples only
iii) Higher complexity
iv) Can be more expensive and time-consuming.
Types of Probability Sampling Methods
a) Simple Random Sampling
This is the purest and the clearest probability sampling design and strategy. It is also the most popular way of a selecting a sample because it creates samples that are very highly representative of the population. Simple random is a fully random technique of selecting subjects. All you need to do as a researcher is ensure that all the individuals of the population are on the list and after that randomly select the needed number of subjects. This process provides very reasonable judgment as you exclude the units coming consecutively. Simple random sampling avoids the issue of consecutive data to occur simultaneously.
b) Stratified Random Sampling
A stratified random sample is a population sample that involves the division of a population into smaller groups, called ‘strata’. Then the researcher randomly selects the final items proportionally from the different strata. It means the stratified sampling method is very appropriate when the population is heterogeneous. Stratified sampling is a valuable type of sampling methods because it captures key population characteristics in the sample.
c) Systematic Sampling
This method is appropriate if we have a complete list of sampling subjects arranged in some systematic order such as geographical and alphabetical order. The process of systematic sampling design generally includes first selecting a starting point in the population and then performing subsequent observations by using a constant interval between samples taken. This interval, known as the sampling interval, is calculated by dividing the entire population size by the desired sample size.
d) Cluster Random Sampling
This is one of the popular types of sampling methods that randomly select members from a list which is too large. Cluster sampling design is used when natural groups occur in a population. The entire population is subdivided into clusters (groups) and random samples are then gathered from each group. Cluster sampling is a very typical method for market research. It’s used when you can’t get information about the whole population, but you can get information about the clusters.
Non-probability Sampling
Non-probability sampling is a group of sampling techniques where the samples are collected in a way that does not give all the units in the population equal chances of being selected. Probability sampling does not involve random selection at all.
Most commonly, the units in a non-probability sample are selected on the basis of their accessibility. They can be also selected by the purposive personal judgment of you as a researcher.
Advantages of non-probability sampling:
i) When a respondent refuses to participate, he may be replaced by another individual who wants to give information.
ii) Less expensive
iii) Very cost and time effective.
iv) Easy to use types of sampling methods.
Disadvantages of non-probability sampling:
i) The researcher interviews individuals who are easily accessible and available. It means the possibility of gathering valuable data is reduced.
ii) Impossible to estimate how well the researcher representing the population.
iii) Excessive dependence on judgment.
iv) The researchers can’t calculate margins of error.
v) Bias arises when selecting sample units.
vi) The correctness of data is less certain.
vii) It focuses on simplicity instead of effectiveness.
Types of Non-Probability Sampling Methods
There are many types of non-probability sampling techniques and designs, but here we will list some of the most popular.
a) Convenience Sampling
As the name suggests, this method involves collecting units that are the easiest to access: your local school, the mall, your nearest church and etc. It forms an accidental sample. It is generally known as an unsystematic and careless sampling method. This technique is known as one of the easiest, cheapest, and least time-consuming types of sampling methods.
b) Quota Sampling
Quota sampling methodology aims to create a sample where the groups (e.g., male vs. female workers) are proportional to the population. The population is divided into groups (also called strata) and the samples are gathered from each group to meet a quota. For example, if your population has 40% female and 60% males, your sample should consist those percentages. Quota sampling is typically done to ensure the presence of a specific segment of the population.
c) Judgment Sampling
Judgmental sampling is a sampling methodology where the researcher selects the units of the sample based on their knowledge. This type of sampling methods is also famous as purposive sampling or authoritative sampling.
In this method, units are selected for the sample on the basis of a professional judgment that the units have the required characteristics to be representatives of the population.

Question 11:
What are the various elements of model building in Economics?
Model building in economics
Many economics courses and textbooks focus very heavily on models as representations of the economic phenomena under investigation. These could be considered as analogies for the real world, alternative representations which may be similar, in some respects, to the real world. A model in economics is constructed by specifying selected variables, some of which are exogenous and others which are endogenous.
It is important to consider the constraints of a model, including its simplifying assumptions and additional aspects to be considered in the real world. These can be thought of in terms of the nature of framing and the processes that may be followed when analysing issues. This perspective implicitly conveys the limited, partial nature of any approach and hence the possibility of numerous, similarly limited, alternatives.

Question 12:
Discuss Quantitative, Qualitative and Mixed Method Research in Economics and distinguish them with appropriate examples.
Quantitative, Qualitative, & Mixed Methods Research Approaches
Generally speaking, qualitative and quantitative approaches are the most common methods utilized by researchers. While these two approaches are often presented as a dichotomy, in reality it is much more complicated. Certainly, there are researchers who fall on the more extreme ends of these two approaches, however most recognize the advantages and usefulness of combining both methods (mixed methods).
Quantitative Research Approaches
A quantitative approach to research is probably the most familiar approach for the typical research student studying at the introductory level. Arising from the natural sciences, e.g., chemistry and biology), the quantitative approach is framed by the belief that there is one reality or truth that simply requires discovering, known as realism. Therefore, asking the “right” questions is key. Further, this perspective favours observable causes and effects and is therefore outcome-oriented. Typically, aggregate data is used to see patterns and “truth” about the phenomenon under study. True understanding is determined by the ability to predict the phenomenon.
Qualitative Research Approaches
On the other side of research approaches is the qualitative approach. This is generally considered to be the opposite of the quantitative approach. Qualitative researchers are considered phenomenologists, or human-centred researchers. Any research must account for the humanness, i.e., that they have thoughts, feelings, and experiences that they interpret of the participants. Instead of a realist perspective suggesting one reality or truth, qualitative researchers tend to favour the constructionist perspective: knowledge is created, not discovered, and there are multiple realities based on someone’s perspective. Specifically, a researcher needs to understand why, how and to whom a phenomenon applies. These aspects are usually unobservable since they are the thoughts, feelings and experiences of the person. Most importantly, they are a function of their perception of those things rather than what the outside researcher interprets them to be. As a result, there is no such thing as a neutral or objective outsider, as in the quantitative approach. Rather, the approach is generally process-oriented. True understanding, rather than information based on prediction, is based on understanding action and on the interpretive meaning of that action.
Differences between quantitative and qualitative approaches (from Adjei, n.d).
Quantitative Qualitative
Tests hypotheses that the researcher generates Discovers and encapsulates meanings once the researcher becomes immersed in the data.
Concepts are in the form of distinct variables. Concepts tend to be in the form of themes, motifs, generalizations, and taxonomies. However, the objective is still to generate concepts.
Measures are systematically created before data collection and are standardized as far as possible; e.g. measures of job satisfaction Measures are more specific and may be specific to the individual setting or researcher; e.g. a specific scheme of values.
Data are in the form of numbers from precise measurement Data are in the form of words from documents, observations, and transcripts. However, quantification is still used in qualitative research
Theory is largely causal and is deductive. Theory can be causal or non-causal and is often inductive
Procedures are standard, and replication is assumed. Research procedures are particular and replication is difficult.
Analysis proceeds by using statistics, tables, or charts and discussing how they relate to hypotheses. Analysis proceeds by extracting themes or generalizations from evidence and organizing data to present a coherent, consistent picture. These generalizations can then be used to generate hypotheses
Question 13:
Clearly discuss the concept of Time Series Data, Cross Sectional and Panel Data, collection and transformation, including the ICT tools involved.
Time Series
A time series is a sequence of data points that occur in successive order over some period of time. In investing, a time series tracks the movement of the chosen data points, such as a security’s price, over a specified period of time with data points recorded at regular intervals. There is no minimum or maximum amount of time that must be included, allowing the data to be gathered in a way that provides the information being sought by the investor or analyst examining the activity.
Time Series Analysis
Suppose you wanted to analyze a time series of daily closing stock prices for a given stock over a period of one year. You would obtain a list of all the closing prices for the stock from each day for the past year and list them in chronological order. This would be a one-year daily closing price time series for the stock.
You might analyze time series data with technical analysis tools to know whether the stock’s time series shows any seasonality. This will help to determine if the stock goes through peaks and troughs at regular times each year. Analysis in this area would require taking the observed prices and correlating them to a chosen season. This can include traditional calendar seasons, such as summer and winter, or retail seasons, such as holiday seasons.
Cross-Sectional Analysis
Cross-sectional analysis is a type of analysis where an investor, analyst or portfolio manager compares a particular company to its industry peers. Cross-sectional analysis may focus on a single company for head-to-head analysis with its biggest competitors or it may approach it from an industry-wide lens to identify companies with a particular strength. Cross-sectional analysis is often deployed in an attempt to assess performance and investment opportunities using data points that are beyond the usual balance sheet numbers.
Cross-Sectional Analysis
When conducting a cross-sectional analysis, the analyst uses comparative metrics to identify the valuation, debt-load, future outlook and/or operational efficiency of a target company. This allows the analyst to evaluate the target company’s efficiency in these areas, and to make the best investment choice among a group of competitors within the industry as a whole.
Analysts implement a cross-sectional analysis to identify special characteristics within a group of comparable organizations, rather than to establish relationships. Often cross-sectional analysis will emphasize a particular area, such as a company’s war chest, to expose hidden areas of strength and weakness in the sector. This type of analysis is based on information-gathering and seeks to understand the “what” instead of the “why.” Cross-sectional analysis allows a researcher to form assumptions, and then test their hypothesis using research methods.
Panel Data
Panel data is a type of data that professionals collect by observing particular variables over a period of time at a regular frequency. This data can help experts establish trends, make correlations and guide further analysis of the variables included in the panel data. For example, panel data may comprise annual income information and the age of individuals over a nine-year period. This data may allow you to establish a connection between age and average income or contribute to the analysis of a related subject, such as age and employment rates.
Uses
You can use panel data for various reasons, but professionals often use it for statistical, financial and economic research. In all fields of study, you may analyze panel data to elicit specific conclusions or to allow future researchers access to your data for their studies. Here are some common uses of panel data

Question 14:
Grant writing is very important for researchers globally. In view of this clearly discuss the Basic Elements of Grant Writing, identify key elements of successful grant writing and analyse how to determine the right funding for your proposal.
Answer
Successful grant writing involves the coordination of several activities, including planning; searching for data and resources; writing and packaging a proposal; submitting a proposal to a funder; and follow-up.
Basic Elements of Grant Writing
i) Identify key elements of successful grant writing
ii) Determine the right funding for your proposal
iii) What the funder is seeking
iv) Key elements of a Grant
Important Elements of Grant Proposal
i) It must be clear
ii) It must be precise
iii) It must be persuasive
Identify the Need for Funding
Firstly, it’s necessary to identify the need for funding. You might have an issue to resolve in the community or an idea for a business startup. Why do you feel the need to resolve this issue or what about this idea makes it worth investing?
For this, you need a grant, which requires a well-written grant proposal. You can easily find some professional grant proposal samples on the web—however, it’s best to plan and propose according to your project needs.
In this part of the funding proposal, you must define the issue you’re addressing. For example, suppose you want to initiate a business that recycles waste products and contributes to waste reduction in the city. Here, you have the opportunity to share the benefits of your startup and how it can facilitate the people.
Moreover, when defining the needs, you must address why this issue hasn’t been resolved in the past? If you can share the statistics and your knowledge, it can easily convince the financiers or donors to agree with your potch.
At this point, compelling and convincing evidence can make your job easier. So, don’t take this lightly. Give your best in this part.
Moreover, for every grant, you need to ensure that the stakeholders accept the existence of a problem, or they get convinced by your startup idea. It will be very challenging to get a grant if they don’t.  
References
Eoghan, Ryan (2022). Research Objectives | Definition & Examples (scribbr.com). https://www.scribbr.com/research-process/research-objectives/
Fresh Proposal (2022). How to Write a Proposal for Funding | Fresh Proposals. https://www.freshproposals.com/write-a-proposal-for-funding/
Indeed Editorial Team (2022). Research Objectives: Definition and How To Write Them | Indeed.com. https://www.indeed.com/career-advice/career-development/research-objectives#:~:text=Research%20objectives%20are%20the%20outcomes%20that%20you%20aim,can%20help%20your%20organization%20achieve%20its%20overall%20goals.
Apple Pages (2022). 31+ SAMPLE Funding Proposals in PDF | MS Word | Google Docs | Apple Pages. https://www.sample.net/business/proposal/funding-proposal/
AngelHack (2021). 7 Key Steps of Writing a Funding Proposal for Your Startup – AngelHack. https://angelhack.com/blog/7-key-steps-of-writing-a-funding-proposal-for-your-startup.html/

ANSWER NO 8
The process of conducting research entails a number of acts or processes that must be taken in the precise order that is intended. It’s important to keep in mind that the many processes in a research process are neither exclusive of one another nor distinct from one another. They do not always follow in any particular order, and the researcher must constantly anticipate the requirements of the upcoming phases at each stage of the research process. However, the following sequence of phases provides a valuable procedural guidance for the research process: (1) developing the research problem; (2) conducting an extensive literature review; (3) developing the hypothesis; (4) preparing the research design; (5) determining sample design; (6) collecting data; (7) project execution; (8) data analysis; (9) hypothesis testing; (10) generalizations and interpretation; and (11) report or presentation of the results.

1. Formulating the research problem: There are two kinds of research problems: those that deal with natural conditions and those that deal with relationships between variables. The researcher must first identify the problem he wishes to investigate, i.e., the general area of interest or facet of a subject-matter into which he wishes to delve. The problem can be expressed broadly at first, and then any ambiguities related to the problem can be clarified. The practicality of a certain solution must therefore be addressed before establishing a working formulation of the problem. The transformation of a broad topic into a specific research problem is thus the first stage in scientific investigation. In essence, there are two processes involved in framing the research problem: properly grasping the problem and rephrasing it in meaningful words from an analytical standpoint.
2. Extensive literature survey: Once the problem has been defined, a succinct summary should be written down. A research worker writing a thesis for a Ph.D. degree must prepare a synopsis of the topic and submit it for approval to the appropriate Committee or Research Board. At this point, the researcher should do an exhaustive literature search related to the subject. The first place to look for this information is in abstracting and indexing journals, as well as published or unpublished bibliographies. Depending on the nature of the problem, academic publications, conference proceedings, government papers, books, and so on must be consulted. It is important to note that one source will lead to another during this process. Previous studies that are similar to the current topic, if any, should be thoroughly examined. At this time, an excellent library will be of tremendous assistance to the researcher.
3. Development of working hypotheses: After a thorough review of the literature, the researcher should clearly identify the working hypothesis or hypotheses. A working hypothesis is a speculative assumption that is made to explore and test the logical or empirical ramifications of that assumption. As a result, as they serve as the main focus of study, how research hypothese are formed is very crucial. They have an indirect impact on the quality of the data needed for the analysis as well as the way tests must be carried out throughout data analysis. The creation of a working hypothesis is crucial for the majority of study kinds. Since hypotheses must be tested, they should be highly narrow and limited to the piece of study at hand. The hypothesis’s job is to guide the researcher by limiting the scope of the inquiry and keeping him on track. It sharpens his thoughts and directs his attention to the more critical aspects of the situation. It also specifies the type of data requested and the data analysis procedures to be employed.
4. Preparing the research design: Following clearly defining the research subject, the researcher will need to develop a research design, in which he will need to specify the conceptual framework within which the research will be carried out. The creation of such a design makes it easier for research to produce the most knowledge while being as efficient as feasible. In other words, the goal of study design is to make it possible to get relevant data with the least amount of work, time, and expense. But how all these can be achieved depends mainly on the research purpose. Research purposes may be grouped into four categories, viz., (i) Exploration, (ii) Description, (iii) Diagnosis, and (iv) Experimentation.
5. Determining sample design: A ‘universe’ or ‘population’ is made up of all the elements under examination in any subject of study. A census enquiry is a full enumeration of all the items in the ‘population.’ It can be assumed that when all of the things are covered, no element of chance remains and the highest accuracy is reached. In practice, however, this may not be the case. Even the smallest element of bias in such an investigation will grow more and larger as the number of observations grows. Furthermore, there is no way to check the element of bias or its extent without doing a resurvey or using sample checks. Furthermore, this form of investigation takes a significant amount of time, money, and energy. Not only that, but in many cases, census inquiries are not feasible. Blood testing, for example, is only done on a sample basis. As a result, we frequently choose only a few elements from the universe to study. The objects chosen constitute what is known technically as a sample.
6. Collecting the data: In dealing with any real-world situation, it is frequently discovered that the data at hand is insufficient, necessitating the collection of appropriate data. There are various methods for gathering acceptable data, each with its own set of expenses, time, and other resources available to the researcher. Primary data might be gathered through an experiment or a survey. When a researcher conducts an experiment, he collects quantitative measurements, or data, with which he tests the truth of his hypothesis.
7. Analysis of data: After gathering the data, the researcher moves on to the task of analyzing it. Data analysis necessitates a variety of closely related procedures, including the creation of categories, the application of these categories to raw data via coding, tabulation, and the drawing of statistical inferences. For further examination, the bulky data must be condensed into a few digestible groups and tables. As a result, researchers should organize the raw data into meaningful and useable categories. Coding is typically performed at this stage, in which data categories are turned into symbols that may be tabulated and counted. Editing is the process of improving the quality of data for coding. The stage is now ready for tabulation thanks to the code. Tabulation is a technological operation that involves arranging classified data into tables. At this point, mechanical devices can be utilized. Computers tabulate a vast amount of data, particularly in large investigations. Computers not only save time, but also allow you to investigate a vast number of variables affecting a problem at the same time.
8. Generalisations and interpretation: If a hypothesis is tested and supported multiple times, the researcher may be able to get at generalisation, i.e., create a theory. In reality, the true usefulness of research resides in its ability to reach specific generalizations. If the researcher started with no hypothesis, he can try to explain his data using some theory. It is referred to as interpretation. The process of interpretation frequently generates new questions, which may lead to additional research.
9. Preparation of the report or the thesis: Finally, the researcher has to prepare the report of what has been done by him. Writing of report must be done with great care keeping in view the following: 1. The layout of the report should be as follows:
(i) The preliminary pages; (ii) the main text, and (iii) the end matter.
In its preliminary pages the report should carry title and date followed by acknowledgements and foreword. Then there should be a table of contents followed by a list of tables and list of graphs and charts, if any, given in the report. The main text of the report should have the following parts:
(a) Introduction: It should contain a clear statement of the objective of the research and an explanation of the methodology adopted in accomplishing the research. The scope of the study along with various limitations should as well be stated in this part.
(b) Summary of findings: After introduction there would appear a statement of findings and recommendations in non-technical language. If the findings are extensive, they should be summarized.
(c) Main report: The main body of the report should be presented in logical sequence and broken-down into readily identifiable sections.
(d) Conclusion: Towards the end of the main text, researcher should again put down the results of his research clearly and precisely. In fact, it is the final summing up.
 
ANSWER NO 9
The measurement scale of variables can be considered in terms of their mathematical properties. The most widely used classification of measurement scales are: (a) nominal scale; (b) ordinal scale; (c) interval scale; and (d) ratio scale.
(a) Nominal scale: Nominal scale is just a mechanism for labeling events by assigning number symbols to them. The most common example is the assigning of numbers to basketball players in order to identify them. Such numbers cannot be deemed to be associated with an ordered scale since their order is irrelevant; the numbers are only convenient labels for a certain class of events and so have no quantitative value. Nominal scales are useful for keeping track of persons, items, and events. With the statistics involved, there isn’t much one can do. For example, one cannot generate a meaningful value by averaging the numbers on the backs of a set of football players. Neither can the numbers assigned to one group be properly compared to the numbers assigned to another.
(b) Ordinal scale: The ordinal scale is the lowest level of the ordered scale that is often employed. The ordinal scale arranges events in chronological order, but there is no attempt to make the scale’s intervals equal according to some criterion. Ordinal scales are represented by rank orders, which are widely employed in qualitative research. An ordinal scale is used to determine a student’s rank in his graduating class. When making statements regarding scores based on ordinal scales, one must use extreme caution. For example, if Ram’s position in his class is 10 and Mohan’s position is 40, Ram’s position cannot be claimed to be four times better than Mohan’s. The statement would be completely illogical. Ordinal scales only allow items to be ranked from highest to lowest. Ordinal measurements may not have absolute values, therefore real differences between adjacent ranks may not be equal. All that can be said is that one individual is higher or lower on the scale than another, but precise comparisons are impossible to make.
(c) Interval scale: In the case of interval scale, the intervals are modified in accordance with a rule established as a basis for making the units equal. The units are equivalent only if the assumptions on which the rule is based are accepted. Interval scales can have an arbitrary zero, but they cannot have what is known as an absolute zero or the unique origin. The interval scale’s fundamental shortcoming is the lack of a genuine zero; it cannot measure the total absence of a feature or traits.
(d) Ratio scale: The absolute or real zero of measurement on ratio scales. The word “absolute zero” is no longer as exact as it formerly was. We can imagine an absolute zero of length, just as we can imagine an absolute zero of time. The zero point on a centimeter scale, for example, denotes the entire absence of length or height. However, absolute zero temperature is theoretically impossible to achieve and remains a concept only in the minds of scientists. The number of minor traffic offences and the number of erroneous letters on a typescript page represent ratio scale scores.

COMMON ERRORS IN MEASUREMENT
There are six sorts of errors that can occur while measuring a physical quantity: constant error, systematic error, random error, absolute error, relative error, and percentage error. Each sort of measurement inaccuracy is described below.
a) Constant error
Constant errors are those that have the same effect on the outcome every time.For example, if a thermometer reads 10 C when placed in melting ice at normal pressure, the instrument has a 10 C inaccuracy.
b) Systematic error
Systematic errors are caused by known causes according to a specified law and tend to be either positive or negative. We can reduce systematic errors by using better instruments, refining experimental methodologies or processes, and eliminating personal errors as much as possible. These systematic mistakes can be estimated to some extent for a given experimental setup, and the necessary corrections can be made to the observed values.
Types of systematic error
There are four sources or types of systematic error: Instrumental error, gross error, error due to external causes and the error due to imperfections.
1. Instrumental error
Instrumental errors are mistakes caused by the equipment or measurement instruments used. It could be inaccuracies caused by an improper design or calibration of the measurement device, a zero error in the instrument, or anything else. It is determined by the measurement instrument’s limit or resolution. For example, employing a metre scale with graduations at one mm intervals limits the precision of the reading to one mm. The mistake in reading the metre scale is assumed to be of the order of half of the smallest division on the scale, or 0.5 mm.
2 Gross error
Another sort of systematic error is the gross error, which is committed owing to personal idiosyncrasies of the experiment, such as carelessness in taking observations without taking essential measures or a lack of appropriate setting of the measuring devices, and so on. The personal error or observation error is another name for the enormous error. For example, while reading the instrument meter, the observer may read 41 as 47.
3 Error due to external causes
These errors develop as a result of changes in environmental factors such as temperature, wind speed, pressure, humidity, electric field, or magnetic field, among other.For instance: During the summer, the iron meter scale’s length increases by more than a meter.

c) Random error
Random error refers to the inaccuracy caused by the individual taking the measurement. The random error is affected by the measurer’s qualities and the attention given throughout the measuring process. Another name for it is the chance error. The measurements are repeated many times to decrease random errors, and the average (arithmetic mean) value is regarded as the true value of the quantity being measured. The most precise reading would be quite close to the average value. The random error is decreased to 1/n times when ‘n’ observations are made.
When a physical quantity is measured and given n different readings, a1, a2, a3,… an, its arithmetic mean value, which is given by

d) Absolute error
The absolute error in measurement is the magnitude of the difference between the true value of the quantity and the measured value. Because the true value of the quantity is unknown, the arithmetic mean of the measured values can be used to get the true value.
If a1, a2, ….. are the measured values of a certain quantity, the errors in ∆a1, ∆a2, ……… in the measurements are
∆a1 = amean – a1
∆a2 = amean – a2
…………………………………
The arithematic mean of all the absolute errors is taken as the final absolute error in the measurement and is known as mean absolute error. The relative error or fractional error is the ratio of the absolute error to the true value of the measured quantity. Because the arithmetic mean value is assumed to be correct, the relative error is given by,

The value obtained in a single measurement may be in the range
amean ± ∆ amean
e) Relative error

f) Percentage error
It is the relative error exprressed in percentage.

Example:
When the diameter of a wire is measured using a screw gauge, the successive readings are found to be 1.10 mm, 1.12 mm, 1.14 mm, 1.08 mm, 1.16mm and 1.17mm. Calculate the absolute errors and the relative error in the measurement.
 
ANSWER NO 10
Sampling is a technique for choosing certain individuals or a small portion of the population in order to draw conclusions about the population as a whole and estimate its characteristics. Researchers frequently utilize various sampling techniques in market research so they do not have to study the full community in order to gather useful information. It serves as the foundation of any research design because it is also a time- and money-efficient strategy. For the best derivation, sampling techniques can be utilized in research survey software.

For example, It is nearly impossible to carry out a research study that includes every member of the population if a drug maker wants to investigate the negative side effects of a drug on the nation’s population. In this instance, the researcher selects a sample of individuals from each demographic, studies them, and then provides him or her with indicative feedback on the drug’s behavior.
Types of sampling:
Probability sampling: Probability sampling is also referred to as ‘random sampling’ or ‘chance sampling.’ Every item in the universe has an equal chance of being included in the sample under this sampling design. It is, in a sense, a lottery method in which individual units are drawn at random from the entire group rather than deliberately. In this case, only chance determines whether one item or the other is chosen. The results of probability or random sampling can be assured in terms of probability, i.e., we can measure estimation errors or the significance of results obtained from a random sample, and this fact highlights the superiority of random sampling design over deliberate sampling design. The law of statistical regularity, which asserts that if on average a sample is picked at random, the sample will have the same composition and features as the universe, is ensured by random sampling. This is why choosing a representative sample through random sampling is thought to be the best method.
The method of sample selection known as “random sampling from a finite population” ensures that every potential sample combination has an equal chance of being chosen and that every item in the population as a whole has an equal chance of being included in the sample. This is true for sampling without replacement, where once an item is chosen for the sample, it cannot be chosen again (Sampling with replacement, which is less common, involves returning the element chosen for the sample to the population before choosing the next element). In such a situation the same element could appear twice in the same sample before the second element is chosen). In brief, the implications of random sampling (or simple random sampling) are:
(a) It gives each element in the population an equal probability of getting into the sample; and all choices are independent of one another.
(b) It gives each possible sample combination an equal probability of being chosen.
Types of probability sampling with examples
Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the same probability of being chosen to be a part of a sample.
For example, in an organization of 500 employees, if the HR team decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected.

Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inference from the feedback.
For example, if the United States government wishes to evaluate the number of immigrants living in the Mainland US, they can divide it into clusters based on states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be more effective as the results will be organized into states and provide insightful immigration data.

Systematic sampling: The systematic sampling approach is used by researchers to randomly select a population’s sample members. It necessitates choosing a sample size and starting point that can be repeated on a regular basis. This sort of sampling strategy is the least time-consuming because it uses a specified range.
For instance, a researcher might want to systematically interview 500 persons from a population of 5000. Each component of the population is assigned a number between 1 and 5000, and every tenth person is chosen to be a member of the sample (Total population/Sample Size = 5000/500 = 10).

Stratified random sampling: Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized and then draw a sample from each group separately.
For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results.Non-probability sampling: Non-probability sampling is that sampling procedure which does not afford any basis for estimating the probability that each item in the population has of being included in the sample. Non-probability sampling is also known by different names such as deliberate sampling, purposive sampling and judgement sampling. In this type of sampling, items for the sample are selected deliberately by the researcher; his choice concerning the items remains supreme. In other words, under non-probability sampling the organisers of the inquiry purposively choose the particular units of the universe for constituting a sample on the basis that the small mass that they so select out of a huge one will be typical or representative of the whole. For instance, if economic conditions of people living in a state are to be studied, a few towns and villages may be purposively selected for intensive study on the principle that they can be representative of the entire state. Thus, the judgement of the organisers of the study plays an important part in this sampling design.
 
ANSWER NO 11
Elements of Economic Model
An economic model is a simplified description of reality, designed to yield hypotheses about economic behavior that can be tested. An important feature of an economic model is that it is necessarily subjective in design because there are no objective measures of economic outcomes. Different economists will make different judgments about what is needed to explain their interpretations of reality.
There are two broad classes of economic models—theoretical and empirical. Theoretical models seek to derive verifiable implications about economic behavior under the assumption that agents maximize specific objectives subject to constraints that are well defined in the model (for example, an agent’s budget). They provide qualitative answers to specific questions—such as the implications of asymmetric information (when one side to a transaction knows more than the other) or how best to handle market failures.
In contrast, empirical models aim to verify the qualitative predictions of theoretical models and convert these predictions to precise, numerical outcomes. For example, a theoretical model of an agent’s consumption behavior would generally suggest a positive relationship between expenditure and income. The empirical adaptation of the theoretical model would attempt to assign a numerical value to the average amount expenditure increases when income increases.
Economic models generally consist of a set of mathematical equations that describe a theory of economic behavior. The aim of model builders is to include enough equations to provide useful clues about how rational agents behave or how an economy works (see box). The structure of the equations reflects the model builder’s attempt to simplify reality—for example, by assuming an infinite number of competitors and market participants with perfect foresight. Economic models can be quite simple in practice: the demand for apples, for example, is inversely related to price if all other influences remain constant. The less expensive the apples, the more are demanded. Or models can be rather complex: some models that seek to predict the real level of output of an economy use thousands of complex formulations that go by such names as “nonlinear, interconnected differential equations.”
1. How economists build empirical models?
Despite their diversity, empirical economic models have features in common. Each will allow for inputs, or exogenous variables, which do not need to be explained by the model. These include policy variables, such as government spending and tax rates, or nonpolicy variables, like the weather. Then there are the outputs, called dependent variables (for example, the inflation rate), which the model will seek to explain when some or all of the exogenous variables come into play.
Every empirical model will also have coefficients that determine how a dependent variable changes when an input changes (for example, the responsiveness of household consumption to a $100 decrease in income tax). Such coefficients are usually estimated (assigned numbers) based on historical data. Last, empirical model builders add a catchall variable to each behavioral equation to account for idiosyncrasies of economic behavior at the individual level. (In the example above, agents will not respond identically to a $100 tax rebate.)
There are, however, fundamental differences among economists regarding how an empirical model’s equations should be derived. Some economists insist that the equations must assume maximizing behavior (for example, an agent chooses its future consumption to maximize its level of satisfaction subject to its budget), efficient markets, and forward-looking behavior. Agents’ expectations and how they react to policy changes play a vital role in the resulting equations. Consequently, users of the model should be able to track the effect of specific policy changes without having to worry about whether the change itself alters agents’ behavior.
Other economists favor a more nuanced approach. Their preferred equations reflect, in part, what their own experience has taught them about observed data. Economists that build models this way are, in essence, questioning the realism of the behavioral constructs in the more formally derived models. Incorporating experience, however, often means it’s impossible to untangle the effect of specific shocks or predict the impact of a policy change because the underlying equations do not explicitly account for changes in agent behavior. The gain, these same economists would argue, is that they do a better job of prediction (especially for the near term).
2. Ingredients of a good economic model
Irrespective of the approach, the scientific method (lots of sciences, such as physics and meteorology, create models) requires that every model yield precise and verifiable implications about the economic phenomena it is trying to explain. Formal evaluation involves testing the model’s key implications and assessing its ability to reproduce stylized facts. Economists use many tools to test their models, including case studies, lab-based experimental studies, and statistics.
Still, the randomness of economic data often gets in the way, so economists must be precise when saying that a model “successfully explains” something. From a forecasting perspective that means errors are unpredictable and irrelevant (zero) on average. When two or more models satisfy this condition, economists generally use the volatility of the forecast errors to break the tie—smaller volatility is generally preferred.
An objective signal that an empirical model needs to be revised is if it produces systematic forecasting errors. Systematic errors imply that one or more equations of the model are incorrect. Understanding why such errors arise is an important part of the regular assessment economists make of models.

All economic models, no matter how complicated, are subjective approximations of reality designed to explain observed phenomena. It follows that the model’s predictions must be tempered by the randomness of the underlying data it seeks to explain and by the validity of the theories used to derive its equations.
 

ANSWER NO 12
Quantitative Methods: Quantitative Methods is basically numbers and statistics. The advantage here is that you can collect and analyse much more information. With good design, that means you can make general statements about what is likely to be true overall. A drawback can be a lack of depth (e.g. reasons why, context, emotions or feelings). Also, it requires mathematical and/or statistical knowledge to be able to analyse the data effectively. Descriptive statistics (bar graphs, pie charts, etc.) are useful to present the data and inform the reader, but are not usually adequate analytical methods. These only describe your sample. Inferential statistics are used to explain or demonstrate hypotheses in the overall population. Your research design needs to consider what statistical analyses will be performed from the beginning. You need to know at the outset what type of data you will be collecting. For example, if you are collecting data on sickness at work, you could collect this as a ‘yes/no’ type question (e.g. have you been off sick in the last month) or as a scale (e.g. how many days have you been off sick). So the data that you need affects the questions that you ask. When reading articles which use quantitative data, examine the methodology section to see how they have identified their population and sample. Also examine this section to see whether or not their approach supports their conclusion. For example, studies may identify a similarity between two variables (e.g. ice cream sales and murders committed) and conclude that there is a link (although it may be coincidental) or it could be due to a third variable (e.g. heat – murders go up in summer and so do ice cream sales). So always look for alternative explanations for the link or explanation the author is proposing.
Qualitative Methods: Qualitative data includes words, opinions, thoughts, feelings and behaviours. The advantage is that you get lots of detail about specific cases, people or group. The disadvantages are that you can’t make general statements, and that analysis is time consuming. Some would argue that the analysis is also very subjective, but this depends on your approach. When reading a piece of qualitative research, look for the level of detail and clarity in the methodology and particularly how they analysed the data. For example, you will often see ‘thematic analysis’ referred to, but the author should give details about how the themes were identified and on what basis where certain themes kept and others ignored. Also, be alert to what generalisations are made on the basis of very small samples or case studies. A good qualitative research article will have a solid basis in previous research and will compare their results to other studies. It will also include lots of rich detail, usually in the form of quotes or examples, to illustrate their interpretations. So read the results sections carefully, and see whether or not you agree with how they have analysed the data.Which one you should use typically depends on your research questions and topics, as well as your purpose. If your purpose is to explain, measure, and/or prove a link between two different things (e.g. diet and obesity), quantitative data would probably be more appropriate. For example, quantitative data topics might be:
● A company’s profitability
● A comparison of primary school children’s reading marks and family background
● How rates of secondary infection in a hospital ward change in winter
● How many newspaper articles mention immigration in a given period
● The frequency of particular personality types e.g. introversion If your purpose is to explore, illustrate, and/or give rich and detailed information about particular instances, you are probably going to prefer qualitative data.
Qualitative data topics might be:
● Consumer perceptions of a company or brand
● Parents’ feelings and habits about reading to their children
● Nurses’ knowledge and opinions of infection prevention protocols
● How newspaper articles describe and represent immigrants
● How introverts think of themselves Note that all of these topics are much too vague to be really good dissertation or research article titles; they need to be more focused in reality. And purpose depends on previous literature. If previous research shows you that rates of secondary infection in hospital wards usually go up in the winter, you don’t need to repeat that research. But perhaps you might have a new idea about why, which you could test. Consider this when reading articles: was their research really necessary? What new knowledge is the article adding?

Mixed Method: Mixed methods means that you collect both quantitative and qualitative data and analyse both together to answer your question. For instance, say you wanted to know whether parents’ feelings about reading with their children affected children’s reading scores. You could interview parents, sort them into groups (e.g. confident, mixed feelings, and anxious) and compare the test scores of children whose parents fall into different groups. That would be a good mixed methods design, because you are using the qualitative data to inform the analysis of the quantitative data (known as multistage research design).

Alternatively you can ‘triangulate’, or collect different types of data to check your findings or illustrate your interpretations. For instance, you could compare the reading test scores of children, and also interview them and their parents to find out how they feel about reading. This would let you ask parents directly about how they think their
confidence affects their children’s reading, and see whether this matches your statistical analysis. However, not all topics require mixed methods, and it is not always better to use mixed methods. For instance, say you want to know whether a company’s profits are affected by employee absences or not. Since you can count both variables, there is no need for additional qualitative data. Similarly, say you want to know how introverts think of themselves. Would you learn anything relevant by counting the frequency of introverts in a particular social group? Probably not. Occasionally, you will read journal articles which use two different sets of data that never really relate together. If this happens, you can point out in a literature review or critical evaluation that it was perhaps a weak use of a mixed methods design.

ANSWER NO 13

Time-series data
This type of data is explicitly organized in time sequence, generally in equally spaced units such as days, weeks, months, quarters, years, etc.

Figure 1 – Impact of various factors on GDP
Here, data are presented for a single individual (a country in this case) in time sequence order with one row of data for each year. Each row consists of the characteristics under study (GDP, unemployment rate, government expenditures and national debt in Figure 1) for that year.
Cross-sectional data
This is the type of data that are collected about individuals (people, countries, plants, etc.) over a single period of time or over various points in time where no special attention is paid to the time element. Data can be collected using sampling approaches or from historical data sources.

Figure 2 – Relationship between various factors and personal income
The order of the observations doesn’t play any role in the analysis, and, in particular, the roles are not related to time.
Panel (Longitudinal) data
Panel data consists of time-series data for each cross-sectional individual. Once again, individuals can be people, cities, companies, plants, etc. and time can be years, months, weeks, etc. Figure 3 shows the impact of higher fuel standards (in miles per gallon) in 2015 from those in 2010 on auto manufacturers.

Figure 3 – Impact of higher miles per gallon standards
Unlike in pooled cross-sectional data, here the same individuals or units (auto companies in Figure 3) have rows for the different time periods. Although the data in Figure 3 contains two time periods per individual, there can be many more time periods. In a balanced model, for each individual, there will be one row for each time period being studied.

ANSWER NO 14
Elements of a Grant Proposal While specific requirements depend on the funding source, most
grant proposals require the following elements:
Title Page and Cover Letter

Introduction/Abstract/Summary
A brief overview of the proposal with information about the institution, its ability to complete the project, need, methods to be used and how those served will benefit. This is the first thing the reader sees, but it is written last.

Institutional/organisation Background
Describes the institution in terms of its location, demographics, mission, relationship to the service area and past successes in the project area. Establishes credibility.

Problem Statement/Needs Assessment
Documents the problem or need with hard data, linking it to the funding source’s goals and priorities. What is the situation that is causing concern? Why is it happening?

Program Goals and Objectives (Outcomes)
Identifies anticipated outcomes and benefits in measurable terms. How is the situation expected to change as a result of the grant program?

Methods/Implementation Plan
Describes the activities that directly support the achievement of the objectives. A timeline may be included in this section as well as a description on staffing needs.

Evaluation Plan
Presents a plan for determining the success of the project at interim points and at the end of the project.

Future Funding/Sustainability
Describes how expenses not supported by the grant will be covered and how the project effort will continue after the grant ends.

Budget
Identifies the costs to be met by the funding source and the methods used to determine costs.

Other Components
May include items such as:
Letters of support
Personnel resumes
Proof of 501(c)3 status

 
ANSWER 14

Ways to determine the right funding for your Grant Proposals
What funding agencies are you choosing from?
You first need to know which public and private funding agencies support research grants in your field. Then, you need to determine which of these organizations you are eligible to submit to and from which geographical areas they accept applications. Using your key words generated from the development of your concept paper, you should conduct a strategic search of potential public and private funding agencies. Try to narrow down to at most 5–7 of each.
What is the scope of your project?
Now that you have your shortlist of potential funders you can shorten that list by thinking through the scope of your project. Drawing on your concept paper determine whether your project is more exploratory or a full-scale research project, such as an efficacy study or a clinical trial. The scale of your project, together with the funds needed, should help you determine which of the funders on your shortlist provide you with the greatest chance of a successful application. If you are considering a smaller-scale project, with a smaller budget, you might start by reaching out to the private funders on your list and having conversation with them.

Where support is there to help you choose?
Most institutions have a corporate and foundation relations office. The staff in these offices can offer you support in identifying, communicating with, and developing proposals for appropriate foundations and corporations. For these offices, success is based on how much funding they can help to bring into the institution. So, they are motivated to provide support to you and they are familiar with the granting priorities of many private funders. Similarly, there is often support at institutions to help you identify public funders. These staff might work within your college or department, or they might work at the institution level. Identifying all the supports available to you, and beyond, your institution before, and utilizing these supports throughout the process will help you be more successful.
 
REFERENCES

Bound, J., Brown, C., & Mathiowetz, N. (2001). Measurement error in survey data. In Handbook of econometrics (Vol. 5, pp. 3705-3843). Elsevier.

Coccia, M. (2018). An introduction to the methods of inquiry in social sciences. Journal of Social and Administrative Sciences, 5(2), 116-126.

Daniel, B. V., West, J. K., Daniel, G. E., & Flowers, P. (2006). Grant Writing as a Funding Source and Collaborative Endeavor. College Student Affairs Journal, 25(2), 199-208.

Fleetwood, D. (2011). Types of sampling: Sampling methods with examples.

Gale, D. (1989). The theory of linear economic models. University of Chicago press.

Kothari, C. R. (2004). Research methodology: Methods and techniques. New Age International.

Illingworth, S., & Allen, G. (2016). Applying for funding. In Effective Science Communication: A practical guide to surviving as a scientist. IOP Publishing.

Ouliaris, S. (2011). Back to Basics: What Are Economic Models?: How economists try to simulate reality. Finance & Development, 48(002).

Suchman, E. (1968). Evaluative Research: Principles and Practice in Public Service and Social Action Progr. Russell Sage Foundation.

Thanks.
I have seen the assignment.