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Research methods--quantitative, qualitative, and more: overview.

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 25, 2024 11:09 AM
URL: https://guides.lib.berkeley.edu/researchmethods

Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

Provide an overview of the research problem and the need for a research methodology section
Outline the main research questions and objectives

II. Research Design

Explain the research design chosen and why it is appropriate for the research question(s) and objectives
Discuss any alternative research designs considered and why they were not chosen
Describe the research setting and participants (if applicable)

III. Data Collection Methods

Describe the methods used to collect data (e.g., surveys, interviews, observations)
Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or software used for data analysis

V. Ethical Considerations

Discuss any ethical issues that may arise from the research and how they were addressed
Explain how informed consent was obtained (if applicable)
Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

Summarize the key aspects of the research methodology section
Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period.

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization.
Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

types of correlation. Scatter plot. Positive negative and no correlation

If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved.

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way.

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view.

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
Natural : Here, spontaneous behavior is recorded in a natural setting.
Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.
Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

Test-retest reliability : assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
Primary data is first-hand data collected for the purpose of the investigation.
Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
You can easily answer any questions about your research if they arise at a later stage.
A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
It also helps ensure that ethical guidelines are followed while conducting research.
A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

Sampling: The most common type is probability sampling.
Interviews: Commonly telephonic or done in-person.
Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review: Reviewing existing research or documents to collect evidence for supporting the research.
Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

Measures of frequency (count, percent, frequency)
Measures of central tendency (mean, median, mode)
Measures of dispersion or variation (range, variance, standard deviation)
Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

Correlation: To understand the relationship between two or more variables.
Cross-tabulation: Analyze the relationship between multiple variables.
Regression analysis: Study the impact of independent variables on the dependent variable.
Frequency tables: To understand the frequency of data.
Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

Research objectives, aims, and questions —these would help structure the research design.
Review existing literature to identify any gaps in knowledge.
Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
Research method —this can be quantitative, qualitative, or mixed-method.
Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
Sampling —this involves selecting a representative subset of the population being studied.
Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
Data analysis —describe the data analysis methods you will use once you’ve collected the data.
Research limitations —mention any limitations you foresee while conducting your research.
Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

Research design
Data collection procedures
Data analysis methods
Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

Participants should not be subjected to harm.
Respect for the dignity of participants should be prioritized.
Full consent should be obtained from participants before the study.
Participants’ privacy should be ensured.
Confidentiality of the research data should be ensured.
Anonymity of individuals and organizations participating in the research should be maintained.
The aims and objectives of the research should not be exaggerated.
Affiliations, sources of funding, and any possible conflicts of interest should be declared.
Communication in relation to the research should be honest and transparent.
Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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RESEARCH METHODS NOTES

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CHARACTERISTICS OF SCIENTIFIC RESEARCH

Purposiveness: The research must have a definite aim and purpose for achieving objectives.
Rigor: The research must have a good theoretical base and sound methodology that enables collection of the right of information for data analysis.
Testability: This means that hypothesis must be developed after a study of the problem.
Replicability: The results of the research and hypothesis should be supported in subsequent studies conducted under similar circumstances for confidence in the research design.
Precision and confidence: This refers to how close the findings based on a sample are to the reality. the closer the results are to the predicted phenomena, the higher the precision. Confidence refers to the probability that estimates are correct.
Objectivity: Conclusions drawn through interpretation of results of data analysis should be objective and based on facts from actual data collected/
Generalizability: This refers to the scope of applicability of the research findings. The wider the range of applicability of solutions by research, the more useful the research. It depends on the sampling design, instruments used for data collection and objectivity in the interpretation of data.
Parsimony: This is the simplicity in explaining phenomena and challenges that occur in the application of solutions from research outcomes.
Ethical-This is the most important characteristic in carrying out research.

CHARACTERISTICS OF GOOD RESEARCH

Good research generates reliable data and follows the standards of scientific methods, which include:

Clear definition of purpose of the research and research problem. This should include its scope, limitations and definition of terms.
The research process should be described in sufficient detail to permit other researchers to repeat the research.
The research design should be carefully planned to yield objective results. The sample of a population should include evidence of the degree of representation of the sample.
High ethical standards must be applied. A research design must include safeguards against mental or physical harm to participants, exploitation, invasion of privacy and loss of dignity.
Limitations of the study that may arise from research design must be revealed in the research report.
Data analysis should be sufficiently adequate for revealing the significance of the research. Data analysis should give rise to findings and conclusions.
Findings must be presented in clear, precise assertions that are carefully drawn. Presentation of data should be comprehensive and easily understood. Findings should be presented unambiguously
Conclusions must be justified by the data collected from the research, with detailed findings.
The research report should contain information that gives the qualifications of the researcher for greater confidence in research reports.

TYPES OF RESEARCH.

CLASSIFICATION OF RESEARCH.

In business world there exists different kinds of problems. Consequently different types of research are also used. The following are the basic modes of classification:

The field of study in which the research is conducted. i.e. Discipline; for example educational research, sociological research, marketing research etc.
The place where the research is conducted. Hence we talk in forms of field research, laboratory research, community research etc.

3.Application of the research – the way/mode in which the findings of the research will be used eg, Action research(small scale and situational), service research etc A good example is census that is mainly used by the government to plan.

Purpose of the research i.e. basic research (pure and fundamental research), action research, applied research and evaluation research(analyze data to make a decision).
By methods of analysis, i.e., descriptive research(mean,mode,median,variance,standard deviation) and empirical research (practical rather than theory),
Character of data collected i.e. qualitative research and quantitative research.
Procedure/Design used – experimental research, survey research, observation or historical/documentary etc.

TYPES OF RESEARCH

Basic research
It is also referred to as pure or fundamental research.
It is a type of research which is characterized by a desire to know or to expound the frontiers of knowledge.
It is research based on the creation of new knowledge.
It is mainly theoretical and for advancement of knowledge.
Basic researchers are interested in deriving scientific knowledge which will be a broad base for further research.

2.Applied Research

The type of research which is conducted for purpose of improving present practice, normally applied research is conducted for the purposes of applying or testing theory and evaluating its usefulness in solving problems.
Applied research provides data to support theory or suggest the development of new theories. It is the research done with the intention of applying the results of its findings to solve specific problems, currently being experienced in an Organization.

3. Action Research

This is a small scale intervention in the functioning of the real world and a close examination of the effects of such interventions.
Normally situational and it is concerned with diagnosing a problem in a specific context and attempting to solve it in that context.
Conducted with the primary intention of solving a specific, immediate and concrete problem in a local setting.
Not concerned with whether the results of the study are generalized to other settings, since its major goal is to seek a solution to a given problem.
Limited in its contribution to theory, but it is useful because it provides answers to problems that cannot wait for theoretical solutions.
Studies done on new teaching programmes in mathematics for secondary schools
Effective ways of dealing with absenteeism in work place
Effective ways of dealing with absenteeism in schools
Descriptive Research
Undertaken in order to ascertain and be able to describe the characteristics of variables in a situation.
Descriptive studies are undertaken in organizations in order to learn about and describe characteristics of employees. g. Education level, job status, length of service etc
The most prevalent method of gathering information in a descriptive study is the questionnaire. Others include: interviews, job analysis, documentary analysis etc.
Descriptive statistics such as the mean, standard, deviation, frequencies, percentages are used in the analysis of descriptive research.
Correlational Research
Usually descriptive in that it cannot presume (not certain) a cause-and-effect relationship.
It can only establish that there is an association between two or more traits or performance.
Involves collecting data to determine whether a relationship exists between two or more quantifiable variables.
Main purpose of correlation research is to describe the nature of the relationship between the two variables.
Helps in identifying the magnitude of the relationship.
6 . Casual Research
Refers to one which is done to establish a definitive ‘cause’ ‘effect’ relationship among variables.
The researcher is keen to delineating one or more factors that are certainly causing the problem.
The intention of the researcher conducting a casual study is to be able to state that variable X cause’s variable Y to change.
A casual study is more effective in a situation where the researcher has already identified the cause of the problem.
Relationship of young and old employees in an organization.
Remuneration package
end month and mid moth performance
Facilitation e.g. transport.
Historical Research (USE OF DOCUMENTS)
This is the systematic and objective location and synthesis of evidence in order to establish facts and draw conclusions about past events.
The act of historical research involves the identification and limitation of a problem of an area of study which is based on past events.
The researcher aims to:
Locate as many pertinent sources of information as possible concerning the specific problem.
Then analyze the information to ascertain its authenticity and accuracy, and then be able to use it to generalize on future occurrences.
Historical research is important because:
It enables solutions to contemporary problems to be solved in the past.
Throws light on present and future trends.
Allows for the revelation of data in relation to select hypothesis, theories and generalizations that are presently held about the past.
Ability of history to employ the past, to predict the future and to use the present to explain the past gives historical research a dual and unique quality which makes is exceptionally useful for all types of scholarly study and research.
Experimental Research
The investigator deliberately controls and manipulates the conditions which determine the events to which he is interested in.
Qualitative Research.(Human behaviors and aspects).
Includes designs, techniques and measures that do not produce numerical data.
Data is usually in form of words rather than numbers and this words are grouped into categories

THREE METHODS OF COLLECTING QUALITATIVE DATA .

Direct observation

Participant observation

Interview method.
Human behaviors are explained best using qualitative research.

10 QUANTITATIVE RESEARCH.

Includes designs, techniques and measures that produce discrete numerical or quantifiable data.
Radom sampling is usually done to ensure a representative of a sample is given.

ADVANTAGES OF USING BOTH QUALITATIVE AND QUANTITATIVE DATA .

A researcher has several objectives of study, hence they can be assessed using both.
Both supplement each other.

DISADVANTAGES.

Combining both methods can be expensive (time, energy and money)
Researcher may not have been sufficiently trained in this method to be able to use them effectively.

HOW DOES RESEARCH BEGIN?/RESEARCH PROBLEM.

Research usually begins with clarification of a topic in which one has some interest or about which increased knowledge is clearly needed.

The term topic refers to subject issue or area under discussion. The topic is essential in success of research project. One’s interest in topic is mandatory in order to sustain the research.

Research problem refers to an area in any field where researcher would like to find an answer/solution.

CONDITIONS TO BE MET AFTER IDENTIFICATION OF THE PROBLEM.

There must be an individual,group or organization to which the problem can be attributed(sample)eg teachers,farmers,doctors,engineer,workers,students etc
There must be some environment which the problem pertains(place/location ie Nairobi county).
There must be some objectives to be attained.

FACTORS AFFECTING THE TOPIC SELECTION/POINTS TO OBSERVE WHEN SELECTING A RESEARCH PROBLEM

Personal interest-Interest produces enthusiasm on what one is doing. It is the interest that makes the experience adequately rewarding.
Topic one selects should be important-The topic selected should not be brought forward just because of personal interest but also because it will add to knowledge.
Time-Due to time limitations, writers of academic research need to avoid complex topics as they may require large population samples. It is important to compare the time that topic will take against time available.
Newness-It is always good to look at a new area so that, what one is doing is a little different from what others have done in past.
Accessibility to material and respondents-A suitable topic is one which allows researcher to access the material. It is important to note that getting materials and respondents in some areas might not be an easy task.

Examples include

Senior government officials.

Vice chancellor of a university private or public.

Health officials.

National intelligence service.

Ethical consideration-It is both unethical and illegal to conduct research that may slander or do physical or psychological damage to subjects involved hence a researcher needs to take care of a subject in a very humane manner.

Subject /topic selected for research should be familiar.ie known to unknown or general to specific.

Costs involved

Selection of a problem must be pre-decided by a preliminary study.

Avoid the following;

A subject that have been overdone

Too narrow/fake problem

Controversial subjects.

STEPS IN TOPIC SELECTION.

Identify areas that puzzles an interest to you-Many issues may interest or puzzle a researcher and this may be social, economic, political,hr related issues, culture and religion.

2.Identify/select key words for the topic-Researcher should narrow down to the real aspects that are puzzling or interesting him/her and express the in specific key words. Example in human resource management, researcher may be interested on how mergers and acquisitions affect company loyalty.

Define the topic-Researcher analyses selected key words and tries to put them together meaningfully.

4.Formulate the topic-After problem identification and definition it is important that reseacher comes up with a complete topic e.g. impact of mergers and acquisitions on company loyalty in a private sector.

QUALITIES OF AN EFFECTIVE RESEARCH TOPIC.

Clear and an un ambiguous.
Supported by credible evidence.
Should captivate or interest researcher.
Should be researchable.

WHERE TO GET RESEARCH TOPIC FROM/SOURCES OF RESEARCH PROBLEMS.

Current issues(Newspaper)
Observation of environment behavior.
Personal Experience
Course;lecturers,discussion groups and literature.
Previous research work i.e. impact of microfinances on SME s
Natural calamities
Review of related literature-Review of published literature eg textbooks,journals,magazines etc.Other sources in this categories include. Research bulletin,research projects,research thesis,journals of management research,dissertations and internet.
Consultation with experts and research institutions.
Participation in professional discussions-forums,seminars,workshops and conferences.
Social development –social changes and technological changes.
Media-news like alcoholism,drug abuse,addiction and immorality.

STEPS OF RESEARCH PROCESS.

1 . Problem identification.

Research problems can emanate from different sources i.e. area of interest, results from observation of phenomenon, issues being shared in media, practical problems shared in newspapers that require attention and area of specialization.

Formulating research objectives and questions/hypothesis.

To address research problem.

Literature review.

After identifying research problem, research of related literature on research problem are conducted. This is the process of finding out what is already and not known about study.

Research design.

Researcher should come up with a design that will help him or her arrive at answers to research questions. The research design is basically mechanism employed for sampling population, data collection and analysis.

Hypothesis formulation-Optional.
Objectives and research questions(RQ)
Objectives and hypothesis(HO s )

Its possible to carry out a research study without hypothesis in which case,RQ will be necessary.

Data collection.

Researcher selects instruments/tools for data collection.Data collection tools include:

Questionnaires
Interview schedules
Interview guides
Focused groups
Experiments
7 . Sampling.

Select people who will be in your study as participants.

Researcher goes to field to gather data required for answering research questions. Data collection can be undertaken by administering questionnaires to students, focused group discussions and carrying out experiments.

Data processing .

Data is usually collected in raw form and should be processed so that meaning can be made out of it.

10 Report/project writing.

This is the last stage in research process where the researcher documents important details of research. The report should explain in detail the various stages of study and present results as well as the recommendations.

STATEMENT OF PROBLEM

Refers to an

RESEARCH ETHICS

Ethics are guidelines that deal with the conduct on an individual. Ethical considerations must be kept in mind when dealing with respondents. Ethical research requires personal integrity from the researcher.

Respondent’s anonymity request must be adhered to when promised.
Confidentiality must be kept where promised.
Asking embarrassing questions, expressing disgust when collecting data, using threatening statements, etc.
Respondents must willingly participate in research. Researcher must disclose the real purpose of the research. Informed consent includes the following information.
Purpose of the study
Any unforeseen risks
A guarantee of anonymity and confidentiality
Identification of the researcher
An indication of the number of subjects involved
Benefits and compensation or the lack of them
Use of vulnerable and/or special populations such as children, mentally disabled people, and sick people etc. permission must be obtained from those who care for these special populations.
Financial Issues and Sponsored Research

Sponsor of a research demands compromise on quality of research to save time and/or money. Sponsors may demand that research findings be distorted. An ethical research should never accept such compromise in order to protect their integrity. Unethical conduct also occurs when researchers divert research funds for other purposes. This affects the quality of research and may yield misleading data.

Dissemination of Findings

A research must not conceal research findings after conclusion of research. Where findings are sensitive, modalities of releasing results should be agreed on. It is a waste of resources to undertake research only to hide the findings.

Research Plagiarism and Fraud

Plagiarism is a situation where a researcher refers to another person’s work as theirs without acknowledging the author. Stealing ideas from another scholar is also considered plagiarism. This is a crime punishable by law. It erodes the integrity of the victim and has serious professional consequences.

Fraud occurs when a researcher fakes data that has actually not been collected. Fraud also includes false presentation of research methodology and results. It is a punishable crime.

RESEARCH METHODS TERMS

Concepts: a concept is a bundle of meaning or characteristics associated with certain events, objects, conditions or certain situations. Classifying and categorizing objects or events that have common characteristics beyond the single observation creates concepts. Concepts are acquired through personal experience. Some concepts are unique to a particular culture and not readily translated into another. For instance, we might ask respondents for an estimate of their monthly total income. We might receive confusing answers unless we restrict the concept by specifying the following:
Time period. I.e. weekly, monthly or annually.
Before or after income taxes.
For the head of the family or all family members.
For salary and wages only or also for dividends, interest and capital gains.
Constructs: this is an image or idea specifically invented for a given research or for theory building purposes. Constructs are built by combining simpler concepts especially when the idea or image we intend to convey is not directly subject to observation.
Definitions: words may have different meanings to parties involved. An operational definition is a definition stated in terms of specific testing criteria or operations. These terms must have empirical references. We must be able to count, measure or gather information through our senses. Whether the object being defined is physical, e.g. a machine or abstract, e.g. motivation, achievement, the definition must specify the characteristics to be studies and how they are to be observed. The specifications and procedures must be clear so that any competent person using them would classify the objects in the same way.
Variables: a variable is a measurable characteristic that assumes different values among the subjects. There are 5 types of variables that one is likely to find in a study.
Independent variable (IV): this is the variable the researcher manipulates in order to determine its influence on another variable. It influences the dependent variable either positively or negatively.
Dependent variable (DV): this variable attempt to indicate the total influence arising from the total effect of the independent variable.
Moderating Variable (MV): in typical situations and relationships, there is at least one IV and one DV. For simple relationships, all other variables are considered extraneous and ignored. E.g. in a typical office, we might be interested in studying the effect of the 4 day work week on the productivity. Our hypothesis will be: the introduction of the 4 day work week (IV) will lead to higher office productivity (DV). However, a simple one on one relationship needs revision to take other variables into account. The MV is the second IV that is included because it is believed to have a significant contributory effect on the original IV, DV relationship. Our hypothesis is; The introduction of the 4 day work week (IV) will lead to higher productivity (DV) especially among older workers (MV).
Extraneous Variable (EV): these are at times referred to as confounding variables because they confound the effect of the IV on the DV. They affect the outcome of a research study, either because the researcher is not aware of their existence or if the researcher is aware, there is no control for them. In routine office work (EV control), the introduction of a 4 day work week (IV) will lead to higher productivity (DV) especially among older workers (MV). For example Teaching methods are the IV, genes of students (EV) and performance (DV).
Intervening Variables (IVV): this is a conceptual mechanism through which the IV and MV might affect the DV. It is defined as that factor that theoretically affects the observed phenomenon but cannot be seen, measured or manipulated. It must be inferred from the effect of the independent MV on the observed phenomenon. E.g. Introduction of a 4 day work week (IV) will lead to higher productivity (DV) especially among older workers (MV) by increasing job satisfaction (IVV).
Research Theory: a theory is a systematic explanation of facts. A good theory is simple and free of jargon and has predictive accuracy. It should also be of importance to the society and discuss current issues. These are characteristics of an “elegant theory”.

RESEARCH PROPOSAL

A research proposal is a document written by a researcher that provides a detailed description of the proposed study. It is an outline of the research process that gives a reader a summary of the researchers’ intention to carry out a study.

It is therefore a detailed work plan on how a research activity will be conducted. The research proposal is ones way of showing that one has an idea that is of value and can contribute important knowledge to the specific field. A research proposal is intended to convince the readers that one has a worthwhile research study and that one has the competence and the work-plan to complete it.

The proposal should have sufficient information to convince readers that one has an important research idea, that one has a good grasp of the relevant literature and the major issues, and that methodology is sound. A research proposal should address the following questions:

What one plan to accomplish,
why one want to do it and
How you are going to do

To propose means to state an intention, suggestion. It indicates a researcher’s intention to carry out a study. A Research proposal is written in future tense since the study has not yet been carried out. A research study starts with a brief introductory section that narrows down to the specific problem to be studies.

FORMAT OF A RESEARCH PROPOSAL

A proposal is divided into the following sections

PRELIMINARIES

This is the first section of the proposal. However it is the last to be written. It includes the following:

It is often times referred to as the cover page, this section is where one indicates the title of the research, name, institutional information . This section includes

The research title
Name and student number
Statement- A research proposal submitted in partial fulfillment for the degree of (insert the name of the Degree) of kenya Methodist University
Month and year of submission
Declaration Page

This includes the declaration by the student and supervisor:

Declaration by student

I declare that this research s proposal is my original work and has not been presented for a degree or any other award in any other university

Name…………………Signed………………………….Date……………………….

Declaration by university supervisor (s)

This research proposal has been submitted for examination with our approval as university supervisors

Signed …………………….. Date……………………………

Name…………………………………………………………………………………..

Dedication

This is not compulsory and may apply to an individual who has had a major impact on the researcher. It should not exceed 25 words

Acknowledgement

This refers to individuals who in one way or the other have contributed to the success of the study. It should not exceed 150 words

This summarizes the major areas in the proposal. It should not exceed 500 words. It should be comprehensive with no paragraph.

Table of contents

This indicates all the section in the proposal. The page numbers should be included

g) List of tables

h) List of figures

g) Abbreviations and acronyms

The Research Process

The research begins with a selection of identification of a subject/problem to be studied. Once the subject has been identified, the researcher takes the following steps:

Formulating the research problem.
Defining the hypothesis.
Determination of the type of data to be collected.
Data collection procedures and data analysis and generalizations

CHAPTER ONE: INTRODUCTION TO THE STUDY

The first section of a research proposal is referred to as the introduction. This is because it indicates how the study will flow. It is the opening /beginning of the study. The main purpose of the introduction is to provide the necessary background or context for the research problem. Its purpose is to establish a framework for the research. This section is divided into the following:

1.0 Introduction

Background of the study
Statement of the problem
Objectives of the study
Research questions
Significance of the study
Scope of the study
Operational definition of terms

1.1 Background of the study

This sections aims to create reader interest in the topic. It lays the broad foundation for the problem that leads to the study. It places the study within the larger context of the scholarly literature. (Creswell, 1994, p. 42). The background should be concise, interesting and written in language which is understandable to a well-informed but non-specialist audience. It should assist the readers to understand the dependent and independent variables. It puts the topic into perspective. In writing this section one should

Provide the contemporary context in which the proposed research lies
Identify the key independent and dependent variables
specify the phenomenon one wants to study
Briefly describe the major issues and sub-problems to be addressed by the research.

This section gives a background of what should be studied. It puts the topic into perspective. It should be about three pages.

1.2 Statement of the problem

This is the issue of concern. it is the “why” of the study. it refers to what has propelled the need for the study. “a problem might be defined as the issue that exists in the literature, theory, or practice that leads to a need for the study”. it is important in a proposal that the problem stand out—that the reader can easily recognize it. one should state the problem in terms intelligible to someone who is relatively uninformed in the area of investigation. effective problem statements answer the question “why does this research need to be conducted.”.

The following should be considered while formulating a problem

What is the problem one aims to solve and
Why is it important to be investigated?

The problem statement should be given in the clear and understandable form.

It indicates a gap between the actual and desired state. This is an essential and focal point of the proposal. Without a problem there is no study. Citations to justify the issue of concern should be included.

It should be precise, specific

Characteristics of a good research problem are as follows:

It should be written clearly in a way that captures the reader’s interest.
The specific problem identified is objectively researchable.
The scope of the specific research problem is indicated.
Importance of the study in adding new knowledge is clearly stated.
The problem statement must give the purpose of the research

Research Problem

The research problem is formulated using the following criteria.

Is the research within the range of resources and time constraints?
Is the necessary data accessible?
Can you come up with an answer to the problem?
Is the required methodology manageable and understandable?
Is the problem of sufficient magnitude to fulfill the motivation of the study?
Are there enough variables?
Are you interested in the problem area?
Does it relate to your background and career?
Will you learn useful skills from pursuing it?
Does the problem fill a gap in literature?
Does it challenge previously held opinions?
Will others recognize its importance?
Will it contribute to the advancement of knowledge?
Is it publishable?
Will the solutions to the problem improve available knowledge?
Are other researchers likely to be interested in the results?
Will your own research skills be improved as a result of the study?

Sources of Research Problems

Existing theories that contain generalizations of hypothesized principles. This is suitable for theory based studies.
Existing literature as a source requires systematic and extensive reading in the general area of interest. E.g. books, articles etc.
Discussions with experts on general topics, in class or seminars.
Previous research studies that indicate areas of further research.
Replication, which involves carrying out a research project that has been done previously to establish if the findings hold over time and across regions.
Media frequently reports issues that can form the basis of a research problem since the issues are discussed by the public and are important to the majority of people.
Personal experiences, which include first hand observations and reflection on experiences lead to vivid images and intuition on the part of the researcher.

1.3 Objectives

This is the ultimate goal or aim of the study. It is what the researcher hopes to achieve by the end of the study. It should provide a specific and accurate synopsis of the overall purpose of the study” What will be achieved at the end of the study. It is linked to the research Title.

Purpose of the Study

The researcher conveys the focus of the research study in one or two sentences. this is the purpose of the study. the purpose should be accurately expressed for the research process to be carried out with ease. the criteria used in formulating a purpose of the study are as follows:.

It must be clearly indicated, unambiguous and in a declarative manner.
It should indicate the concepts or variables in the study.
Where possible, the relationships among the variables should be stated.
The purpose should state the target population.
Variables and target population given in the purpose should be consistent with the variables and target population operationalized in the methods section of the study.

Specific Objectives

These are the measurable tasks that will assist meet the purpose of the study.

These are what the researchers hope to achieve by the end of the study.

Active verbs are used

To analyze……………..
To assess…………………
To find out………………
To examine……………
To evaluate……………..
To determine

At least 3 to 5 objectives

1.4 Research Questions

A research question poses a relationship between two or more variables but phrases the relationship as a question. These are specific objectives in question form. When answered the research objectives will be addressed.

(Research Hypothesis)

Formulating a Hypothesis

A hypothesis is a researcher’s prediction regarding the outcome of the study. They are derived from existing theories, previous research or personal experiences and observations. A study can have one hypothesis or where there are several variables, multiple hypotheses can be formulated. When a researcher analyzes collected data to determine whether the hypothesized relationship exists and the results fail to support a stated hypothesis, it does not mean that the study has failed. Such a situation implies that existing theories need revision or testing under different environments. Purposes of hypotheses are as follows:

They provide direction by bridging the gap between the problem and the evidence needed for its solution.
They ensure collection of the evidence necessary to answer questions in the statement of the problem.
They enable the investigator to assess information collected by examining the relevance and organization.
They sensitize the investigator in ascertaining aspects of the study that are relevant regarding the problem at hand.
They permit the researcher understand the problem with greater clarity.
They guide the collection of data and provide the structure for their meaningful interpretation.
They form the framework for the ultimate conclusions as solutions.

Formulating a sound hypothesis requires reviewing of literature or existing theories. This is carried out after the literature review but prior to data collection.

Characteristics of a Good Hypothesis

Must be clearly and briefly define the expected relationship between the variables.
Must be based on sound theory, previous research or personal experience.
Must be consistent with common sense or generally accepted truths.
Must be testable and within a reasonable time.
Must be related to empirical phenomenon.
Variables stated must be consistent with purpose statement, objectives and the operationalized variables in the method’s section.
Must be simple and concise as much as the complexity of the concepts involved allow.
Must be stated in a way that implications can be deduced in the form of empirical operations.

Types of Hypothesis

Null hypothesis: also called a statistical hypothesis. It always states that no real relationship or difference exists between variables. E.g. there is no difference in maize productivity during rainy seasons or dry seasons. Mathematically expressed as:

H 0 : µ 1 = µ 2

Alternative hypothesis: also known as a research hypothesis. It usually states that there is a relationship or difference but the researcher does not know the nature of such difference or relationship. E.g. high rainfall increases maize productivity. Mathematically expressed as:

H 1 : µ 1 ≠ µ 2 Or H 1 : µ 1 > µ 2 Or H 1 : µ 1 < µ 2

There are statistical tests applied to hypothesis i.e. one tailed or two tailed tests. It is easier to obtain statistical significance with one tailed tests.

The alternative hypothesis takes several forms. In statistical form, directional hypothesis make use of the signs > or < in which case a one tailed test is applicable. In a non-directional hypothesis, a two tailed test is used.

1.5 Significance of the study

This section indicates who will gain from the study and how. one should think about implications— how results of the study may affect scholarly research, theory, practice, educational interventions, curricula, counseling, and policy. the major issues to be addressed are:.

What will be improved or changed as a result of the proposed research?
Why is the study important
How will results of the study be implemented, and what innovations will come about

1.6. Scope of the study

This refers to the parameters of the study, the area that the study will focus on. it is a general outline of what the study will cover. “this helps one to remember and keep within the accepted range of one’s study. this also reminds a researcher that this method of investigation should be centered around trying to solve the problem within the provide scope..

1.7 Operational Definition of Terms

In this section all terms assumed to have a unique meaning should be defined

CHAPTER TWO

LITERATURE REVIEW

Literature refers to the analysis of textbooks or manuscripts. “the term literature” means the works the researcher consulted in order to understand and investigate the research problem.
A literature review therefore is an account of what has been published on a topic by accredited scholars and researchers .
It is a critical look at the existing research that is significant to the work that the researcher will be carrying out. It involves examining documents such as books, magazines, journals and dissertations that have a bearing on the study being conducted.
Literature is the process of reading, analyzing, evaluating, and summarizing scholarly materials bout a specific topic. It is an analysis of textbooks and manuscripts related to ones area of study. Its purpose is to summarize, synthesize and analyze the arguments of others. In effect, a literature review compiles, outlines and evaluates previously established research and relates it to ones own study. A literature review is a body of text that aims to review the critical points of current knowledge. Most are aware that it is a process of gathering information from other sources and documenting it

The section reviews the relevant studies upon which the research is based and introduces the conceptual framework. This section indicates the theoretical concepts used. This section provides relevant readings from previous works. The materials should be relevant to the topic of the research. The literature review accomplishes the following

It shares with the reader the results of other studies that are closely related to the study being reported (Fraenkel & Wallen, 1990).
It relates a study to the larger, ongoing dialogue in the literature about a topic, filling in gaps and extending prior studies (Marshall & Rossman, 1989).
It provides a framework for establishing the importance of the study, as well as a benchmark for comparing the results of a study with other findings.
It “frames” the problem earlier identified.

Importance of Literature Review in Research

Literature review is essential in research. This is due to the following:

It sharpens and deepens the theoretical foundation of the research. Literature review enables the researcher to study different theories related to the identified topic. By studying these theories, a researcher gains clarity and better understanding of the specific objectives
It gives the researcher insight into what has already been done in the selected field, pinpointing its strengths and weaknesses. This information guides the researcher in the formulation of a theory that aims at addressing the identified gaps.
It enables the researcher to know the kind of additional data needed in the study. This helps avoid duplication of work.
An understanding of previous works helps the researcher to develop a significant problem which will provide further knowledge in the field of study. It also helps in delimiting the research problem. This is through portraying what has already been done and what would be useful to focus on in the current study.
Wide reading exposes the researcher to a variety of approaches of dealing with the research issue. This contributes to a well designed methodology. The researcher can avoid methods indicated in the literature to have failed and adopt new approaches. This will result in a significant study.

In general therefore the literature review serves the following:

Ensures that you are not “reinventing the wheel”.
Gives credits to those who have laid the groundwork for ones research.
Demonstrates ones knowledge of the research problem.
Demonstrates ones understanding of the theoretical and research issues related to your research question.
Shows the ability to critically evaluate relevant literature information.
Indicates your ability to integrate and synthesize the existing literature.
Provides new theoretical insights or develops a new model as the conceptual framework for your research.
Convinces reader that the proposed research will make a significant and substantial contribution to the literature (i.e., resolving an important theoretical issue or filling a major gap in the literature).

Qualities of an effective Literature Review

The following are qualities expected from an effective literature review.

It is critical, organized and analytical in orientation:.
It Justifies the need for the study :
It highlights the relationship between the past and the current study: An effective literature review links the current study with past studies. It evaluates and shows the relationships between the work already done by other scholars and the researchers work. This link brings consistency and continuity in relation to the identified topic.
It puts the research problem into perspective: By quoting and analyzing various studies related to the selected topic, the literature review helps define the research problem. It also acts as a guideline in assessment of the research questions.

Guidelines in formulating effective Literature Reviews

a) Identify key issues to be addressed by the literature review:

b) Identify sources of information: The researcher needs to identify books, articles, professional papers and other relevant publications that relate to the research title and the research problem. .

c) Analyze critically the articles identified:

d) Synthesize the information gathered: Select studies that relate most directly to the problem at hand

e) Evaluation

After carrying out the review and writing, the researcher should reflect on the following:

Challenges faced in the formulation of a Literature Review

Failure to connect the reviewed studies with the current study :
Poor presentation:
Lack of/poor referencing:
Lack of critique :
Failure to review current studies :

Reviewed literature may also be rejected due to the following:

Lacking organization and structure
Lacking focus, unity and coherence
Being repetitive
Failing to cite relevant studies
Failing to keep up with recent developments
Failing to critically evaluate cited papers
The reviewed literature should be stimulating.

The References in the Body of the Text

The appropriate point at which to indicate the source of an idea is as soon as is convenient. When it is at the beginning or middle of a sentence, the researcher should indicate the surname of the author and year of publication. The year of publication should be enclosed inside brackets e.g. Orodho(2003)pointed out that…., Kombo (2004) indicated that …..At the end of a sentence or paragraph, one needs to enclose the surname of the author and year of publication in brackets. The name and year should be separated by a comma. For example (Orodho, 2003); (Kombo, 2004).

Referencing Within the Text

There are 2 methods of accrediting a statement:

The authors last name and year of document publication are put after a paraphrased statement in a text. E.g.

Income has been found to be positively related with quality of life (Williams, 2011).

The authors name comes at the beginning of a sentence with the year following in brackets. E.g.

Williams (2011) found a positive relationship between income and quality of life.

According to William (2011), there is a positive relationship between income and quality of life.

References & Bibliography

References refer to a list of works the researcher read and cited in the text. A bibliography refers to a list of material read whether they are cited or not. There are various ways of writing references. The most commonly used in Kenyan universities is the American Psychology Association (APA) style.

2.1 Introduction

This section briefly indicates the content to be covered in the study

2.2 Theoretical orientation

In this section the researcher identifies 2/3 theories related to the variables of the study and highlights the contribution of the theories to the study

2.3 Empirical Review

This is the longest section in the chapter. The researcher indicates what various authors have stated in relation to each specific objective. The authors should be cited.

2.4 Conceptual Framework

A concept is an idea. A conceptual framework is the researcher’s idea of the effect of the independent variable on the dependent variable. It should be in diagram form and explained

2.5 Operationalization

In this section the researcher identifies the specific areas that will be focused on in the independent variable. It should be in diagram form.

This section should be 10-20 pages

CHAPTER THREE: RESEARCH METHODOLOGY

This chapter contains definitions, procedures, and explanations of techniques used to collect, analyze and present information. The section deals with the description of the methods applied in carrying out the study.

RESEARCH DESIGN

Research design is the conceptual structure within which research should be conducted. Research design provides the glue that holds all the elements of a research study together. It indicates how all of the major parts of the research project work together to try to address the central research questions. It is the scheme, outline or plan that is used to generate answers to research questions. It is an arrangement of conditions for collection and analysis of data. The function of research design is to provide for the collection of relevant information. It constitutes the blue print for the collection, measurement and analysis of data.

Selection of Research Design:

In selecting a research design one should consider the following:

What the study is about- Objectives of the research study.
Why the study is being carried out
Where the study will be carried out
Method/techniques of Data Collection to be adopted
Time required
Data Analysis– qualitative and quantitative

In selecting a research design a researcher should :

Name and describe the research design
Justify why it was selected
Explain how it will be used.
One must state the rational for selecting the research design

TYPES OF RESEARCH DESIGNS

Descriptive Research Designs

This design describes phenomena as they exist. Descriptive studies generally take raw data and summarize it in a useable form. Descriptive Research addresses issues of who, what, where, how related to research. It provides further insight into the research problem by describing the variables of interest. This method can be used for profiling, defining, segmentation, estimating, predicting, and examining associative relationships. In a descriptive study, no attempt is made to change behavior or conditions. One measures things as they are. This method describes the state of affairs as they are. It results in the formulation of knowledge and solutions to problems. The focus of interest is the respondent’s opinion and views. Questionnaires/interviews are mainly used to gather information.

Experimental Research Design

This design is used to test the cause-effect relationship through the manipulation of variables. The experimental group is manipulated while the control group is not. Environmental factors are also controlled. It involves the systematic manipulation of some characteristics and examination of the outcome. In an experimental study one take measurements, try some sort of intervention, then take measurements again to see what happened.

Correlational Research Design

This method determines whether or not and to what extent an association exists between two or more variables. Data is collected from varied groups of subjects and then compared for their similarities and differences. It provides procedures for understanding relationships. It enables the researcher to assess the degree of relations that exist between two or more variables.

This is an intensive, in-depth analysis of a single entity. It aims at gaining in-depth insight of an issue using smaller samples. The findings can be generalized to a wider population. It seeks to describe a unit in details. In a case study a great deal can be learned from a few examples of a phenomenon under study. It is an in-depth study of an individual group, institution, organization or program. Data gathering include interviews, field notes of observations, archival data and biographical data.

Survey Design

This is an investigation of views from a wider population such as the opinion polls. These are general views affecting a wider group in general. The method is used to analyze and discover occurrences. It explains events as they are, were or will be.

Exploratory Research

Designed to generate basic knowledge, clarify relevant issues uncover variables associated with a problem, uncover information needs, and/or define alternatives for addressing research objectives. This is a very flexible, open-ended process.

Historical research

This refers to exploration, explanation and understanding of past phenomenon from data already available. It is the Collection and evaluation of data related to past events that are used to describe causes, effects and trends that may explain present or future events. Data are often archival. It aims at arriving at conclusions about causes, trends, and effects of past phenomenon in order to explain the present and predict and control the future. This method is useful where primary data cannot be collected.

Cross cultural research design

This method is mainly used to analyze to what extent cultural beliefs and practices in ones immediate environment influences ones attitude hence development.

B) SAMPLING PROCEDURE

RESEARCH INSTRUMENTS

Types of research instruments.

There are three major types of research instruments. These are

Observation

QUESTIONNAIRES

These are research instruments that gather data over a large sample. Respondents note down their views. Each person is asked to respond to similar questions.

Types of questionnaires

Self administered questionnaires

Postal\mail questionnaires.

This refers to cases where the researcher hand delivers the research instrument

Post/mailed questionnaires

This refers to questionnaires that are sent to respondents through the mail.

B) INTERVIEWS

Refers to oral administration of questions

Types of interviews

Structured/ unstructured interviews

Structured interviews

Unstructured interviews

FACE TO FACE INTERVIEWS

This refers to cases where the researcher has direct communication (face to face) with respondents.

TELEPHONE INTERVIEWS

This refers to oral questions asked through the telephone.

FOCUS GROUP DISCUSIONS

There are group interviews where the researcher acts as a facilitator.

OBSERVATION

This is a research instrument that deals with analyzing what people do. It involves the systematic watching, recording, analyzing and interpreting of people’s behavior.

Types of observations:

participant observation
non-participant observation

This refers to a study I which the observer becomes a part of or an active participant in the study. The subjects may not be told about the participant observer. The researcher attempts to participate fully in the lives and activities of the subjects. E.g. Phenomenological studies. Respondent becomes comfortable with researcher.

Non-participant observation

In this study, the participant is not directly involved in the situation to be observed. The researcher may not intentionally interact with respondents.

CHAPTER THREE

RESEARCH METHODOLOGY

3 .1 Introduction

This section should be brief and indicate the content in the study.

3.2 Research design

This refers to the outline/scheme or plan that will be used to collect information. The researcher should identify the design and justify why it was selected. Justify with citation. (1/2 page)

3.3 Target Population

This section indicates the group the researcher would like to focus on in the study. The researcher should justify why the group has been selected and its contribution to the study. The researcher should indicate the group’s characteristics including size.

3.4 Sampling procedure

This section indicates how the sample will be selected and the sample size. One should justify the sample design

3.5 Data collection instruments and procedures

In this section the researcher identifies the research instrument (s) that will be used in data collection. The researcher justifies why the instrument(s) has/have been selected. The quality of the instrument (Validity/reliability) is addressed. One should indicate the procedures of administering the research instruments. It should indicate how the authority to collect data will be sought, methods of ensuring high response rates, ethical values to be considered.

. Mugenda and Mugenda (2003) asserted that the accuracy of data to be collected largely depended on the data collection instruments in terms of validity and reliability. Validity as noted by Robinson (2002) is the degree to which result obtained from the analysis of the data actually represents the phenomenon under study.

Validity will be achieved by pre-testing the instrument to be used to identify and change any ambiguous, awkward, or offensive questions and technique as emphasized by Cooper and Schindler (2003). Reliability on the other hand refers to a measure of the degree to which research instruments yield consistent results (Mugenda & Mugenda, 2003). In this study, reliability will be ensured by pre-testing the questionnaire with a selected sample. The pre-test exercise will take place at the convenience of both the researcher and the research assistant

3.6 Methods of data Analysis

This section should indicate how the variables will be measured and presented. The statistical methods used should be justified

3.7 Research Limitation

In this section the researcher highlights some of the challenges likely to be encountered during the study and how they will be addressed.

3.8 Research Ethics

This section indicates some of the values that the researcher will reinforce during the study

RESEARCH DESIGN AND METHODOLOGY

3.1 Introduction

This chapter presents the methodology that will be used to carry out this study. Research methodology is defined as an operational framework within which the facts are placed so that their meaning may be seen more clearly. The methodology includes the research design, population to be studied and sampling strategy, the data collection process, the instruments used for gathering data, and how data is analyzed and presented.

3.2 Research Design

In this study a descriptive survey design will be used. Descriptive research portrays an accurate profile of persons, events, or situations (Robinson, 2002). It allows the collection of large amount of data from a sizable population in a highly economical way. It allows one to collect quantitative data, which can be analyzed quantitatively using descriptive and inferential statistics. Therefore, the descriptive survey is deemed the best method to fulfill the objectives of this study. The design is preferred because it is concerned with answering questions such as who, how, what which, when and how much, (Cooper and Schindler 2001). A descriptive study will be carefully designed to ensure complete description of the scenario, making sure that there is minimum bias in the collection of data.

3.3. Target Population

Target population is the specific population about which information is desired. A population is a well defined or set of people, services, elements, events, group of things or households that are being investigated. The target population will consist of the following population: Top level management, middle level management and lower level management from Kenya Revenue Authority. The target is as follows:

Table 3.1 Target Size

Source: Author (2013)

3.4 Sample Design

The researcher will use stratified sampling procedure to select samples that are representative of the target population. This procedure is preferred since the entire target population has an equal chance of being selected. Mugenda and Mugenda (1999), point out that stratified sampling method ensures inclusion of small groups which otherwise could have been omitted entirely by other sampling methods. Thus the population will be divided into stratus. The sample is as follows:

Table 3.2 Sample Size

3.5 Data Collection Instrument and Procedures

Primary data will be used in this study. According to Ochola (2007), primary data refers to what is collected directly by the researcher for the purpose of the study. The data will be collected by the use of questionnaires and interviews. Research questionnaires having both structured and unstructured questions will be designed and administered. This enables the researcher to get vital data directly from the respondents. The researcher will interview the respondents in person and also through telephone using interview questions that will be both structured and unstructured; Interviews will ensure immediate feedback, accuracy, clarity and they will help reveal sensitive information. Interviews were used as a primary data collection technique.

This method is advantageous because of the direct feedback to the researcher. There is an opportunity to reassure respondent(s) should s/he be reluctant to participate, and the interviewer also clarifies certain instructions or questions. The interviewer also has the opportunity to probe answers by asking the respondent to clarify or expand on specific response(s). Finally, the interviewer can supplement the answers by recording his/her own observations, for instance; gender, time of day/place where the interview will take place.

3.5.1 Validity and reliability

Mugenda and Mugenda (2003) asserted that the accuracy of data to be collected largely depended on the data collection instruments in terms of validity and reliability. Validity as noted by Robinson (2002) is the degree to which result obtained from the analysis of the data actually represents the phenomenon under study.

3.6. Data Analysis

The data will be collected by use of questionnaires. Questions will be analyzed both qualitatively and quantitatively by first editing to get the relevant data for the study. The edited data will then be coded for easy classification and to facilitate tabulation. The tabulated data will then be analyzed by calculating various frequencies and percentages where possible. The collected Data will then be calculated by use of statistical inferences such as mean and mode where applicable. Presentation of data will be in the form of tables and figures.

3.7 Research Limitations

CHAPTER FOUR

DATA ANALYSIS PRESENTATION AND INTERPRETATION

This chapter is written after the completion of data collection. It is written in past tense. It contains the results of the data analyzed.

4.1 Introduction

This section discusses the content in the chapter

4.2 Research findings

This section analyzes the research findings with particular emphasis on the objectives of the study. The results are presented in quantitative and qualitative analysis where applicable. Tables and graphs are used where applicable to facilitate clarity of the results.

The results presented should be discussed and should be linked to the literature reviewed.

Subheadings should be used.

4.3 Summary

This section should briefly summarize the major highlights of the study with emphasis on the objectives. The researcher should ensure that all items in the data collection instruments are addressed.

Types of Scales

A scale measures the magnitude or quantity of a variable. A variable is a symbol e.g. X or Y that represents any of a specified set of values. There are four types of scales commonly used as levels of measurement.

Nominal scales allow for qualitative classification. They deal with categorical responses that take on values that are names or labels. E.g. gender is categorized as male or female, ethnicity, marital status, religion etc. The appropriate statistics for nominal data include mode, frequency and chisquare.
Ordinal scales are similar to nominal variable but it can be ordered in a meaningful sequence. Ordinal data has order but the intervals between the scale points are uneven because of lack of equal distances, arithmetic operations are impossible. However logical explanations can be performed.
Interval scales deal with interval variables which give better information than ordinal scales. They have an equal distance between each value. E.g. the distance between 1 and 2 is equal to the distance between 99 and 100. Appropriate statistics are the same as the nominal and ordinal scale including mean, standard deviation, correlation, regression, ANOVA.
Ratio scales measure variables that have the same properties as the interval variables except that with ratio scaling, there is an absolute zero point. E.g. height, weight, length, unsold units etc. All statistics permitted for the interval scale including geometric mean, harmonic mean and logarithms.

Characteristics of Sound Measurement

There are 3 major criteria for evaluating a measurement tool.

Validity: includes internal validity which refers to the outcome of the study based on the function of the program. A study has internal validity if the outcome of the study is a function of the approach being tested. It is justified by the conclusion that the researcher has been able to control the threats of other variables i.e. IV, MV or EV.

Internal validity is further classified as:

Content validity: if the measuring instrument is adequate to cover the topic under study.
Criterion related validity: reflects the success of measures used for prediction or estimation. A researcher may want to predict an outcome or estimate the existence of a current behavior or condition; these are the predictive and concurrent validity.
Construct validity: where we consider measurement of abstract characteristics for which no empirical validation seems possible.
Reliability: is measure is reliable to the degree that it supplies consistent results. Reliability is concerned with accuracy and precision of a measurement procedure.
Practicability: is concerned with a wide range of factors of economy, convenience and interpretability.

DATA PREPARATION METHODS

According to Cooper & Schindler (2011), data preparation is conducted using the following methods: editing, coding and data entry. These activities ensure the accuracy of their data and their conversion from raw form to reduced and classified forms that are more appropriate for analysis. The methods are discussed as under:

Data Editing

It is the first step of data analysis which involves detection of errors and omissions correcting them when possible to certify that maximum data quality standards are achieved. The purpose of editing data is to guarantee accuracy, consistency, uniformity, completeness and proper arrangements to simplify coding and tabulation of data. Editing: involves checking raw data to eliminate errors or points of confusion in data. The main purpose of editing is to set quality standards on the raw data. The analysis will then take place with minimum confusion. Editing detects errors and omissions, corrects them when possible. This is to guarantee that the data is accurate, consistent with other information, uniformly entered, complete and arranged to simplify coding and tabulation. There are 2 stages in editing:

To establish whether actual data collection was conducted in the field. For example for interview the approach to check responses to open ended questions might be used to unearth falsification of responses.

To correct data inconsistencies for instance if instead some data captured in form days instead of number of weeks.

Data Coding

According to Mugenda and Mugenda (2012), coding is a system of classifying a variable into categories and assigning different numbers to various classifications to enable quantitative analysis to be conducted for example a variable like occupation would have different classifications for example teacher, nurse, driver etc which would each have a numerical cods like teacher-1,nurse-2,driver-3,clerk-4 etc. Coding: involves assigning numbers or other symbols to answers so that the responses can be grouped into a limited number of classes or categories. The classifying of data into limited categories sacrifices some data detail but is necessary for efficient analysis. For Male or Female, a researcher may use M or F and code 1 for male and 2 for female or use 0 and 1. Coding helps the researcher in reducing several thousand replies into a few categories containing the critical information for analysis. The researcher determines appropriate categories into which responses are placed. Different numerical codes are assigned to each response category. Researchers frequently use summary statistics for presenting findings. These include measures of central tendency (mean, median and mode), measures of dispersion (variance, standard deviation, range, interquartile range), measures of skewness and kurtosis and percentages. They enable generalization about the sample of study objects. Frequency tables, bar charts and pie charts are used in displaying data.

Tabulation:

This involves counting the number of responses that fit in each category. Tabulation may be in form of simple tabulation which addresses one variable (e.g. number of cigarettes smoked per day) or cross tabulation that combines variables (e.g. number of cigarettes smoked per day with the age of the respondent). These are used for simple studies. Studies involving large numbers of respondents with many items to be analyzed rely on computer tabulation and computer packages for analysis. Data entry involves converting information gathered by secondary and primary methods to a medium for further manipulation. There is wide variety of ways to enter the data into the computer for analysis. Probably the easiest is to just type the data in directly. In order to ensure a high level of data accuracy, the data analyst should use a procedure called double entry (entering data only once).

Dealing with “Don’t Know” Responses

First, there is the legitimate DK response from respondents who do not sincerely know the question being asked.

Second, DK responses from respondents who ignore to answer questions or refuse to give the questionnaire the seriousness it deserves may be encountered.

The best way to deal with undesired DK answers is to design better questions at the beginning. Researchers should identify the questions for which a DK response is unsatisfactory and design around it. During interview process, a good rapport should be established between the interviewer and interviewee so that more probing can be done easily so that respondents can provide definite answers. The interviewer may also record verbatim any elaboration by the respondent and pass the problem on to the editor.

CHAPTER F IVE

SUMMARY OF RESEARCH FINDINGS, CONCLUSION AND RECOMMENDATIONS

This chapter summarizes the major findings highlighted in chapter four

5.1 Introduction

The section addresses the content in the chapter

5.2 Summary of research findings

This section summarizes the views expressed by various respondents in relation to the objectives of the study

5.3 Conclusion

This is a summation of the researchers view in relation to the responses raised based on each of the objectives. The conclusion must be based on the results obtained.

5.4 Recommendations

This must be derived from the results. They should address each of the specific objectives.

5.5 Recommendations for Further Research

This should be based on issues that emerged in t he process of research but were not investigated.

Use the American Psychological Association (APA) format

RESEARCH REPORT

Presenting Results: Written Reports

The research report communicates the findings of the research project. The project should answer questions raised in the statement of the problem and objectives of the study. For a report to communicate effectively it should satisfy the following criteria.

Completeness: should provide all information relevant to the readers.
Accuracy: data generated during data collection should be accurate for the report to be accurate.
Clarity: this is achieved by clear logical thinking and precision of expression. Short simples sentences, no grammatical errors and uniform style and format should observed.
Conciseness: the writer must be concise in their writing. The report should be brief and to the point.

WAYS IN WHICH INFORMATION TECHNOLOGY CAN IMPROVE RESEARCH.

Globalization

Information technology has not only brought the world closer together but it has allowed the world’s economy to become a single interdependent system. This means that a researcher cannot only share information quickly and efficiently but can also bring down barriers of linguistic and geographic boundaries (Kothari, 2010). Of great importance is the issue that the world has developed into a global village due to the help of information technology allowing researchers not only separated by distance but also by language to share information with each other in the language one understands.

Mugenda and Mugenda (1998) state that, there has been growing interest in research networks and its implications on the creation of new knowledge. The rapidly increased use of the web, internet, intranets, extranets, e-business, e-commerce and mobile computing changes the manner in which research is done and even application in business transactions. Of special importance is the emergence of the second generation e-commerce applications such as m-commerce, c-commerce, e-learning and e-government where research can be carried out effectively. It enables researchers to stimulate, visualize, model and experiment with complex, real-world problems, promoting exploratory and inquiry- based models of researching. Further in research, information technology enables and fosters development of critical thinking skills, visualization, conceptualization, integration of disparate data and resolution of patterns within data (Kothari, 2010)

Online interviewing and focus groups

The internet is used to communicate with research subjects and in addition to quantitative surveys, online approaches to qualitative research have been tried. Online interviewing and focus groups can be an effective means to collect qualitative data. Careful planning and attention to rapport building is useful to elicit the kind of accounts that researchers hope for. People can take part from home and this be able to fit in the interview even though they would travel to a face to face meeting. They may feel more comfortable discussing sensitive subjects online such as fertility problems. According to Slavin (2007), where people are comfortable with the idea of communicating online, it can be possible to use email to collect rich qualitative data. People being interviewed feel that the online interaction puts them more in charge than they would be face-to-face, allowing them to think carefully and reflect on their answers and also respond only when they feel able to cope with the interaction. Data collected online can therefore be useful to researchers and can sometimes provide insights that face to face methods do not.

Fieldwork on online settings

There is a large quantity of naturally occurring data on the internet that allows a researcher to observe what people do under less controlled circumstances. The internet is a filed site for ethnographic research in which the researcher uses some familiar techniques from more conventional ethnography to explore the culture in the online setting. Ethnography research involves a combination of techniques. When carried out online, it includes observation through reading messages or being present in interactions together with online interviews. Sometimes face to face interviews may be carried out particularly when participants themselves have face to face meetings in the normal course of events ( www.researchnavigator.com )

Publications

To publish is to bring specific information to the public domain through written documents or by posting such information in a website. Publications refer to published documents including books, periodicals, scholarly journals, magazines among others.. Publishing also includes the distribution of copies of the written work to the general public with the consent of the author. The document may be distributed free on sold. Researchers are encouraged to publish their findings in journals books or other forms of publication. This facilitates wider sharing of research findings among researchers, professionals and policy makers. Publishing research findings and regularly reading journals papers published by other researchers improve ones research skills.

Published articles thoroughly describe the research methodology that the author has followed in conducting the study. Experience has shown that prolific writers of research materials also tend to be exemplary researchers. What such people share with the research community helps to shape the way research is conducted by setting certain standards. Subjecting journal papers to referees, ensures that high standards are maintained in research (Mugenda and Mugenda 1998).

Bridging the cultural gap

Information technology has helped to bridge the cultural gap by helping people from different cultures to communicate with one another and allow for the exchange of views and ideas thus creating awareness and reducing prejudice. Further, a researcher is facilitated by information technology in connections across disciplinary, institutional, geographical and cultural boundaries (Slavin 2007).

Saving time

Internet is open for twenty four hours daily all over the globe. This means that a research can be undertaken all the time in a twenty four hour basis. This is unlike the library or research sample which has restricted time. This includes printing the literature one may find fit for benchmarking or aiding his research study.

Information technology to researchers aids and illustrates the workings of complex methods by exploring cause-effect relationships and hypothetical scenarios. It aids research by encouraging collaboration with other individuals, teams or institutions while exposing researchers to different ideas and perspectives within a limited time frame.
Calculations/ tabulation of data

Computers perform calculations almost at the end speed of light. They are ideally suited for data analysis concerning large research projects. Researchers are essentially concerned with huge storage of data, their faster retrieval when required and processing of data with the aid of various techniques (Baikie 2003). Researchers in economics and other social sciences have found electronic computers to constitute an indispensable part of their research equipment. Computers can perform many statistical calculations easily and quickly. Software packages are readily available for the various simple and complicated analytical and quantitative and complicated analytical and qualitative techniques of which researchers generally make use of.

To the researcher, the use of computer to analyze complex, data has made complicated research designs practical. Electronic computers have by now become an indispensable part of research students in the physical and behavioral sciences as well as in the humanities. The research student, in this age of computer technology, must be exposed to the methods and use of computers. A basic understanding of the manner in which a computer works helps a person to appreciate the utility of this powerful tool. Researchers using computers can carry on the task at faster speed and with great reliability. The developments now taking place in computer technology will further enhance and facilitate the use of computers for researchers. Programming knowledge would no longer remain an obstacle in the use of a computer (Kothari 2010)

WAYS IN WHICH INFORMATION TECHNOLOGY HAS BEEN MISUSED IN RESEARCH

Plagiarism is the unauthorized use of close imitation of the language and thoughts of another and representation of them as their one’s original work or simply copying of another’s written work. This is the biggest challenge in research work. It is no secret that plagiarism is the biggest trouble that a writer can get into. A thousand of free information provided by the internet has allowed dishonest writers who steal other peoples work and present it as their own. Most research scholars have misused Information Technology by just copying what others have already researched on and posted their results in the website, this contributes to lack of originality because the work is only but a duplication of other’s work. Some times because the work in the website may have been produced a long time ago, the information at the time of duplication is not up to date especially when the information relies on data or numerical values because time has passed since the data that is in the website was produced hence the duplicated work is not a true reflection of the current time (data is not up to date)

Over reliance to information may lead to getting irrelevant information

Technology has rocked the research with quite a chunk of literature. This information may be very relevant to one title or topic but equally the literature may be quite large that for one to go through and sieve this requirement is quite a task, this is further made worse by the fact that one in the process may carry out unnecessary information which does not add up properly and this contributes to irrelevant information being gathered. More so today the tools used in research are so complicated that if not correctly and rightly controlled will give out wrong perception including researcher’s conclusion and recommendation which may be disastrous if implemented. This implies that some of these techniques of research like sampling and gathering information must be practically done and results analyzed rather than using the Information Technology to generate them.

Lack of originality

Since the introduction of Information Technology on research, it has come to notice that most of the researchers especially the scholars do not produce their own original work from the field since they may be undertaking a similar project that is already posted in websites. Since the work could be accessible to anybody this could lead to duplicating work that has already been done without even getting data from the actual field but simply doing exactly what others have already done, this contributes to lack of originality and creativity and this may make the whole research lose its meaning because no much personal effort has been made but only relying on others efforts.

Lack of privacy or confidentiality (impact on confidentiality)

Confidentiality is one of the ethical issues in research work. A research project should guarantee confidentiality when the researcher can identify a given person’s responses but essentially promises not to do so in publicly. In an interview survey for example the researcher could make public the income reported by a given respondent, but the respondent is assured this will not be done. When a research project is confidential, it is like researchers responsibility to make that fact clear to the respondent. This is not always an easy task to follow. Once the researcher does not keep this confidentiality and the information spreads to the public either through the media or the website, it spreads to a large number of populations and this creates much harm to the feelings of the respondent, because information that is in the internet can be accessed to everyone hence compromising privacy or confidentiality. J. Steven (1996a, 1996b) points out that a certificate of confidentiality to protect confidentiality of research subject data against disclosure can act as an important protection through both filed reports and data in the websites.

Hacking of research information

Researchers may use computer to steal research data and information stored from other computers through hacking. Computer hacking is the practice of modifying computer hardware and software to accomplish a goal outside of the original purpose. People who engage in computer hacking are called hackers. This involves manipulating other person’s security details (password) and accessing his/her information in the computer software without his/her consent. This is a crime, in some cases, computer hackers or thieves often take advantage of one’s effort to access their information has already been exposed to the public with or without her knowledge. It is true that internet has made research work easier but it is also reflecting an uglier side to its existence through a number of problems to its users. Internet theft and misuse of information has been a great challenge. Cases of people using someone’s information and research and using it as if it were their own have been reported through this practice of hacking. Since at times protection in the computer software may not be effective to keep off hackers, researchers find it difficult to do their work and at times are forced to do it manually.

Production of poor results

Hokanson and Hooper (2000 ) report that technology use in Education research has generated poor results .He argues out that technology has been used only to automate existing educational processes and thus has short changed its potential. The computer technology be realized leading to improved Educational quality and productivity. In cases where one relies on only what others have already done is a topic related to what one may under take leads to production of results that are not a true reflection.

Encouragement of cheating through impersonification through the websites

Impersonification in the act of using another person’s identity and details to perform a certain task for him/her. Today people do not do their own work anymore. More and more students and researchers rely on the web to do their work for them. This can range from copy and pasting to paying a website to write a paper for them. This encourages cheating in ones paper in research because the work has been done by another person.

Use of dominance culture that may be irrelevant to our situation

While information technology may have made the world a global village, it has also contributed to one culture dominating another .In all aspects of life including scholarly work, business among others, for example it is now argued that the US researchers influence others all over the world on how to perform their research and if one does not conform with their standards no matter how relevant the results are, it may be nullified. Languages too have become overshadowed with English becoming the primary mode of communication for research everywhere, bearing in mind that not all countries or nations speak or communicate in English.

Loosing of data through over reliance

Since most researchers store their information in computer storage devices which includes flash disks, CDS and tapes, without proper backup the information may be lost. It is no longer important to file one’s work in written records because they type their work in computers, store the data in the computers, when these devices get destroyed the information is lost. This issue is brought about by over reliance on technology.

Null/ Alternative Hypothesis

Null Hypothesis- There is no significant relationship e.g there is no significant relationship between training and performance.

Alternative Hypothesis- indicates a relationship-If employees are trained, performance will improve

Directional/ Non Directional Hypothesis

The direction of the relationship between the variables- positive or negative is indicated. The greater the stress the lesser the performance Non directional Hypothesis do not indicate the direction of the relationship- There is a relationship between rewards and sales

Inductive/deductive hypothesis

Generalization based on observation.

Critical Thinking
C conclusion
Recommendations

Deductive Hypothesis- Derived from theory

Hypothesis (based on Theory)
Data collection to support hypothesis

QUALITIES OF AN EFFECTIVE HYPOTHESIS

States researchers expectation concerning the relationship between variables
Indicates what the researcher thinks the outcome of the study will be
The data collected either supports or refutes the hypothesis
The hypothesis is testable
Clear and brief
The hypothesis is consistent with the existing body of knowledge

REFERENCING

Academic conventions and copyright law require that you acknowledge when you use the ideas of others. In most cases, this means stating which book or journal article is the source of an idea or quotation. Referencing is a standardized method of formatting the information sources used in assignments or written work and serves the purpose of acknowledging the source and allowing the reader to trace the source.

There are several styles used for referencing among them are;

Havard AGPS Referencing guide.
American Psychological association (APA) Referencing guide.

The APA style consists of rules and conventions for formatting term papers, journal articles, books e.t.c in the behavioral and social sciences.

Reference lists and bibliography

A reference list is a list of all the sources that have been used as in-text references in the research report. A bibliography is a wider list of reading that includes both in-text references and other sources which may have informed thinking on the topic, but may not have been placed as an in-text reference in the research writing.One of the main reasons why referencing is important is to avoid plagiarism. Plagiarism is taking, using and submitting the thoughts, writings etc. of another person as one’s own.

APA CITATION SPECIFICATIONS – IN TEXT

One Author: If a book has just one author, the author’s last name followed by the publication date is usually provided. For example: Freud (1900) found out……Or …as Jones (2001) described…

Direct Quotation: If a direct quotation is used, the APA citing should always include the page number where the source can be found.

No Author: Some sources lack information on authorship. In-text citations should use a short article title enclosed in parentheses and the date. When article titles are long, simply use the first word or two of the title.

For example: The study revealed a strong positive correlation between the two variables (“Learn APA,” 2006).

Referencing materials without dates : According to the official APA style website, the correct way to do this is to include the notation “n.d.” for no date. For example, you would cite an article from a website as follows:

Cherry, K. (n.d.). How to become a psychologist. About.com. Retrieved from http://psychology.about.com/od/careersinpsychology/ss/become-a-psychologist.htm

Two Authors: When a source lists two authors, in-text citations should provide the last names of both authors and the publication date. For example: …later studies demonstrated a similar effect (Ross & Hudson, 2004).Or Ross and Hudson (2004) found a similar effect in later studies.

Three to Seven Authors: Proper APA format for sources with three to seven authors requires listing the last names of all authors the first time they are cited as well as the publication date.For example: …results indicated a strong positive correlation between the two variables (Robsen, Hutchkins, Ru, & Selanis, 1989)., Or Robsen, Hutchkins, Ru, & Selanis (1989) found a strong positive correlation between the two variables.

Subsequent citations should list only the last name of the first author along with the publication date. For example: Robsen (1989) demonstrated the affects of…Or …in a study demonstrating these effects (Robsen, et al., 1989).

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Cement production and CO 2 emission cycles in the USA: evidence from MS-ARDL and MS-VARDL causality methods with century-long data

Research Article
Open access
Published: 10 May 2024

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Melike E. Bildirici 1 &
Özgür Ömer Ersin ORCID: orcid.org/0000-0002-9177-2780 2

The cement industry is among the top three polluters among all industries and the examination of the nonlinear and cointegration dynamics between cement production and CO 2 emissions has not been explored. Focusing on this research gap, the study employs a novel Markov-switching autoregressive distributed lag (MS-ARDL) model and its generalization to vector error correction, the MS-VARDL model, for regime-dependent causality testing. The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO 2 emission cycles in the USA for a historically long dataset covering 1900–2021. Empirical findings point to nonlinearity in all series and nonlinear cointegration between cement production and cement-induced CO 2 emissions. The phases of regimes coincide closely with NBER’s official economic cycles for the USA. The second regime, characterized by expansions, lasts twice as long relative to the first, the contractionary regime, which contains severe economic recessions, as well as economic crises, the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and the COVID-19 Shutdown and Wars, including WWI and II. In both regimes, the adverse effects of cement production on CO 2 emissions cannot be rejected with varying degrees both in the long and the short run. Markov regime-switching vector autoregressive distributed lag (MS-VARDL) causality tests confirm unidirectional causality from cement production to CO 2 emissions in both regimes. The traditional Granger causality test produces an over-acceptance of causality in a discussed set of cases. Industry-level policy recommendations include investments to help with the shift to green kiln technologies and energy efficiency. National-level policies on renewable energy and carbon capture are also vital considering the energy consumption of cement production.

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Introduction

The issue of environmental pollution has significant effects on global warming. The achievement of sustainability in economic development cannot be achieved without environmental sustainability. As a greenhouse gas, carbon dioxide (CO 2 ) is among the most important sources of global warming and climate change. In the last century, CO 2 emissions have risen to unprecedented levels on which industrial production has strong effects. Compared to the pre-industrial levels (1850–1900), the mean temperatures on Earth have been 1.53 °C higher in the last decades and global warming is affecting life on globe through shifts of climate zones, extreme weather events, alterations in the functioning, and structure of climate including the carbon-cycle feedbacks of Earth (Alkama & Cescatti 2016 ; Forzieri et al. 2017 ; Hoffman et al. 2014 ; Richardson et al. 2013 ). Consequently, if human-induced climate change is not controlled, climate change becomes irreversible. Recent reports from the International Energy Agency (IEA) projects that CO 2 emissions will further reach a peak of 37 billion tons (Gt) in 2025 (IEA 2022a ) and if serious action is not taken soon, global warming will reach an irreversible level in the next 75 years (IEA 2022c ) leading to UN Climate Change Report noted the seriousness of the issue and emphasized the insufficiency in the political commitment already existent (UNCC 2022 ). The report noted that if current policies were to be maintained in the future, CO 2 emissions would reach a 10.6% increase in 2030, just in 8 years; however, to reverse global warming, the opposite, a cut of 45% is necessary before year 2030 (UNCC 2022 ).

If all industries in the world are ranked according to the amount of CO 2 emissions they yield, the cement industry is the top third (The Guardian 2019 ). If the cement industry were a country itself, it would be the third top CO 2 emitter after China and the USA. Worrell et al. stressed that cement production was responsible for 5% of the anthropogenic CO 2 emissions in the early 2000s (Worrell et al. 2001 ). In 2020, its contribution to global CO 2 emissions reached 8–10% (Wu et al. 2022 ). Following these concerns, in the UN’s COP24 meeting that took place in Poland in 2018, cement’s CO 2 emissions were taken into the goals to revert climate change, with a target of 16% reduction in cement production-induced CO 2 emissions by 2030 (Rodgers 2018 ). As a result, the cement sector is one of the most important contributors to CO 2 emissions in the globe with direct and indirect channels.

The direct channel of the cement-induced CO 2 emissions is due to the emissions released during the processes in production. There are three main sources of anthropogenic emissions of CO 2 , i.e., fossil fuel oxidation, land-use change (including deforestation), and decomposition of carbonates, and cement production is considered an important emitter mainly in terms of the third (Andrew 2018 ). CO 2 is emitted from the calcination process of limestone and the combustion of fuels in the kiln during cement production (Costa and Ribeiro 2020 ). Reducing the CO 2 from cement production processes is of great importance. CO 2 emissions are also released due to the utilization of high levels of energy during production. The CO 2 statistics stated in the previous paragraph for cement production avoid indirect releases due to the high levels of energy consumed. As shown by Worrell et al. ( 2001 ) the total amount of CO 2 emissions from processing cement and from the energy it necessitates and the average intensity of CO 2 emissions from global cement production is 222 kg per ton of cement produced. Nagi and Jang stress that the amount is four times higher for Portland cement; each ton of cement produced releases an equal amount of CO 2 emissions (Naqi & Jang 2019 ). The CO 2 emissions of cement accelerate as the share of fossil fuel or nonrenewable energy consumption in the total energy mix is not low and depending on the country and the energy policy followed, the CO 2 mitigation effect would be altered. In the context of Industry 4.0, the nonrenewable energy consumption share of the USA in its total energy use is shown to be one of the highest (M. Bildirici & Ersin 2023 ). Footnote 1 As a result, the rigorous commitment to green energy and a large share of renewable energy in the energy mix would help in the reduction of CO 2 emissions in addition to energy-efficient cement production technologies. As shown in the discussion section, the major cement industries are in China, India, and the USA, and these countries are also among the top countries with high shares of fossil-fuel energy in their energy mixes. As a result, cement industries have not only a national level but also a global effect on CO 2 emissions.

In addition to its role in environmental pollution, cement is an essential product closely linked to economic development policies and various sectors. Economic development projects are generally coupled with construction projects. The nexus between cement production and economic growth has significant connections to business cycles in the economy; cement production is also subject to interconnected fluctuations in the economy and to the influence of fluctuations in the GDP. Business cycles, which include expansionary and contractionary phases, govern economic activity and construction investments which rise during periods of economic development and growth, which fuel cement production.

The business cycle in the USA is shown to be subject to nonlinearity with the expansionary and recessionary periods with asymmetry in characteristics and durations (Hamilton 1989 ). These cycles are frequently influenced by economic crises and deep recessions which also bring about fiscal and monetary interventions of the policymakers to bring the economy back to the track of economic growth. It is clear that the economic policy interventions that favor economic expansions had significant and nonlinear effects on environmental sustainability. The production patterns for cement are expected to be highly nonlinear possessing a cyclical tendency that is also connected to economic activity, not to mention, important historical events such as World Wars or deep recessions. Cement production directly emits CO 2 emissions as a characteristic of cement production and kiln that requires reaching a heat level of 1200 °C. Cement is responsible for 8% of global CO 2 emissions. In addition to its direct effects, the production requires excessive use of energy that further contributes to CO 2 emissions. Indirect effects include the CO 2 emissions geared by construction. Therefore, it is of crucial importance to examine cement production and CO 2 emissions historically by putting forth the cement-induced CO 2 emission cycles and their relation to economic cycles in the USA. In addition to these effects, CO 2 emissions resulting from cement production are expected to be nonlinear and have asymmetric effects that differ in size under distinct regimes with different durations.

With this motivation, the investigation of nonlinear long-run relations and nonlinear causality among cement production and CO 2 emissions will provide vital information regarding the environmental effects of the cement industry from an empirical perspective. For this purpose, the study employs a long sample starting from 1900 to provide a historical perspective. The sample covers economic contractions, deep crises, and abrupt changes caused by World War I and II, the Great Depression of 1929, the Oil Crisis of 1973, the 2009 Great Recession, and 2020 COVID-19. Therefore, the sample provides a laboratory to examine the CO 2 and cement production nexus and the cement-induced CO 2 cycles. The overlook is that these cycles have a relation with economic recessions in the USA; however, the type of recession has a strong influence. As shown in the empirical and discussion sections, cement production and cement-induced CO 2 emissions fluctuate sharply with deep recessions as well as economic crises and abrupt shocks exampled above. The cycles in the cement-induced CO 2 emissions and cement production are in close synchronization with the economic business cycles. Given the size of the cement sector among all sectors, its strong influence on the overall CO 2 emissions of the USA could not be rejected. In addition to the relation of the cement industry with economic cycles in the USA, the relation is not constant, or is linear. The type of the recession matters. We argue that the sector is not affected by short-lasting economic recessions, but has strong relations with longer-lasting and deep recessions, crises, and abrupt changes. The influence of cement on CO 2 emissions differs in size under expansionary and contractionary cement production regimes. Further, cement manufacture is strongly encouraged by the policymakers during periods of recessions and crises for recovery, in addition to economic expansion periods, to contribute to economic growth or to achieve back its track. As a result, contraction in cement production is not a common situation for all recessions and crises, depending on the type of recession. In many cases, especially for long periods of deep recessions or periods of contractions geared by wars, cement is an important sector with inclines in cement production, which also yields cement-induced CO 2 emissions.

In light of the discussion above, the goal of the study is to design a nonlinear method to examine the long-run relation between cement production and environmental pollution in the USA with historically long data covering 1900–2021. The reason for choosing the USA is its significant cement production. In fact, in 2015, the cement industry in the USA yielded 82.8 million tons (81,500,000 long tons; 91,300,000 short tons) of cement, valued at US$9.8 billion (DATIS 2020 ). The USA was ranked as the world’s third-largest cement producer in 2019, trailing behind China and India (USGS 2024 ). By the end of 2022, cement production in the USA had reached around 95 kilometric tons, placing the nation as the fourth-largest cement producer globally after China, India, and Vietnam Footnote 2 (WPR 2024 ). On the other hand, there is no long-term data available for China and India, the top two countries for cement production, and the econometric methods employed in this study require data over a long period.

The study suggests a novel approach, the Markov-switching autoregressive distributed lag (MS-ARDL) model by integrating two seminal methods. The MS-ARDL allows modeling regime dynamics and business-cycle modeling benefiting from the dynamic Markov-switching regressions (MSR) of Hamilton (Hamilton 1989 ). The MS-ARDL approach merges the linear ARDL approach for bound testing and cointegration modeling (Pesaran et al. 2001 ) with the MSR to obtain a unique approach that captures regime-dependent cointegrated long-run relations and short-run relations with different error correction dynamics to the long-run equilibrium under each regime. The MS-ARDL follows single-step modeling of long- and short-run dynamics similar to the ARDL (Pesaran et al. 2001 ), which generalizes the well-known two-stage long-run cointegration methodology (Engle & Granger 1987 ). The proposed model is further generalized to vector autoregressive (VAR) models to obtain the MS-VARDL model in this study. Both MS-ARDL and MS-VARDL models provide insightful information concerning regime durations, cycle dating, and regime-dependent Granger causality investigation for the cement production and cement-induced-CO 2 emission relation. The contribution of this study to the literature is twofold. Firstly, the study proposes the MS-ARDL and MS-VARDL models, which are expected to provide significant contributions to the empirical analyses, especially in energy and environmental research. Secondly, the contribution of the article to the environmental literature is emphasized by analyzing 123 years of data, highlighting the impact of long-term data usage in this literature.

The paper is structured as follows. The literature review is given in the “ Literature review ” section, where a discussion of cement-CO 2 emission relation is evaluated. The econometric methodology for the MS-ARDL model is given in the “ Econometric methodology ” section. The empirical results are given in the “ Econometric results ” section. The discussion, policy recommendations, and conclusion are given in the “ Conclusion ” section.

Literature review

If the literature on industrial production and emissions is investigated, a large body of research focuses on the positive effects of production on emissions at low levels of production and the relation being reversed at high industrial production levels, the so-called environmental Kuznets curve (EKC). Further, we noted that the empirical literature on the cement and emissions nexus is very limited, especially concerning econometric findings at the national level. The existing recent research focuses mainly on China, and as of our literature search, only a few papers discuss the relation of cement industry emissions in the context of other countries, especially the USA.

The empirical research on emission-gross domestic product (GDP) has gained significant pace following the seminal findings (Grossman & Krueger 1991 ; Selden & Song 1994 ; Stern 1994 ). Grossman and Krueger’s empirical results related the levels of two main pollutants by signifying an inverted- U -shaped relation, i.e., environmental pollution increasing (decreasing) at low (high) levels of per capita (Grossman & Krueger 1991 ), and Selden and Song underlined declining hazardous emissions at high levels of economic development (Selden & Song 1994 ). The cause of the decline in emissions at high GDP levels was considered as decentralization of industrial production, and the reversal of the positive trend in population growth at high-income levels (Stern 1994 ). To overcome the impossibility of a negative effect of industrial production on emissions, Lopez recommends internalization of emissions and feedback effects at the industry level and emissions should be taken as a factor of production (Lopez 1994 ). Convergence of carbon emissions at high GDP levels is an important factor and several empirical findings stressed sigma, stochastic, and beta convergence in addition to the existence of the environmental Kuznets curve (EKC) (Anjum et al. 2014 ; Pettersson et al. 2014 ). The existence of a decline in emissions at high industrial production levels is rejected empirically after omitting the bias caused by beta convergence on the empirical methods (Stern et al. 2017 ).

The long-run and causal effects between energy consumption, growth, and CO 2 emissions also found significant applications and the importance of energy efficiency and renewable energies were documented (Ozturk & Acaravci 2013 ). Our findings indicated the close relations of these factors to the cycles in the production of cement; however, these relations are strongly nonlinear both in the short and in the long run, and in addition, our findings suggest the advocation of energy efficiency and green energy policies in the cement industry, which has strong ties with the business cycles of economic growth with differentiated dynamics in the expansionary and recessionary regimes. By investigating the environmental and health effects of the construction industry within a comparative perspective with various sectors, the negative effects of cement production on health and air quality are documented (Bildirici 2020 ).

By following nonlinear regime switching neural network models and by calculating the sensitivity of CO 2 growth rates to fossil fuel and economic growth, Bildirici and Ersin emphasize the questionability of linear in parameter-type EKC formulations, in addition to stressing the role of transfer of industrial production to newly industrializing other countries from already industrialized nations (M. Bildirici & Ersin 2018a ). Bildirici and Ersin suggest a novel nonlinear STARDL cointegration model, with which important deviations from the EKC are obtained compared to linear ARDL, and it is suggested that CO 2 and economic growth have nonlinear characteristics due to business cycles, crises, and structural changes in production historically for 1800–2014 period in the USA (M. Bildirici & Ersin 2018b ). Using a panel of countries including the OECD countries with the nonlinear Panel STAR model, the EKC relation is strongly rejected in both regimes for the panel of countries (Ersin 2016 ). Ersin stresses that the turning point threshold is determined by CO 2 emission growth rates, not the economic growth rates; after the turning point, evidence is against the reversal from environmental degradation under nonlinearity and threshold effects (Ersin 2016 ).

The consensus in the literature that focuses on the cement industry and its impacts on the environment relates emissions to energy levels needed in production and a common policy recommendation is to increase energy efficiency. However, concerns were also raised about how energy efficiency would slow down the emissions of the cement industry. Accordingly, clinker production activity is identified as the central polluter in the industry (Wang et al. 2013 ), and estimates show that the cement industry is the highest emitter industry both in China and in the world (Teller et al. 2000 ). Empirical findings determine labor productivity and energy intensity as major determinants of CO 2 emissions in the cement sector (Lin & Zhang 2016 ). Ke et al. confirm the carbon emissions due to the energy intensity of cement production and advocate energy efficiency to lessen emissions (Ke et al. 2012 ). Xu et al. ( 2012 ) distinguish among four features of cement manufacture, overall output, ratio of clinker, processing technique, and type of energy used up per kiln type (J. H. Xu et al. 2012 ). They identify the link between growth in cement production and economic growth coupled with infrastructure and construction sectors (J. H. Xu et al. 2012 ).

Specific investigation of the cement industry and its effects on emissions has gained increasing attention and led to important findings (Bekun et al. 2022 ; Cai et al. 2016 ; Gao et al. 2017 ; Ren et al. 2023 ; Supino et al. 2016 ; Tan et al. 2022 ), Further, the majority of empirical research on national data is centered on China with few exceptions. Various studies are investigated which focus on different sectors and among these, some have ties with the cement industry. Regarding important mitigating effects concerning emissions, the emphasized sectors in the literature, other than cement, include petrochemical (Xin et al. 2022 ), mining (Chen & Yan 2022 ; Li et al. 2023 ), logistics (Liang et al. 2022 ; B. Xu & Xu 2022 ), transportation (M. Liu et al. 2021 ; H. Xu et al. 2022 ), steel and nonferrous metal (J. Zhang et al. 2023 ), foundry (Zheng et al. 2022 ), manufacturing industry chains (Lin & Teng 2022 ), and coal industry (Xia & Zhang 2022 ). In addition, the construction sector, as a sector related directly to cement consumption, also is among the strong emitters of CO 2 emissions (Y. Liu et al. 2022 ; Zhao et al. 2022 ). The construction sector is followed by sectors of steel, nonferrous sectors, and a fraction of chemical industries as sectors with relations to cement consumption. Concerning the effects of mining (Chen & Yan 2022 ; Li et al. 2023 ), the main findings advocate carbon-neutrality policies in the sector to reduce high levels of emissions (Chen & Yan 2022 ). The nonferrous metal and steel sectors are directly related sectors to the construction sector and have relations to cement consumption. The nonferrous sector has strong effects on CO 2 mitigation and emission reduction strategies are presented (Cao et al. 2022 ; Y. Zhang et al. 2022 ).

Production techniques are criticized in terms of their environmental impacts and alternate techniques are advocated. As an example, the replacement of clinker as the binding material in cement production with recycled material is suggested (Costa and Ribeiro 2020 ). Footnote 3 Martins et al. study the emissions due to solid, construction, and demolition wastes in addition to the energy consumption created through construction contributing to climate change (Martins et al. 2023 ). Karlsson et al. calculate a potential 40% reduction in construction-embodied CO 2 by realizing material efficiency, recycling, and construction supply chains (Karlsson et al. 2021 ). Though zero-emission is advocated through transforming transportation to electric vehicles (EV), if the very large share of fossil-fuel energy in total energy consumption is taken into consideration, as a typical, over 80% in the USA such an EV policy would have little effect without transformation of energy production from nonrenewables which also has strong emission potential in the installment and maintenance (M. Bildirici & Ersin 2023 ).

Studies investigated cement production and emissions in selected countries. Hanle et. al is among the few studies, which emphasize the USA’s cement production highlighting the level of CO 2 emissions it generates (Hanle et al. 2004 ). Footnote 4 The Dutch construction industry is emphasized in terms of recycled concrete materials to achieve circular economy objectives (Yu et al. 2021 ). Pakdel et al. investigate the energy-induced CO 2 mitigation effects of traditional and contemporary methods in the Iranian construction industry (Pakdel et al. 2021 ). Karlsson et al. explore the Swedish road construction industry through the role of supply chains to achieve net-zero CO 2 (Karlsson et al. 2020 ). Huang et al. empirically analyze the nexus between emissions and energy embodied in the construction of buildings in Taipei (Huang et al. 2019 ). Vorayos and Jaitiang ( 2020 ) analyze the relationship between the environment and energy performance of Thailand’s cement industry (Vorayos & Jaitiang 2020 ). Oke et al. ( 2017 ) investigate carbon emission trading in the construction industry in South Africa (Oke et al. 2017 ). The regional dataset for 2000–2005 and data envelopment techniques for India are used to determine the state-level inefficiency levels of the cement sector and the consequences of CO 2 emissions (Kumar Mandal & Madheswaran 2010 ). Turkey’s cement industry is investigated with data envelopment for 51 cement factories in 2016 and CO 2 emission externality in the cement production process is highlighted (Dirik et al. 2019 ). These empirical results revealed that only 16% of all integrated cement factories were efficient leading to inclined environmental worsening (Dirik et al. 2019 ). Belbute and Pereira utilize time-series models with fractional integration to obtain CO 2 emission forecasts from fossil-fuel consumption and cement production in Portugal and their findings could be interpreted as showing the importance of lowering cement production in achieving carbon emission targets (Belbute & Pereira 2020 ). By providing a comparative analysis of China and the USA’s cement industry with nonlinear models and Granger causality among cement production, economic growth, and environmental pollution, Bildirici ( 2019 ) stresses that if nonlinear relations are ignored, policy recommendations would lead to incorrect results which hamper environmental sustainability (M. E. Bildirici 2019 ). It is also shown that the effects of cement production and its effects on environmental degradation and health would be insufficiently identified (M. E. Bildirici 2020 ). Footnote 5

Econometric methodology

Single-regime ardl approach.

Cointegration is a seminal technique that allows the researcher to model long-run and short-run dynamics, adjustment towards the long-run equilibrium following shocks, and the length of adjustment in linear relations (Engle & Granger 1987 ). The ARDL method of Pesaran-Shin-Smith (PSS) generalizes the cointegration method to ARDL methodology with generated testing method of bound tests (Pesaran et al. 2001 ). The paper aims to generalize the linear, i.e., single-regime, ARDL approach to Markov-switching (MS) to account for nonlinear dynamics in long-run relations.

A single-regime long-run linear regression form is

assuming Y t dependent variable being modeled with k number of X 1,t ,…, X k,t independent variables, and the long-run form consisting of k + 1 number of parameters including the intercept $\left\{{\delta }_{0},{\delta }_{1},{\delta }_{2},...,{\delta }_{k}\right\}$ . PSS also allows exogenous variables such as a linear trend, or dummy variable, $D_{t}$ , to be included in the long-run form:

In the Engle-Granger methodology, the variables have a long-run relation if integrated of a common order d and if their linear combination is stationary so that the residuals are stationary (Engle & Granger 1987 ). In the PSS methodology, the ARDL model allows the combination of I (1) and I (0) variables; however, to eliminate degenerate cases and loss in power of the ARDL cointegration test, the dependent variable should be I (1). The short-run model in which the error-correction presentation is embedded is achieved as

where $\omega$ is the error correction parameter and the speed of transition to the long-run equilibrium is $1/\omega$ ; for the mechanism to work, it necessitates an estimate of $\omega$ such that $-1<\widehat{\omega }<0$ similar to the Engle-Granger cointegration model (Engle & Granger 1987 ). For simplicity, in Eq. ( 3 ), $\left\{{Y}_{t}, \, {X}_{1,t}, \, \dots ,{X}_{k,t}\right\}\sim I\left(1\right)$ , so that ${\Delta }^{d}=\Delta$ . However, the Engle-Granger approach is a two-step model in nature given Eqs. ( 1 ) and ( 3 ). The ARDL model of PSS allows long-run and short-run dynamics to be modeled simultaneously within a single-step estimation,

and further, in Eq. ( 4 ), the integration properties of variables are defined as in Pesaran et al. ( 2001 ) so that the series is allowed to be I (1) or I (0) processes or a combination of both (Pesaran et al. 2001 ). The bound test statistic of Pesaran et. al. (2001), F PSS , is calculated by restricting ${\lambda }_{1}$ , ${\lambda }_{2}$ ,…, ${\lambda }_{k+1}$ = 0 under ${H}_{0}:{\lambda }_{1}=0,{\lambda }_{2}=0,...,{\lambda }_{k+1}=0$ , i.e., no cointegration, to be tested against ${H}_{1}:{\lambda }_{1}\ne 0,{\lambda }_{2}\ne 0,...,{\lambda }_{k+1}\ne 0$ . If F PSS > F PSS,Upper and F PSS > F PSS,Lower , Pesaran et al. ( 2001 )’s upper and lower bounds, the result would favor cointegration and long-run association (Pesaran et al. 2001 ). However, confirmation of the existence of a single cointegration vector is necessary (Narayan 2014 ). The error-correction form is a restricted form as

where $\omega {\eta }_{t-1}$ defines the error-correction mechanism and the previous definitions of ${\eta }_{t}$ and $\omega$ hold. For simplicity, assume k = 1. Single-regime ARDL model of Eq. ( 4 ) becomes

and the restricted ARDL representation in Eq. ( 5 ):

In Eq. ( 6 ), single-regime and linear ARDL-type cointegration test hypotheses are ${H}_{0}:{\lambda }_{1}=0,{\lambda }_{2}=0$ , ${H}_{1}:{\lambda }_{1}\ne 0,{\lambda }_{2}\ne 0$ and if statistically F PSS > F PSS,Upper and F PSS > F PSS,Lower , the long-run linear association is accepted. If cointegration is established, a confirmatory test is ${H}_{0}:\omega =0$ and ${H}_{1}:\omega \ne 0$ in Eq. ( 7 ); the former suggests no linear cointegration, by assuming only a linear form of a long-run association. The above-mentioned ARDL methodology has been challenged and criticized for various aspects: (i) Over-acceptance of cointegration, Narayan’s critical values should be preferred (Narayan 2014 ), especially for small samples. Further confirmation of the existence of a single cointegration vector is necessary (Narayan 2014 ). (ii) PSS requires dependent variable to follow ${Y}_{t}\sim I\left(1\right)$ to avoid power loss in the test procedure and to avoid degenerate case-1 (McNown et al. 2018 ). Under such cases, the F or t-tests of ARDL cointegration become inconclusive (McNown et al. 2018 ). Footnote 6 (iii) Bildirici and Ersin ( 2018a , b ) noted ignoring nonlinearity would lead to incorrect policy recommendations and introduce smooth transition ARDL (STARDL) models, by generalizing the single-regime ARDL to smooth transition autoregression (STAR) type nonlinear processes to nonlinear cointegration. Footnote 7

(iv) Banerjee et al. ( 2017 ) show the loss of power of the ARDL test under structural breaks and integrate Fourier terms into the ARDL model. Bildirici and Ersin ( 2023 ) generalize the proposed Fourier ARDL model to bootstrapping ARDL model to achieve Panel Fourier BARDL to control inefficiency under structural change and nonlinearity (M. Bildirici & Ersin 2023 ). Banerjee et al. ( 2017 ) argue that the Fourier functions with different dimensions could capture various forms and numbers of nonlinear structural changesMetin girmek için buraya tıklayın veya dokunun.. Enders and Lee ( 2012 ) show that Fourier is more efficient in correcting the bias in unit root tests under smooth changes and less efficient in abrupt changes (Enders & Lee 2012 ). MS-type regime models are capable of capturing sudden and abrupt shifts in regimes in addition to determining the dating and duration of regimes. Footnote 8

Markov regime-switching ARDL model

The MS-ARDL model is a nonlinear error correction (NEC) model that allows nonlinearity in both long-run and short-run dynamics simultaneously. Therefore, MS-type regime changes (Hamilton 1989 ) are integrated into the ARDL model to achieve the Markov regime-switching autoregressive distributed lag (MS-ARDL) model. Various other forms of NEC are evaluated by Saikkonen ( 2008 ). Among these models, Saikkonen ( 2005 ) allows regime changes governed by an indicator function to achieve a threshold NEC. Saikkonen ( 2008 ) also discusses possible extensions to Markovian regimes to achieve NEC models.

Significant models on modeling NEC have been proposed with various nonlinear techniques. A general tendency for NEC modeling so far has been to keep the short-run parameters linear while allowing error correction parameters to be regime-specific generalizations of Engle-Granger methodology (Kapetanios et al. 2006 ; Saikkonen 2005 , 2008 ). Krolzig develops the MS-VEC model in a VAR setting and the MS-VEC allows MS-type changes in the error correction as a nonlinear generalization to Engle-Granger’s cointegration approach (H. M. Krolzig et al. 2002 ). Footnote 9 Pavlyuk applies MS-ARDL model to traffic forecasting (Pavlyuk 2017 ); however, the model is an MS-ARX model and does not utilize the AR and DL terms in the spirit of NEC modeling and PSS-type ARDL cointegration.

Other NEC models include Kapetanios et al. which allow exponential smooth transition functions to capture regime-dependent error correction (Kapetanios et al. 2006 ). Shin et al. developed a nonlinear ARDL (NARDL) framework with a threshold-type nonlinearity instead of MS (Shin et al. 2013 ). Bildirici and Ersin generalize the ARDL to smooth transition type nonlinearity with the smooth transition ARDL (STARDL) model. The STARDL model generalizes ARDL to nonlinearity and asymmetry both for the long- and short-run relations (M. Bildirici & Ersin 2018b ). With this respect, both STARDL and the NARDL models relax the symmetry assumption for either the long- or the short-run terms.

An MS-ARDL model with two or more regimes is

where ${\alpha }^{{s}_{t}}={\left\{{\alpha }_{0}^{{s}_{t}},{\alpha }_{1,i}^{{s}_{t}},{\alpha }_{2,i}^{{s}_{t}}...,{\alpha }_{k+1}^{{s}_{t}}\right\}}^{\prime}$ is the short and ${\lambda }^{{s}_{t}}={\left\{{\lambda }_{1}^{{s}_{t}},{\lambda }_{2}^{{s}_{t}},...,{\lambda }_{k+1}^{{s}_{t}}\right\}}^{\prime}$ is the long-run parameter vector, both being regime-dependent; regime changes are governed with s t for r number of regimes ${s}_{t}\in \left\{\mathrm{1,2},...,r\right\}$ . Hence, s t = 1, s t = 2,…, and s t = r is a finite regime sequence. $N\left( 0,\sum_{}^{}\left( s_{t} \right) \right)$ is distributed with zero conditional mean and regime-dependent $\sum \left({s}_{t}\right)$ nonnegative conditional variance. As a result, ${\varepsilon }_{t}^{{s}_{t}}$ are allowed to be locally homoskedastic for sub-regression spaces, while being globally heteroskedastic. For a similar approach, see Saikkonen ( 2008 ). The model generalizes the single-regime ARDL in Eq. ( 4 ) to MS-type regime switches in Eq. ( 8 ).

The generalization of ARDL bound testing is necessary in the MS-ARDL modeling stages. Once the existence of MS-ARDL type nonlinearity is accepted against linear ARDL following the Davies linearity test, the null hypothesis of no cointegration relation is

which means neither linear nor nonlinear error correction exists, to be tested against the alternative of MS-type nonlinear cointegration,

defining a regime-dependent cointegration in each distinct regime. The testing requires a conventional F test approach, the calculated F statistic is F MSARDL , and if it passes both the upper and lower bounds, F MSARDL > F PSS,upper and F MSARDL > F PSS,Lower , the H 0 null hypothesis of no cointegration is rejected against the alternative H 1 , that is, MS-ARDL-type cointegration with r number of regimes. The proposed F MSARDL test statistic follows an F distribution, F ( q , n − r ( k + 1) − 1), with q = r ( k + 1) where r represents the number of regimes and k + 1 is the number of ${\lambda }^{{s}_{t}}$ tested for cointegration for each regime.

By replacing the long-run part with the regime-specific error correction mechanism, reduced form nonlinear MS-ARDL error correction representation of Eq. ( 8 ) is

${\omega }^{{s}_{t}}$ is a regime-specific error correction parameter for ${\eta }_{t-1}$ , the error-correction term. If the error correction parameter estimate, ${\widehat{\omega }}^{{s}_{t}}$ , is statistically accepted to lie between $-1<{\widehat{\omega }}^{ {s}_{t}=r}<0$ , regime-specific error correction duration is calculated as 1/ ${\omega }^{{s}_{t}}$ , which holds for each r distinct regimes as long as ${\widehat{\omega }}^{ {s}_{t}=1}\ne {\widehat{\omega }}^{ {s}_{t}=2}\ne ...\ne {\widehat{\omega }}^{ {s}_{t}=r}$ . Footnote 10

The conditional probability density of time series y t is stated as

where ${\phi }_{r}$ is the vector of parameters in r = 1,2, … , r number of regimes (H.-M. Krolzig 1997 ). The Markov chain defining the regime-switching process for the model is as follows:

where p ij is the probability of being in regime i at time t conditional on the state (or regime) j at time t − 1 (Hamilton 1989 ). Similar to the MS-AR and MS-VAR models, p ij is subject to

where $P\left\{\left.{s}_{t}\right|{s}_{t-1};\rho \right\}$ is the probability of state s t at period t conditional on the previous state s t − 1 (M. E. Bildirici 2020 ). The switching variable, s t , is an unobserved discrete-state Markov chain, which governs the endogenous switches in r number of regimes (Krolzig & Toro 2002 ). Footnote 11 In each distinct regime, a locally linear ARDL sub-space exists defining regime-specific relations among modeled time series. Hence, it is an irreducible ergodic Markov process with r number of states for which the transition matrix is (Hamilton 1989 )

Consistent with the MS-VAR and MS-AR models, the Markov chain follows the irreducible and ergodic process and each p ij has an unconditional and stationary distribution given the ergodicity of the Markov process (H.-M. Krolzig 1997 ). The probability of ${s}_{t-1}=i$ at t − 1 is conditional on the information set available and the parameter set, $\Omega_{t-1};\;\phi_r$ . Hence, in the iteration process, for t = 1, 2, …, T , the probability for the previous period is used as an input:

The present state ${\xi }_{it}$ includes all information regarding the Markovian process that follows in the future (H.-M. Krolzig 1997 ):

The conditional log-likelihood is stated as $\log\;f\;(y_1,y_2,...,y_T\vert y_0;\;\phi)=\sum\log\;f\;(y_t\vert\Omega_{t-1};\;\phi)$ .

For a two-regime MS-ARDL model, Eqs. ( 15 ) and ( 16 ) become.

where the unconditional distribution of each p ij is

and the calculation leads to

In the case of two regimes, observations are conveyed into the first sub-regression space if $Pr({s}_{t}= \, 1\left|{Y}_{T}\right.)\ge 0.5$ or to the second if $Pr({s}_{t}= \, 1\left|{Y}_{T}\right.)<0.5$ . In the estimation step, the expectation maximization (EM) algorithm is utilized (Hamilton 1989 ).

Markov regime-switching vector autoregressive distributed lag model

The MS-ARDL model assumes both the long-run and short-run dynamics to follow nonlinear regime-switching. A vector autoregressive (VAR) generalization of the MS-ARDL model is necessary to investigate the existence of a single cointegration vector. In addition, the MS-VARDL model could be easily adapted to examine nonlinear Granger causality between the analyzed variables depending on the distinct regimes. Therefore, it is convenient to write the Markov-switching vector ARDL (MS-VARDL) model as a VAR generalization of Eq. ( 8 ). For simplicity, a two-variable, two-regime MS-VARDL model is given as

where ${\alpha }_{1,i}^{{s}_{t}},{\alpha }_{2,i}^{{s}_{t}}$ and ${\theta }_{1,i}^{{s}_{t}},{\theta }_{2,i}^{{s}_{t}}$ are the short-run parameter sets in MS-VARDL vectors 1 and 2 and ${\lambda }_{1}^{{s}_{t}},{\lambda }_{2}^{{s}_{t}}$ and ${\tau }_{1}^{{s}_{t}},{\tau }_{2}^{{s}_{t}}$ are the vector-specific long-run parameters. Given that ${s}_{t}=\mathrm{1,2}$ , cointegration testing necessitates repeating the MS-ARDL cointegration test separately for each vector in Eq. ( 22 ) for ${\lambda }_{1}^{{s}_{t}},{\lambda }_{2}^{{s}_{t}}$ and for $\tau_{1}^{{s_{t} }} ,\tau_{2}^{{s_{t} }}$ . In vector 1, zero-restricted $\lambda_{1}^{{s_{t} }} ,\lambda_{2}^{{s_{t} }}$ lead to the null hypothesis of no cointegration (linear or nonlinear) in both regimes:

For the second vector, the null of no-cointegration,

is tested against nonlinear cointegration as

For both tests, F MSARDL test statistic follows an F distribution as $F_{MSARDL} \sim F(q,n - r\left( {m + n + k + 1) - 2} \right)$ and for the two-variate, two-regime model, q = 4. As a next step, one could also estimate a restricted error correction form of MS-VARDL for confirmatory purposes:

To test no-cointegration (linear or nonlinear) against nonlinear cointegration, hypotheses are $H_{0} :\omega_{1}^{{s_{t} }} = 0$ and ${H}_{1}:{\omega }_{1}^{{s}_{t}}\ne {0}{{\text{for}}}{s}_{t}=\mathrm{1,2}$ in vector 1, and $H_{0} :\omega_{2}^{{s_{t} }} = 0^{{}}$ and $H_{1} :\omega_{2}^{{s_{t} }} \ne 0$ in vector 2 of the model given in Eq. ( 27 ). Vector-specific F MS-VARDL test statistic follows $F_{MS - VARDL} \sim (2,n - r\left( {m + n + 1} \right) - 2)$ . The MS-VARDL modeling steps proposed above aim at testing nonlinear ARDL-type error correction occurring in each vector. For specific applications, researchers could also consider testing the existence of regime-specific nonlinear cointegration (M. Bildirici & Ersin 2018b ). In this case, once MS-VARDL given in Eq. ( 22 ) is estimated sub-tests targeting specific regimes for specific vectors are likely. As a typical, assume testing regime 1 of vector 1, a low volatility or economic growth regime. Regime-specific hypotheses are $H_{0} :\lambda_{1}^{{s_{t} = 1}} = \lambda_{2}^{{s_{t} = 1}} = 0$ , $H_{1} :\lambda_{1}^{{s_{t} = 1}} \ne \lambda_{2}^{{s_{t} = 1}} \ne 0$ . For regime-specific or global nonlinear cointegration, readers are referred to M. Bildirici and Ersin ( 2018b ).

To achieve the existence of multiple regimes, Davies tests should be applied. Further, the stability of the ergodic switching probabilities should be examined with the diagonal of Eq. ( 15 ) or in a two-regime model, the p 11 and p 22 in Eq. ( 18 ) so that p 11 < 0.5, p 22 < 0.5 to achieve persistence in each regime in addition to confirming their statistical significance.

MS-VARDL in Eq. ( 22 ) reduces to the MS vector error correction (MS-VEC) model (H.-M. Krolzig & Toro 2002 ) under very mild restrictions applied on the long- and short-term parameters. The MS-VEC generalizes the VEC model to MS and cointegration methodology. The MS-VARDL, on the other hand, generalizes MS-ARDL to MS-VARDL. The nonlinear MS-VEC model is given as (Clements & Krolzig 2002 ; H.-M. Krolzig 1997 )

$\delta^{{s_{t} }}$ is a drift term that is a function that shifts the intercept in the long-run equation. $\beta^{\prime}$ is the long-run parameter vector and B i is the short-run parameter set. The short-run parameters are not subject to regime-switching. Y t is the variable matrix and the model is distinguished as a shifting mean regime-switching model for s t = 1,2,…, r number of regimes. By applying zero restrictions to an MS-VARDL, the reduced form MS-VEC representation exists. Footnote 12

Econometric results

The study will focus on the following steps in the empirical section:

Unit root (UR) testing with a battery of tests allowing different forms of data-generating processes. Included tests are ADF, KPSS, KSS, and F-ADF. KPSS is robust to various forms of structural breaks, the KSS test (Kapetanios et al. 2006 ) tests unit root against nonlinear stationary series, and F-ADF is the Fourier ADF test of Engle and Lee (2011) known to be robust to a wide form of nonlinear series in addition to smooth structural breaks.

F bound testing with traditional single-regime ARDL and Johansen cointegration test to investigate the existence of cointegration.

The BDS test (Broock et al. 1996 ) is applied to investigate the nonlinearity of the series.

Nonlinear regime-dependent bound testing is tested with the MS-ARDL test.

Single-regime ARDL and nonlinear MS-ARDL models are estimated.

Determination of regime durations, datings, and regime switching probabilities for MS-ARDL.

Linearity is tested against regime-dependent nonlinearity with F tests.

Model evaluation with diagnostics tests.

The determination of the direction of causality and comparative analysis with single-regime causality (VEC-based) and regime-switching causality (MS-VARDL-based).

Inference and policy recommendations following the direction of causality determination.

The study is among one of the studies that utilize historically long datasets in the literature focusing on environmental sustainability within econometric respects. In terms of the evaluation of the effects of cement on the environment, the study is, to our knowledge, a pioneering study that evaluates a historically long sample for the post-1900 period in terms of focusing on the econometric relations between CO 2 emissions and cement production. Footnote 13 The sample covers the 1900–2021 period for the USA and the dataset is yearly. The period contains several significant events, including the First and Second World Wars, the 1973 Oil Crises, and important economic crises, such as the 1929 Great Depression, the Great Recession in 2008, and recently, COVID-19. The emission data represents CO 2 emissions from cement production in the USA and is in kilotons of CO 2 . Cement production ( CP t ) is in billion metric tons and is available from the Andrew ( 2022 ) database which obtains the yearly CP t data from the U.S. Geological Survey (Andrew 2018 , 2022 ). Variables are subject to natural logarithms as LCO t = ln( CO 2t ) and LCP t = ln( CP t ). As reported in the following section, these level series contain unit roots and are integrated of order 1. After first differencing, the respectful series are Δ LCO t and Δ LCP t , which also represent the growth rates. The descriptive statistics are reported in Table 1 .

For the level series, Jarque–Bera test statistics imply that, at 5% level of significance, normality for level variables cannot be rejected. For the first differenced series, series are not normally distributed and are subject to skewness and excess kurtosis. In the next step, series are tested for stationarity and unit roots.

Unit root and stationarity tests

The unit root tests are used to determine the order of integration of the series and whether the variables are I (0) or I (1). A battery of tests are applied which include traditional tests in addition to those robust to various forms of nonlinearity. These tests include the Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), Kapetanios-Shin-Snell (KSS), and Enders and Lee (2011) Fourier-ADF. The test results are reported in Table 2 . The traditional linear unit root test, mainly the ADF test, is known to have size distortions under nonlinearity and structural changes and the ADF test tends to over-reject the null hypothesis of unit root (Nelson et al. 2001 ). KPSS tests stationarity under the null and the test is known to be less influenced by nonlinear series. The KSS tests the null of the unit root series against the alternative of stationary time series following a nonlinear STAR process under the alternative. Enders and Lee’s ( 2012 ) Fourier ADF test is utilized to test the unit root null hypothesis and the test is known to be robust against smooth forms of structural changes. The unit root tests utilized in the study evaluate stationarity under forms of structural changes and nonlinearity. In all tests reported in Table 2 , both LCP t and LCO t series are integrated of a common order of 1, and they become stationary once first differenced.

BDS test results

Broock, Deckert, and Sheinkman (BDS) test is a test based on correlation dimension and the test examines the independent and identically distributed i.i.d. series under the null hypothesis against the alternative of nonnormality caused by series following nonlinear or chaotic behavior. Broock et al. ( 1996 ) provide a recent treatment of the test (Broock et al. 1996 ). BDS test provides an investigation of deviations from independence and is known to be efficient in detecting different forms of nonlinearity. BDS test is also suggested as a model architecture determination tool similar to tests such as the Ljung-Box test of autocorrelation; hence, the test could also be used on residuals of models to investigate remaining nonlinearity (Broock et al. 1996 ).

It should be noted that the unit root and stationarity tests determined that series in levels follow I (1) processes and the model in the next section will utilize both the levels and the first differenced series. In Table 3 , BDS test results are given for series in levels and for series in first differences. For robustness, the variables are also tested with the Tsay test of threshold-type nonlinearity (Tsay 1986 ). The results are given in the last column of Table 3 .

Since BDS and z -test statistics are greater than the critical values at conventional significance levels, the null hypothesis of i.i.d. is rejected for both LCO t and LCP t , and results favor nonrejection of the alternative hypothesis suggesting that series in levels follow nonlinear processes. For their first differenced counterparts, BDS results suggest similar results at conventional significance levels for all embedded dimensions the test is conducted for. Tsay test results are given in the last section of Table 3 , where the test is repeated for different lag orders. The Tsay test results confirm the rejection of linearity for both level and first differenced series at conventional significance levels against the alternative of nonlinearity. The overall results suggest that linear models could not be appropriate due to the nonlinear characteristics of the data analyzed.

MS-ARDL dating of contractions in cement production-emission cycle

In the first stage, we examined the estimated MS-ARDL model in terms of its relation with the NBER contraction (including economic recessions and crises) periods. The results are reported in Table 4 .

The first column includes the estimated contraction dates for cement production in the cement industry and the CO 2 emissions due to cement production. The second column reports the contraction periods reported by the NBER (National Bureau of Economic Recessions). Therefore, the table provides NBER and the authors’ calculations obtained from the MS-ARDL which will provide important insights. The overall look shows that for the majority, the dates and durations match with those reported by NBER. It should be noted that an exact match should not be expected all the time. NBER dating focuses on economic recessions in the whole industry and the model results focus on cement-industry and cement-induced CO 2 emissions. Though NBER dates and cement-CO 2 cycles are generally in line, it is also expected to have leading and especially lagging relations in the CO 2 emissions instead of exact coincidences all the time. The general outlook is, though these are not in general, they occur mainly after deep recessions and crises and after such periods, once expansion in the economy starts, cement production and especially CO 2 emissions follow in certain additional years which leads to acceleration in cement-induced CO 2 cycles. Investigation of a set of selected periods will hinder important information regarding the cement production and CO 2 emission cycles and their relations to the economy as a whole.

NBER contraction dates are calculated from NBER’s business cycle reference dates. NBER announces trough dates and the duration of contraction periods in months starting from the date of the trough. Though NBER cycles are for the economy as a whole, the contraction dates and durations reported by the MS-ARDL model estimations are for cement-induced CO 2 emissions and cement production. The findings suggest that, depending on the economic recession, the cycle dates do not match all the time. As a result, the findings suggest that the cement production and CO 2 cycle generally lags the economy’s business cycle in general except for distinct cases such as the 1914–1916 contraction which shows that the industry was already in crisis. Further, one could combine 1914–1916 and 1917–1921 contractions which totals 1914–1921, which coincides with the 1914–1915 and 1919–1921 economic cycles. It should be noted that these economic cycle dates coincide with WWI and the post-WW1 period explaining the length and duration of the contraction in the cement output and cement-induced CO 2 emissions. Another cycle starting in 1991 is in line with the year of the Golf War. The economic contraction was estimated to be for 1991–1992 by NBER. With our calculations benefiting from the estimated MS-ARDL model, the recession is estimated for 1991–1994, 2 more years, compared to the NBER cycle for the cement-production and cement-induced CO 2 emission relation. Findings show that following deep recessions and wars, tightening in cement production and cement-induced CO 2 emissions follow with a lag. However, it quickly shifts back again to the track of increasing emissions which occurs during the expansionary economic periods. For other dates, we note that the cycles reported by NBER and the MS-ARDL estimations are close matches. The WWII period is taken into consideration as a significant case that lasted between 1939 and 1945. The NBER dates are 1938–1939 and 1945–1946 which present the contraction years which match with those for the cement production and cement-based CO 2 emission cycle.

Another important finding is that the model catches the dates especially crisis periods and deep recessions especially lasting more than 1 year. Given that the model utilizes cement production instead of cement consumption, these findings are expected. Cement consumption responds quickly through declines in demand in the recession periods. However, cement production continues production and stocks the excess supply as inventories and this is especially so in the short recessions which take between 6 and 11 months. As typical, the 1949 recession lasted 11 months according to NBER but no contractions were captured for cement production and cement-induced CO 2 emissions. If growth rates are to be noted, cement production declined by 12% in both 1980 and 1982 and for the cement industry, more drastic declines are captured by the MS-ARDL model. For example, cement production declined by 27% in 1918 (WWI) and 39% in 1944 (WWII), but only a 6% decline in 1930, just after the 1929 Great Depression. In 1931, cement production declined by 31% and by 50% in 1932 corresponding to the deep recession period afterward. After the Oil Crisis in 1973–1974, cement production decreased by 18%. Further, the decline amounted to 30% in 2009, the Great Recession, and only 12% weakening in early 2020 during the COVID-19 shutdown. Footnote 14 The findings confirm that cement production responds to deep recessions and crises and during mild recessions, the effects of business-cycle contractions are relatively less on the cement industry and therefore on cement production-induced CO 2 emissions in such periods. This is expected since both variables are production-based and during recessions and expansions, cement consumption responds relatively fast and with moderate response to economic fluctuations. Consequently, cement-production-induced CO 2 emissions react to profound recessions and economic crises by deteriorating CO 2 emissions. Otherwise, the emitting effect continues given the level of cement production under mild recessions.

Linear (single-regime) ARDL results

To provide a baseline analysis, linear, single-regime ARDL modeling steps are conducted. Results are reported in Table 5 . Investigation of cointegration with the ARDL model also provides crucial input for the determination of the direction of cointegration in addition to providing the opportunity to investigate the residuals for remaining nonlinearity which cannot be captured with the linear model.

The ARDL model is determined as ARDL(1,1) with Schwarz information criterion (SC). By taking each variable as the dependent variable one by one, bound tests are repeated. F PSS is calculated as 13.745, favoring cointegration, if LCO t is assumed as the dependent variable, the cement-production-induced CO 2 . For the counter case, by taking LCP t as the dependent, F PSS = 3.1081, at the 1% significance level, F PSS < F lower and F PSS < F upper , leading to the decision of no cointegration. (Narayan 2014 ; Pesaran et al. 2001 ). The results favored cointegration relation and long-run association between LCO t and LCP t and the direction of relation is determined as LCO t = f ( LCP t ). For confirmatory analysis, the Johansen cointegration test is conducted. Results are reported in the second part of Table 5 . Johansen test confirms a single cointegration equation for the LCO t and LCP t relation.

The selected ARDL model is tested with the BDS test (Broock et al. 1996 ) and results are reported in Table 6 . As suggested by Broock et al. ( 1996 ), the BDS test could be used as a test of remaining nonlinearity if used for the residuals of the model. Accordingly, the linear ARDL model fails to capture the nonlinearity in the residuals and therefore the nonlinear relation between cement production and CO 2 emissions at conventional significance levels. Hence, the linear ARDL model cannot be accepted under remaining nonlinearity and to avoid possible inefficient policy recommendations, nonlinear ARDL methods should be followed (M. Bildirici & Ersin 2018b ). The analysis will continue with nonlinear MS-ARDL-type nonlinearity testing and modeling.

MS-ARDL nonlinear cointegration test results

In this step, the null of linearity in residuals of the linear ARDL model is tested against the alternative of MS-ARDL-type nonlinearity. The Davies-type linearity test statistic is calculated as F = 18.0521, favoring the rejection of the single-regime model against the regime-switching model with 2 regimes. After that, the MS-ARDL test of no cointegration is conducted. In the test, no cointegration (linear or nonlinear) is tested under the null hypothesis against nonlinear cointegration. The test results are given in Table 7 .

Assuming LCO t as the dependent variable, F MS-ARDL test statistic = 9.882477, statistically larger than the 5% critical lower and upper bounds and the results show that LCO t and LCP t have a nonlinear relation in the long run. By assuming LCP t as the dependent variable, a second F MS-ARDL is calculated and is equal to 3.98456, lower than the upper and lower critical F values, leading to the rejection of cointegration at conventional significance levels. Hence, the results determined nonlinear ARDL cointegration between cement production and cement-production-induced CO 2 emissions in addition to determining a single cointegration vector exists only by taking the emissions as the dependent variable. Footnote 15

Model estimation results

Following the nonlinear cointegration test results, a two-regime MS-ARDL model is estimated. In addition, the linear single-regime ARDL model is estimated for comparative purposes. Following Tables 6 and 7 , cement-induced CO 2 emissions are taken as the dependent variable for both the ARDL and MS-ARDL models, and optimum lag length is selected as three for the ARDL and as two for the MS-ARDL model with the Schwarz information criterion. The MS-ARDL model is determined to have two regimes following the linearity tests. Footnote 16 The ARDL and MS-ARDL model estimation results are given in Table 8 where the long-run dynamics are reported in the first part followed by the short-run dynamics, the regime-switching probabilities, and the diagnostic test results.

The linear ARDL model estimation results are in column 1. Once evaluated, though the parameters of cement production are significant in the long run suggesting the net effect of cement production to be positive on emissions, certain results lead us to be cautious about the ARDL model results. First, the error correction mechanism fails to hold since the parameter of ECT t − 1 is estimated as − 0.015, suggesting an error correction duration approaching 66 years, relatively too long compared to the nonlinear and regime-specific error correction parameter estimates; however, the results are not reliable for the linear model due to the factors listed below. Additionally, the error correction term is statistically insignificant for the linear model, suggesting that the error correction cannot be established for the linear model. The remainder of the linear ARDL model is evaluated with BDS and RESET tests. The BDS test results in Table 6 confirmed the remaining nonlinearity in the residuals of the linear ARDL, possibly leading to biased parameter estimates if nonlinearity in the series is ignored. Among the diagnostics tests, Breusch-Pagan-Godfrey (BPG) and Ramsey’s RESET tests reported in the last section favored homoskedastic residuals, but the model was mis-specified at a 5% significance level. If evaluated together, the estimation results of the linear ARDL model fail to produce satisfactory results, and due to remaining nonlinearity in the residuals, the ARDL parameters for the linear model, if interpreted and if taken for policy purposes, would be seriously misleading.

If the diagnostics tests are evaluated for the MS-ARDL model, the goodness of fit statistics of the MS-ARDL model favors a better fit of the nonlinear model over its single-regime counterpart. Ramsey’s RESET and Breusch-Pagan-Godfrey (BPG) test results for the linear model conclude model-misspecification and heteroskedastic residuals at conventional statistical significance levels. The BPG and RESET test results for the MS-ARDL model favor no homoskedasticity in the residuals and no-model-misspecification. The BPG and RESET tests favor no misspecification and homoscedastic residuals for the MS-ARDL model. Once considered together with the nonlinearity test results, diagnostics tests lead to the existence of remaining nonlinearity in the residuals in the ARDL model and selection for the MS-ARDL results over its linear counterpart. For the MS-ARDL model results reported in columns 2 and 3, the remaining nonlinearity in the residuals is tested with the BDS test, and test results confirm no remaining nonlinearity. As a result, the estimation results of the MS-ARDL model will be taken as central in the study to examine the relations between cement production and emissions.

For the MS-ARDL results given in Table 8 , the first and second regimes correspond to the recession and crisis regime (regime 1) and expansion regime (regime 2), respectively. If an overlook is presented to the estimated nonlinear long-run dynamics, it is striking that the impact of cement production on CO 2 emissions is positive in both regimes with different magnitudes. In the long-run equation of regime 1, the relevant coefficients of LCP t − 1 and LCP t − 2 are estimated as 0.286 and 0.198, and in regime 2, 0.167 and 0.159, respectively. The parameters are statistically significant at the 5% significance level, with one exception: the parameter of LCP t − 2 is significant at the 10% significance level only. Hence, a 1% increase in cement production at periods t − 1 and t − 2 leads to 0.286% and 0.198% increases in the CO 2 emissions in regime 1, compared to 0.167% and 0.159% effects in regime 2, the last one being significant at 10% only, but the positive effect persists. The overall result is that the positive effects of cement production on CO 2 emissions cannot be rejected in the USA for the long run. It should be noted that the positive effect of cement production is positive in both regimes.

The short-run dynamics are presented in the second section of Table 8 . Once the parameters of Δ LCP t are investigated for regimes, they confirm the effects of cement production on CO 2 emissions in the short-run in addition to the long-run dynamics presented. Similarly, the findings confirm regime-dependent and asymmetric effects of cement production in the short run. The parameter estimates for Δ LCP t − 1 and Δ LCP t − 2 are − 0.60 and 0.79 in regime 1 and − 0.0007 and 0.14275 in regime 2. Though a 1% increase in the previous year’s cement production decreased emissions by 0.60 in regime 1, the same parameter in regime 2 is estimated as − 0.0007, and almost no effect exists in regime 2 for the first lag of Δ LCP t − 1 . However, for the second lag, the parameter estimate of Δ LCP t − 2 is estimated as 0.14, significant at 5% significance level, and a 1% increase leading to a 0.79% increase in emissions in regime 1 and 0.14% increase in regime 2. Short-run dynamics also confirm significant and positive impacts of cement production on emissions during both regimes and in terms of the long-run portion of the model, this positive effect increases especially during the cement production expansion regimes.

The significance, sign, and size of the error correction terms play a crucial role in establishing cointegration. The error correction parameters are estimated as − 0.25748 and − 0.48296 for regimes 1 and 2. Hence, 25.7% (48.3%) of the deviations from the long-run equilibrium are corrected within 1 period, and the error correction towards the long-run equilibrium takes 3.9 (2.07) years in regime 1 (regime 2). Overall results show that asymmetry and regime dependence are important factors determining the effects of cement production on emissions. The effects of cement production are positive in both regimes while being larger both in the short and in the long run. Additionally, the error correction mechanisms are asymmetric among regimes; the mechanism takes twice as long in regime 1 compared to regime 2. The estimated probability of staying at the regime at period t conditional on the regime at period t − 1 determines the persistence of both regimes. The estimated regime probabilities are p ( s t = 1 | s t = 1) = 0.885 and p ( s t = 2 | s t = 2) = 0.940845 for regimes 1 and 2, signifying a high degree of persistence in both regimes and regime 2 being relatively more persistent and longer lasting.

MS-ARDL causality results

In the next section, MS-ARDL-based causality analysis is reported. The determination of the direction of causality under regime dependency plays a crucial role in policy suggestions. In the context of the methodology presented in the “ Econometric methodology ” section, once the MS-ARDL model is extended to the MS-VARDL model, regime-specific Granger noncausality test results are calculated and reported in Table 9 . For comparative purposes, linear noncausality test results are reported in the last column.

The regime-specific causality results could be easily determined through the utilization of the methods followed in the paper. According to our results, the null hypothesis of Granger noncausality from cement production to CO 2 is rejected and the alternative is accepted in both regimes in addition to the linear model given in the last section. Hence, the results confirm nonlinear unidirectional causality from cement production to emissions in regime 1, the high-emission regime. This finding is in line with the linear causality test results. However, by investigating regime-specific causality results, our model provides bidirectional causality between cement production and CO 2 in regime 2. As a result, the method the paper provides led to additional insights given the feedback effects specific to regime 2. Hence, the feedback effect due to bidirectional causality is a phenomenon occurring specifically in regime 2, the low-emission regime, in contrast to the unidirectional causal effect from cement production to emissions in regime 1.

Given the fact that the nonlinear causality testing utilizes the MS-VARDL results, after the determination of the directions of causality, our method also allows the determination of the sign of the causal effect by evaluating the regime-specific parameter estimates. Footnote 17 The results are given in the last row of Table 9 . At the 5% significance level, for the determined causal effects in both regimes, the signs of parameters are positive confirming the positive effects of cement production on emissions in all specifications in addition to the positive effect of emissions on the acceleration of cement production in regime 2.

The findings of this paper indicate that reducing CO 2 emissions is contingent upon curtailing cement production, as it is the primary source of CO 2 emissions and this result is obtained in both regimes. Consequently, though asymmetry between the effects of cement production on CO 2 emissions exists, this asymmetry is mainly in terms of the magnitude, but not in terms of the sign of the effect. In addition, nonlinear causality results provided important deviations from the traditional causality results obtained with linear Granger causality techniques. If the findings are evaluated as a whole, the MS-ARDL results are led to the same results as the regime-dependent causality results and confirm these results. Traditional causality results are in line with the causality results in regime 1. Conversely, the causality results in changes in regime 2. Thus, the policy suggestions should be determined independently for regimes 1 and 2, the deep recession and crisis regime, and the expansionary regime, respectively.

Following the estimation results above, interesting results are obtained once the linear ARDL and nonlinear MS-ARDL are considered. The general finding suggests that regime dependency and nonlinearity should be taken into consideration for policy suggestions focusing on the negative effects of the cement industry on environmentally hazardous greenhouse gases. Depending on the regime, such negative effects accelerate and policymakers should consider the regime the economy and the industry are in since the CO 2 emitting effect of the industry depends on the regime type. Utilizing the outcomes of MS-VARDL analysis for nonlinear causality assessment, our approach not only establishes the causal directions but also enables the determination of the causal effect’s polarity through an examination of regime-specific parameter estimations. These outcomes are presented in the final row of Table 9 . At a significance level of 5%, the parameter signs for established causal effects in both regimes are positive, affirming the beneficial impact of cement production on emissions across all specifications. Furthermore, a positive influence of emissions on the acceleration of cement production is confirmed in regime 2. When considering the entirety of the findings, the MS-ARDL results align with regime-dependent causality outcomes, thereby reinforcing these conclusions. Traditional causality results are consistent with the causality outcomes observed in regime 1. In contrast, the causality directions diverge in regime 2. Accordingly, causality results affirm nonlinear unidirectional causality from cement production to emissions within regime 1. In terms of unidirectional links, the findings align with the findings of the linear causality tests for regime 1 only. The nonlinear method reveals bidirectional causality between cement production and CO 2 emissions in regime 2, in contrast to the unidirectional causality in regime 1. As a result, the method presented in this paper yields supplementary insights by uncovering feedback effects specific to regime 2, and the regimes that the industry is at gives vital information since the feedback effect could lead to a circular effect resulting in a cycle of more emissions. Hence, the phenomenon of feedback effects stemming from bidirectional causality manifests uniquely within regime 2, in contrast to the unidirectional causal effect from cement production to emissions observed in regime 1, and policies should aim at avoiding the negative implications on the environment by aiming at the alteration of the type of industrial production techniques with newer technologies on cement production.

Greenhouse gases and the global warming

When the literature was analyzed, vast amounts of environmental pollutants, encompassing SO 2 , NO x , CO, and PM, were discharged during cement production (Lei et al. 2011 ). The production of cement involves the high-temperature calcination of carbonate minerals, resulting in clinker formation and the release of CO 2 into the atmosphere (Xi et al. 2016 ). CO 2 emissions from cement production stem from two primary sources. Firstly, a chemical reaction occurs during the production of the central cement component. The cement generates oxides (lime, CaO), and CO 2 is emitted due to heat effects. These “process” emissions contribute to approximately 5% of total anthropogenic CO 2 emissions, excluding land-use changes (Boden et al. 1999 ). Secondly, nonrenewable energy combustion is used to heat raw materials to temperatures exceeding 1000 °C (IEA 2022b ). Around 90% of worldwide CO 2 emissions from industrial processes result from cement-related activities (M. E. Bildirici 2019 , 2020 ) and the cement industry’s combined emissions account for approximately 8% of the global CO 2 output (Andrew 2018 ; Le Quéré et al. 2018 ).

Overall, a variety of gases, called greenhouse gases (GHG), contribute to the greenhouse effect and global warming. The sizable portion of GHG emissions is dominated by CO 2 emissions (US EPA 2023 ). US Environmental Protection Agency reports that the shares of GHGs are CO 2 at 79.4%, methane (CH4) at 11.5%, nitrous oxide at 6.2%, and the remaining GHGs are fluorinated gases with a total contribution of 3% to the GHG effect (US EPA 2023 ). As the main driver of climate change due to the GHG effect, the recent positive trend of CO 2 in the last century is considered a result of human activity due to the burning of fossil fuels (coal, oil, and natural gas), deforestation, and industrial processes, and about more than 65% reduction of current CO 2 releases is needed to achieve environmental goals (Belbute & Pereira 2020 ). Therefore, CO 2 is among the top contributors to global climate change and the reversal necessitates great political commitment.

Historical relations among cement production, cement-induced CO 2 emissions, and business cycles

As confirmed by our empirical findings, cement production and CO 2 emissions are interrelated. Cement production is a derived demand of construction investments accelerating as economic growth accelerates. It is shown that the cement and construction industry and economic growth relation have important linkages (M. E. Bildirici 2019 ). Economic production is known to follow fluctuations known as business cycles, which include expansionary and recessionary periods. Throughout history, other factors that led to abrupt changes in the production cycle include economic crises, the Great Depression in 1929, the 1973 Oil Crisis, and World Wars (WWI and II). Not only do economic cycles have strong ties with cement as an important ingredient for construction, but also cement industry is also expected to follow a similar pattern possessing expansionary and contractionary episodes in line with the business cycle. Therefore, business cycles also create a derived cement demand during expansions, after crises and recessions. Hence, acceleration in cement production following recessions and even during recessions due to policies encouraging economic growth to overcome such downturns. Such cyclical behaviors in economic business cycles are nonlinear and due to their relation with economic activity, cement production also follows cycles and nonlinearity. Further, cement production is an important emitter of CO 2 . The overlook suggests that the production process of cement and the demand for energy that cement production necessitates are among the top channels that lead to environmental degradation. Various factors with relations to cement production include urbanization and the land-use change (Mishra et al. 2022 ; Zhou et al. 2021 ), which contribute to CO 2 emissions.

Yearly cement production (solid line) and CO 2 emissions from cement production (dashed line) are depicted in Fig. 1 for the 1900–2021 period. The figure also included the recession dates (as a grey bar) obtained from the National Bureau of Economic Recessions (NBER). As seen in Fig. 1 , the fluctuations in CO 2 from cement and cement production are closely linked and a positive association exists between the two series. The inclines and declines in both coincide in terms of occurrence and year and terms of the length of duration in the majority of cases. The recessions in economic business cycles lead to declined economic production, coupled with both declines in CO 2 emissions from cement and cement production in the USA. This relation becomes clearer, especially during the 1929 Great Depression and 2008 Great Recession with sharp declines in both series. The declines also coincide with the NBER dating of recessions. Another example is the decline in CO 2 emissions and cement production during the first year of COVID-19 in 2020, which was reversed afterward during the economic expansion that followed in 2021. Hence, the fluctuations in cement production are argued to be in line with economic business cycles, consisting of expansionary and recessionary episodes, leading to similar cycles in cement production and cement-based CO 2 emissions.

Source : U.S. Geological Survey and NBER. Note that cement production (right-hand side) is in billion metric tons. CO 2 emission from cement is for tons of CO 2 from 1 ton of cement produced and is per capita

NBER recessions, cement production, and CO 2 emissions from cement production in the USA, 1900–2021.

However, there are exceptions such as disputes and wars. During these periods, cement production could increase leading to increased CO 2 . Historical experience shows that during and after periods of conflicts, wars, and economic crises, economic construction investments accelerate. Overlook is construction and cement are cyclical and subject to nonlinearity and so are the emissions. Another point is that expansions are relatively longer lasting compared to recessions. Such economic growth periods lead to inclined cement production and CO 2 emissions. It is convenient to accept that economic recessions and crises are periods during which the policymakers aim to encourage the economy with expansionary economic policies. During such policies, construction and cement production is an important sector to achieve economic growth. Last but not least, depending on the stage of the economy, the long-run relationship between cement consumption and CO 2 emissions is also bound by the state of the economy.

History also shows that wars generate demand for construction during the process and the reconstruction period afterward. The statistics for the last century confirm that global cement production has amplified more than 30 times when economic growth accelerated in the 1950s following World War II (WWII) and the demand for cement production fast-tracked due to urban reconstruction of the after-war Europe and participant countries of WWII (Diefendorf 1989 ). Further, during the expansionary period after the 1980s and 1990s, cement consumption achieved a second period of upward trend, an increase of cement production nearly 4 times in the post-1980s and 1990s, and yearly cement production reached 0.5 tons per person in the world in mid-2010 (Andrew 2018 ). The post-1980s period corresponds to trade liberalization and globalization policies in the world. Along with post-conflict periods, cement production accelerates after economic recessions and after economic crises. The prominent crises include the Great Depression in 1929, the oil crises and their aftereffects after 1973, the exchange rate mechanism (ERM) in the late 2000s, the Southeast Asia crisis in the mid-1990s, and the Great Recession in 2008, which followed economic policies aimed at acceleration of economic growth and CO 2 (M. Bildirici & Ersin 2018b ). More recently, following the COVID-19 pandemic, nations also applied economic growth policies economic sudden-stop in early 2020. Following lock-downs the end of 2020 and year 2021 experienced high inclines in greenhouse gases and the recent economic recovery from COVID-19 in 2021 is a “carbon-intensive recovery” with a more than 1200-Mt increase in CO 2 releases in a single year (M. Bildirici & Ersin 2018b ). This incline in emissions has been drastically more than those observed during the recovery periods following the 2009 financial crisis (M. Bildirici & Ersin 2018b ). As a result, the econometric models focusing on cement and CO 2 emissions require the integration of nonlinear dynamics taken expansionary and recessionary regimes in the long-run relations.

As shown in the empirical section, the cement-induced CO 2 emission fluctuations are in close synchronization with economic business cycles. Furthermore, the cement industry’s CO 2 emissions are closely related to the source of energy. Figure 2 depicts the CO 2 emissions resulting from cement and other industries in addition to the CO 2 emissions from different sources of energy, specifically focusing on the fossil-fuel energy types including oil, coal, gas, and flaring in the USA for the 1920–2022 period. The overlook suggests an upward trend of CO 2 emissions from cement historically, similar to the upward trend followed by CO 2 emissions from different energy sources. For the whole period, oil is a major emitter with a nonreversing upward trend. After WWII, both oil- and coal-based CO 2 emissions continued to incline similar to cement-induced CO 2 emissions. While the pace of oil- and coal-based CO 2 slowed down, especially for coal after the 2000s, gas and flaring became the major emitters of CO 2 as the economic development and the necessary energy inclined. The slowing pace of the upward trend of oil- and coal-based CO 2 is coupled with cement-induced CO 2 . Among all CO 2 sources, Fig. 2 also depicts the sharp fluctuations in the 1929 Great Depression, 1973 Oil Crisis, and 2008–2009 Global Crisis.

Source : Global Carbon Budget

CO 2 emissions in the USA resulting from cement and other industries and CO 2 emissions produced by different energy sources, 1920–2022.

Figure 3 aims to provide a focused look at the comparison between total territorial CO 2 emissions and cement-induced CO 2 emissions in the USA. The overlook highlights the close ties between the CO 2 emissions of the economy and the CO 2 emissions from the cement industry. The total territorial CO 2 emissions (in orange, values on the left axis) followed an upward trend towards the 1970s, which is closely followed by the cement-induced CO 2 emissions. The period ended with the transformation of industrial production by reducing the dependence on oil, which occurred following the 1973 Oil Crisis in the USA. The total CO 2 emissions gained back its rally in the early 1980s for almost 3 decades without a significant interruption, except for minor slow-downs during economic recessions, which are quite negligible. The same pattern is followed by the cement-induced CO 2 . The rally of CO 2 emissions ended after the economic boom in 2007 and after the 2009 crisis after which the USA started to adopt policies to control the enormous level of CO 2 emissions. Though the climb is reversed, the amounts should be carefully addressed. The CO 2 emissions in 2022 were 5000 Mt a year, equal to the amount in the early 1970s when the economy was highly oil-dependent in production and energy. For cement-induced CO 2 emissions, the level reached is far worse; it is close to 45 Mt of CO 2 , far above the levels in the 1970s. CO 2 emissions from cement production are given with the black line in Mt CO 2 .

CO 2 emissions (given in orange, values on the left axis) in the territorial USA, and CO 2 emissions specifically from cement production (black, right axis), in Mt CO 2 , 1960–2022.

The data in Fig. 3 focuses specifically on the CO 2 produced by the production processes and avoids the energy consumption in the industry. Combined with it, the total effect depends on the type of energy consumed, and the relative dependence on nonrenewable energy sources is quite high in the USA. The overall result is that the fluctuations in total CO 2 emissions in the USA and the fluctuations in the cement-induced CO 2 emissions from the cement industry are highly synchronized, with a strong positive correlation (rho = 0.76). The fluctuations in cement-induced CO 2 are more pronounced; they closely capture the recessions and crises with significant volatility. Years 1974 and 1981 as well as 2008 denote the most drastic drops in CO 2 from cement production after a significant reduction in economic production, corresponding to deep economic crises and dispute periods; however, it gained its pace back again in emitting. The examination of Figs. 2 and 3 together confirms the empirical findings of the study and the conclusions. The overall impact of cement production on CO 2 emissions is positive and is regime-dependent for its positive effect on worsening environmental pollution.

Cement production as a driver of economic growth

The overall results in this study also confirm the cement industry’s role in driving economic growth indirectly since the economic expansion periods coincide with expansionary periods in cement production. This study does not directly investigate cement production and economic growth relation empirically and for such treatment, readers are referred to M. E. Bildirici ( 2019 , 2020 ). However, our study points to the industry’s CO 2 emissions and their relation to cement production cycles characterized by expansionary and contractionary regimes. The findings have important implications. Given the environmental repercussions of the cement sector, our study determines that it is imperative to devise effective environmental remedies and the cement industry should be in the focus not only in terms of its connection to CO 2 and GHG effect but also in terms of its strong ties with economic growth and business cycles. Hence, the results confirm the necessity to invest in greening cement production technology and to increase energy efficiency in addition to production techniques in cement.

Policy recommendations

We provided a set of policy aspects in the previous section, which include improving energy efficiency, renewable energy investments, and taking feedback effects in control, especially in relatively higher cement production regimes. Therefore, policies should focus on the reduction of emissions in the sector more with new techniques in cement production, especially during such periods. An interconnected approach is needed that concentrates both on economic and green cement production aspects (Poudyal & Adhikari 2021 ). A potential reduction in cement production might correlate with slowed economic growth, which is an undesired option for economic policymakers. Therefore, though noting such effects, the reduction of emissions of cement through technology requires immediate action. For such technologies, a set of recent research suggests various methods to reduce the environmental effects of cement production. These include negative emission technologies and decarbonization of the industry (Ren et al. 2023 ), carbon capture and storage, and nanomaterials and supplementary materials to be used as cement complementary in cement production (Poudyal & Adhikari 2021 ). The effects of renewable energy and urbanization (Danish et al. 2020 ) as well as innovation focusing on reducing sectoral emissions in construction have been shown in the literature (Erdoğan et al. 2020 ). In the context of cement production, policies focusing on sustainable urbanization and clean energy could contribute to the reduction of the indirect emissions led by the cement industry.

For the cement industry, one of the remedies lies in transitioning towards renewable energy sources to replace fossil fuels for cement manufacturing and the high amounts of energy consumption during the production process. To facilitate this transition, policymakers should establish a subsidy program incentivizing companies to adopt renewable energy technologies. Another policy recommendation is to accelerate investments and research and development for energy efficiency in the sector. In a country-wide analysis, energy efficiency surges are shown to have stronger positive effects on the environment compared to renewables. However, it should be kept in mind that there are different forms of renewable energy sources with varying effects on the environment. Further, it is shown that the transition to renewable energy is costly and benefits could be achieved only in the long run (M. Bildirici & Ersin 2023 ). Policies should re-evaluate the thresholds to achieve and the timeline for net zero carbon transition. This requires steps to be taken to reduce the significant amount of CO 2 emissions of the industry faster than it is planned in the USA. Lastly, governments addressing environmental issues can achieve preventative health benefits by averting certain illnesses by reducing GHG emissions; the results indicate the need for focusing on the role of industries, and among these, the cement industry is the top third polluter in addition to its production techniques that require a significant amount of energy. To reverse adverse health effects in addition to considering the ties of economic growth and cement production, the cement-induced CO 2 emissions led to a significant amount of CO 2 emissions, though some steps are taken in the production techniques of cement compared to the pre-1950s; however, more efforts should be made on production technologies. Such focus can contribute to the formulation of strategies for proactive public health measures, which should include the cement industry, its relation to environmental sustainability economic growth, and energy in the context of the environment-health nexus.

The cement production activities directly cause emissions during the cement production activities and the size of emissions of such direct emissions deserve special attention for achieving sustainable environment and economic development. The investigation of the long-run effects of cement production is crucial for global warming and climate change. Given the nonlinear nature of the CO 2 emissions and cement production datasets, the paper aimed at providing a hybrid approach that integrated the Markov-switching models to the ARDL-type cointegration methodology to achieve methods to provide tools to examine the nonlinear and regime-dependent long-run and short-run effects in addition to nonlinear causality modeling. By utilizing a historically long period, covering the 1900–2021 sample, the cement production and its effects on greenhouse gas emissions were examined for the USA. The investigation of the relationship is of crucial importance for sustainability in economic development and the environment since the level of hazardous emissions in the cement production industry is among the top third polluters.

In this study, by employing the Markov-switching to the ARDL analysis, the regression space is divided into two distinct regimes. Regime 1 represents periods of deep recessions and crises, while regime 2 characterizes expansionary periods in the industry. The latter has historically lasted relatively longer in terms of duration. The empirical findings show that cement production has significant positive effects both in the short run and in the long run in both regimes. Further, the regime dating provided by the MS-ARDL model yields insightful findings. The years classified under the crisis regime closely align with severe economic recessions and crises as well as wards such as the 1973 Oil Crisis and World Wars I and II. Regime 2 is notable for encompassing events such as the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and more recently, the economic shutdown resulting from the COVID-19 pandemic. During such periods, especially after the crises, economic policies aim to revive the economic growth back to its track, and the cement industry, being closely tied to economic cycles, demonstrates resilience in short-lived crises. It is observed that cement production persists during shorter recessions, with production continuing and sector inventories being maintained unless the recession is severe and prolonged. Furthermore, before exiting recessionary periods, both cement production and associated CO 2 emissions from cement production revert to an increasing trend at a faster pace than the previous. Consequently, cement-induced CO 2 emissions do not decelerate at all in both regimes and emissions continue to escalate as long as cement production activities persist.

Following the generalization of the ARDL model to the novel MS-ARDL, the model is further extended to the MS-VARDL model that capture nonlinear causality relations and the directions of the causal links among the variables analyzed. The novel MS-VARDL method in this study incorporates regime-dependent causal relationships, testing regime-dependent causal directions, which are vital for the determination of the causality direction between cement production and CO 2 emissions for policy formation. Therefore, the nonlinear causality analyses developed in this study overcome inefficient causal relations in tests that ignore the regime-dependent and nonlinear dynamics in the analyzed series. Further, avoiding such aspects of data would result in incorrectly determined causality directions, leading to inefficient policy recommendations. Hence, the testing of causality with the novel method is of paramount importance, especially for the environment-related emissions dataset and the cement production series subject to nonlinearity in this study.

The regime-specific causality results from the MS-VARDL approach revealed positive and causal effects of cement production in both regimes though the magnitude varies depending on the regime. The findings align with the findings we obtained from the single-regime (i.e., linear) VAR-type causality analysis in terms of capturing the direction of causality in a general sense. However, the regime distinction made in causality testing revealed significant information over the traditional method. The magnitude of the causal effect is notably amplified in the crisis regime, while maintaining causality also in the expansion regime, but with a lower positive effect. Additionally, the methodology employed in the study identified bi-directional causal effects between cement production and CO 2 emissions, particularly evident in the second regime, suggesting feedback effects between emissions and cement production.

The determination of regime-specific causal effects provided important policy suggestions for the policymakers focusing on the cement industry and its environmental impacts. Accordingly, regime dependence on the causal links necessitates regime-specific policy measures and if the policymaker utilizes traditional approaches, the magnitude of the industry is estimated to be relatively lower than it is in reality. It should also be noted that the USA has a strong commitment to net-zero policies specifically designed for the cement industry towards lowering emissions to net-zero in 2050. However, the findings in our study highlight the underestimation of the severity of greenhouse gas emissions with traditional methods. Given this fact, policy recommendations of the paper underline the necessity of stronger measures towards net-zero policies, state-level government subsidies to achieve greater commitment to renewable energies share in total energy, subsidies to direct investments in the carbon-capture industry, subsidies to the cement industry to achieve pace in energy efficiency, and giving incentives to internalize emission externalities by the industry. Further, as listed in the policy recommendation section, a set of technological improvements are needed to revise the ongoing GHG effects of the cement industry to achieve a sustainable environment and sustainable economic development. Increasing the speed of energy transition towards renewable energy, and reduction of energy consumption with more energy-efficient solutions in the sector, coupled with net-zero policies and technological investments for supplementary material use in the cement industry are among the measures to be taken.

The feedback relation that produces cycles of GHG emissions, especially in the second regime deserves attention. The strong positive ties with economic cycles and industrial production of the industry generate significant amounts of CO 2 emissions, especially during such periods. The reduction of economic growth is one option but it is not a desired one. Conversely, to cover environmental costs, investments in various emission reduction technologies such as carbon capture and storage are among the viable options that should be taken into focus by policymakers.

The study has limitations due to the availability of data. The nonlinear method utilized in this study requires a long span of data and a large sample size; hence, the study’s empirical focus is restricted to the USA and the 1900–2021 period. As a result, China and India were not examined. For future studies, we suggest extending the analysis to a larger set of countries. Another suggestion is to examine the nexus with a panel of countries.

Data availability

Data used in this study are publicly available from the quoted databases reported under the data subheading given in the empirical section. Data are also available upon request from the corresponding author.

Bildirici and Ersin ( 2023 ) investigate a set of countries who engage on Industry 4.0 with highest shares of Industry 4.0 innovations in addition to large shares of international trade and the share of fossil-fuel energy use in these countries are interestingly high, 82% in the USA, 87.67% in China, 93.03% in Japan, 78% in Germany, 74% in Canada, and 80% in UK. Lowest practice is achieved by France with 46% of fossil-fuel energy use in total.

China leads global cement production by a significant margin, estimated at 2.1 billion metric tons in 2022, representing over half of the world’s total cement output. India follows as the second-largest cement producer globally, with production totaling 370 million metric tons in 2022. Vietnam ranked third in global cement production for that year, producing 120 million metric tons (source: https://www.statista.com/statistics/1087115/global-cement-production-volume/ ).

Such innovations will provide important insights to reduce the emission effects of cement production. However, due to the focus of the study, this literature is kept out of focus in the study.

The USA, which produces cement throughout 37 states, is the third-largest cement manufacturer in the world as of 2000. In addition to being a major source of industrial process-related emissions in the USA, cement production also contributes significantly to CO 2 emissions not only from processing, but also from combustion. The process necessitates high temperatures which is achieved largely with 12% fuel burning, 72% with coal energy, and the rest with various sources including natural gas, coke, and oil (Hanle et al. 2004 ).

The empirical findings revealed the nonlinear association between cement production, air pollution, economic growth, and mortality rate with Bayesian MS-VAR and MS-Bayesian Granger causality tests for the USA, Turkey, India, Brazil, and China and findings confirmed cement production as Granger causes not only air pollution and economic growth, but also mortality rates in all regimes for all countries analyzed (M. E. Bildirici 2020 ).

Following these critiques regarding the efficiency of the test, McNown et al. ( 2018 ) proposed a bootstrapped ARDL (BARDL) test as an accompanying test to the F PSS test and the bound critical values of Pesaran et al. ( 2001 ). In the proposed BARDL method (McNown et al. 2018 ), the utilization of three tests are suggested as compulsory to determine the presence of no-cointegration, cointegration, and degenerate cases. In the state of degenerate-2, the supplied bound critical values of Pesaran et al. ( 2001 ) are inefficient and under degenerate case-1, the method is effective.

The STARDL method relaxes the form of nonlinearity by allowing nonlinearity both in the short-run and long-run dynamics and derive the relevant tests. For the STARDL modeling and relevant nonlinear cointegration testing, readers are referred to M. Bildirici and Ersin (2018b). The model generalizes STAR models of Granger and Teräsvirta ( 1993 ) and Luukkonen et al. ( 1988 ) to nonlinear ARDL.

Further, STAR type nonlinear models allow capturing different speed of transition between regimes, both smooth and abrupt, However, MS models and threshold models are efficient under abrupt and sudden regime shifts.

The MS-VEC model is applied to environmental degradation, hydropower, and nuclear energies for their asymmetric long- and short-run relations (Bildirici 2020 ).

Note that the representation above is a theoretical representation and Davies type linearity tests to determine the number of regimes generally selects 2 regimes and for selected set of applications, 3 regimes.

These techniques, in contrast to conventional vector autoregressive (VAR) and vector error correction (VEC) models, also made it possible to evaluate the model without using dummy variables and to examine various stages of the economy.

The model presented is a Markov-Switching Intercept and Heteroskedasticity (MSIH-VEC), no regime switching allowed in the long-run and short-run parameters. However, due to the regime switching being introduced to the drift term, the filtered residual is already affected from the changes in drift.

The pioneering papers that provide a historical perspective (Grossman & Krueger 1991 ; Stern et al. 1996 ) are generalized to nonlinearity with a set of studies. Studies suggest a coverage of centuries long data to examine nonlinear relations among and cycles of emission and GDP growth rates historically (M. Bildirici & Ersin 2018b ; Ersin 2016 ).

1954 and 1961 have 10 months lasted recessions; 1958, 1991, and 2001 recessions lasted 8 months; 1970 recession lasted 11 months. After 1970, 1974 recession lasted 16 months, relatively a long-lasting recession which followed the oil crisis. A deep recession occurred between 1975 and mid 1976. In early 1980s, policies included trade liberalization policies in the world which had implications as a short recession that did not affect the cement industry. A difference in the table is 1982–1983 recession which lasted 16 months. However, the model did not capture a contraction in the period. If data investigated, we noted no drastic decline in the cement production for this period, providing a special case.

In addition, following the proposal given in the “Methodology” section, we calculated F MS-ARDL statistics for single regimes only. Given that the model has 2 variables and 2 states, after estimating an MS(2)-VARDL(2) model, 4 hypotheses are tested by applying zero restrictions. At 5% significance level, results suggest that if emission variable is the dependent variable, cointegration cannot be rejected for regime 1 and regime 2 in addition to nonlinear cointegration in both regimes. In contrast, if cement production is taken as the dependent variable, there exists no cointegration in regime 1, inconclusive result in regime 2, and no cointegration in both regimes, i.e., no nonlinear cointegration.

A model with 3 regimes is also estimated; however, the estimation led to inconsistent results. Though the sample covers a long period of 1900–2021, the dataset is yearly and sample size restricts estimation of nonlinear models with more than 2 regimes. Therefore, two regimes are assumed.

To save space, the MS-VARDL estimation results are reported. They are available upon request. In the determination of signs of causality, the statistical significance of the parameters, sign, and size of parameter estimates are crucial.

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Bildirici, M.E., Ersin, Ö.Ö. Cement production and CO 2 emission cycles in the USA: evidence from MS-ARDL and MS-VARDL causality methods with century-long data. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33489-2

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Research Classification According to the Research Methods used. Depending upon the research method used for the investigation, it can be classified as: a) Survey research, b) Observation research, c) Case research, d) Experimental research, e) Historical research, f) Comparative research. Research Classification According to the Time Frame
What Is Qualitative Research?
Qualitative research methods. Each of the research approaches involve using one or more data collection methods.These are some of the most common qualitative methods: Observations: recording what you have seen, heard, or encountered in detailed field notes. Interviews: personally asking people questions in one-on-one conversations. Focus groups: asking questions and generating discussion among ...
(DOC) RESEARCH METHODS NOTES
research methods notes topic one: Introduction and background Introduction Research is the cornerstone of any science, including both the hard sciences such as chemistry and physics and the social (or soft) sciences such as psychology, management, or education. It refers to the organized, structured, and purposeful investigation aimed at ...
A Guide to Field Notes for Qualitative Research: Context and
The majority of qualitative research methods encourage researchers to take field notes to enhance data and provide rich context for ... a literature search in August 2016 to investigate methods of field note collection using keywords qualitative and field note(s)/research/methods in the Google Scholar, The Cumulative Index to Nursing and Allied ...
PDF Unit: 01 Research: Meaning, Types, Scope and Significance
Research may have certain other qualities such as: a) It is a prearranged / structured enquiry (a formal step by step method or sequence to take up research activity is developed to ensure correctness of data and validity of processes). Scientific methods consist of systematic observation, classification and interpretation of data.
Research Methodology Notes
The research methodology is a discipline that is involved in studies related to a variety of research techniques like case studies, surveys, and provides an overview of how to perform particular research. It also provides fundamental knowledge required to make rational decisions about research strategies. The discipline also explores ethical constraints, codes of ethics, and requirements for ...
Introduction to Research Methods Notes
Kamau,J(2006) Methods of Research OR. Kamau,J(2006) Methods of Research(3 rd ed.)Longhorn Publications Nairobi. Capture Appendices (Any detail that reinforces the body of the proposal and project can be included in an appendix) Time schedule; Budget; Data collection instruments and any other document that the researcher may consider important ...
Essay Quantitative Research Methodology Template
4 Experimental Research Experimental research serves as a means of studying the impact of an intervention or treatment on a particular group and measuring the outcomes. This method typically involves randomly assigning participants to control and experimental groups to establish causality. Furthermore, this method is "a class of research methodologies that involve variable manipulation ...
Cement production and CO2 emission cycles in the USA ...
The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO2 emission cycles in the USA for a historically long dataset covering 1900-2021. ... we note that the cycles reported by NBER and the MS-ARDL estimations are close matches. The WWII period is taken into consideration as ...
An approximated exponentially tilted empirical likelihood estimator of
15. For ET and EL, the outer loop uses the derivative derived from the envelope theorem. All estimates are computed with the Matlab function "minFunc" written by Schmidt (Citation 2005), which is for unconstrained optimization using line-search methods and requires fewer function evaluations to converge than the "fminunc" function in Matlab on many problems.