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THE RESEARCH PROCESS
RESEARCH PROCESS
The research process is a very generalised model of carrying out research.
The research process involves identifying, locating, assessing, and analysing the information you need to support your research question, and then developing and expressing your ideas.
According to Clifford Woody research comprises defining and redefining problems, formulating hypothesis or suggested solutions; collecting, organizing and evaluating data; making deductions and reaching conclusions; and at last carefully testing the conclusions to determine whether they fit the formulating hypothesis.
Regardless of the area of research or choice of methodology, the research process involves similar activities. The process is an expression of the basic scientific method using the following steps: statement of the problem, generating a hypothesis, review of relevant studies, creating measures, choosing the sample, collecting data, analysing data, and reporting results.
RESEARCH PROCESS PARADIGM
Paradigm definition:
- a model of something, or a very clear and typical example of something
- an outstandingly clear or typical example or archetype
- a philosophical and theoretical framework of a scientific school or discipline within which theories, laws, and generalizations and the experiments performed in support of them are formulated
- pattern; mirror; prototype
RESEARCH PARADIGM
A paradigm is a shared world view that represents the beliefs and values in a discipline and that guides how problems are solved (Schwandt, 2001).
In educational research the term paradigm is used to describe a researcher’s ‘worldview’ (Mackenzie & Knipe, 2006). This worldview is the perspective, or thinking, or school of thought, or set of shared beliefs, that informs the meaning or interpretation of research data.
Or, as Lather (1986) explains, a research paradigm inherently reflects the researcher’s beliefs about the world that s/he lives in and wants to live in. It constitutes the abstract beliefs and principles that shape how a researcher sees the world, and how s/he interprets and acts within that world.
ELEMENTS OF A RESEARCH PARADIGM
According to Lincoln and Guba (1985), a paradigm comprises four elements, namely, epistemology , ontology , methodology and axiology.
EPISTEMOLOGY – ways of knowing (how do know what we know)
It is concerned with the very bases of knowledge – its nature, and forms and how it can be acquired, and how it can be communicated to other human beings. It focuses on the nature of human knowledge and comprehension that you, as the researcher or knower, can possibly acquire so as to be able to extend, broaden and deepen understanding in your field of research. The researchers can draw from four sources of knowledge. Those sources are intuitive knowledge, authoritative knowledge, logical knowledge, and empirical knowledge.
ONTOLOGY – philosophical assumptions about the nature of social reality (what do we believe about the nature of reality)
It is concerned with the assumptions we make in order to believe that something makes sense or is real, or the very nature or essence of the social phenomenon we are investigating. It helps you to conceptualise the form and nature of reality and what you believe can be known about that reality. Philosophical assumptions about the nature of reality are crucial to understanding how you make meaning of the data you gather. These assumptions, concepts or propositions help to orientate your thinking about the research problem, its significance, and how you might approach it so as to contribute to its solution.
METHODOLOGY – asking certain questions and using appropriate approaches to systematic inquiry (how should we study the world)
Reporters: GHELVER A. VENTURA Course Name: Research Methods in Public Administration JAMES RYAN P. ESCUSA Master in Public Administration
Methodology is the broad term used to refer to the research design, methods, approaches and procedures used in an investigation that is well planned to find out something (Keeves, 1997). The methodology articulates the logic and flow of the systematic processes followed in conducting a research project, so as to gain knowledge about a research problem. It includes assumptions made, limitations encountered and how they were mitigated or minimised. It focuses on how we come to know the world or gain knowledge about part of it (Moreno, 1947).
AXIOLOGY – ethics and value systems (what do we believe is true)
Axiology refers to the ethical issues that need to be considered when planning a research proposal. It considers the philosophical approach to making decisions of value or the right decisions (Finnis, 1980). It involves defining, evaluating and understanding concepts of right and wrong behaviour relating to the research. It considers what value we shall attribute to the different aspects of our research, the participants, the data and the audience to which we shall report the results of our research. Implementation of ethical considerations focuses on four principles which you need to uphold when dealing with your participants and data. These principles have the acronym PAPA namely: Privacy, Accuracy, Property, and Accessibility.
- Privacy - Under this principle, you need to consider what information participants will be required to reveal to you or to others about themselves, their associations or organisations. It considers the conditions and safeguards under which data will be gathered and analysed.
- Accuracy - This principle considers who is responsible for the authenticity, fidelity, and, accuracy of information. Similarly, it considers how you as the researcher will cross- check with participants so they know you have recorded the data accurately. It also makes it very clear who will be held accountable for any errors in data.
- Property - Under this principle, you need to consider who will own the data. Will there be any payment for the data? If so, what will be the just and fair prices, for the exchange of data. Who will own the channels, such as publications and media through which information will be disseminated.
- Accessibility - This principle considers who will have access to the data. How will the data be kept safe and secure? Under what conditions and with what safeguards will researchers and participants have access to the data. How will access to the data be gained.
TYPES OF RESEARCH PARADIGM
POSITIVIST / POSTPOSITIVIST PARADIGM - the Positivist paradigm defines a worldview to research, which is grounded in what is known in research methods as the scientific method of investigation. Post-positivism is influenced by a philosophy called critical realism (Trochim, 2002). It can be distinguished from positivism according to whether the focus is on theory verification (positivism) or on theory falsification (postpositivism) (Ponterotto, 2005).
CONSTRUCTIVIST / INTERPRETATIVE PARADIGM (ANTI-POSITIVIST) - Constructivism and interpretativism are related concepts that address understanding the world as others experience it. The central endeavour of the Interpretivist paradigm is to understand the subjective world of human experience. This approach makes an effort to ‘get into the head of the subjects being studied’ so to speak, and to understand and interpret what the subject is thinking or the meaning s/he is making of the context.
CRITICAL / TRANSFORMATIVE / EMANCIPATORY PARADIGM - The Critical paradigm situates its research in social justice issues and seeks to address the political, social and economic issues, which lead to social oppression, conflict, struggle, and power structures at whatever levels these might occur. Because it seeks to change the politics so as to confront social oppression and improve the social justice in the situation, it is sometimes called the Transformative paradigm. Other theories within this paradigm include critical theory, feminist theories, Freirian theory, race-specific theories and post-colonial theories.
POSTCOLONIAL / INDIGENOUS RESEARCH PARADIGM - a world view that focuses on the shared aspects of ontology, epistemology, axiology and research methodologies of disempowered or historically oppressed social groups. The postcolonial indigenous paradigm has blossomed in recent years as a means for hearing non-Western voices and emancipating the voices of formerly oppressed generations from silence imposed by colonization. It provides a means for valuing indigenous knowledge systems and philosophies.
PRAGMATIC PARADIGM (MIXED METHODS) - Adoption of a worldview that allows for a research design and methodologies that are best suited to the purpose of the study. It seeks to utilise the best approaches to gaining knowledge using every methodology that helps that knowledge discovery. The
environment, the cosmos, the living and the non-living Place of
values in the
research
process
Science is value free, and values have no place except when choosing a topic
Values are an integral part of social life; no group’s values are wrong, only different
All science must begin with a value position; some positions are right, some are wrong.
All research must be guided by a relational accountability that promotes respectful representation, reciprocity and rights of the researched Nature of
knowledge
Objective Subjective; idiographic
Dialectical understanding aimed at critical praxis
Knowledge is relational and is all the indigenous knowledge systems built on relations What counts
as truth
Based on precise observation and measurement that is verifiable
Truth is context dependent
It is informed by a theory that unveils illusions
It is informed by the set of multiple relations that one has with the universe
Methodology Quantitative;
correlational; quasiexperimental; experimental; causal comparative; survey
Qualitative; phenomenology; ethnographic; symbolic interaction; naturalistic
Combination of quantitative and qualitative action research; participatory research
Participatory, liberating, and transformative research approaches and methodologies that draw from indigenous knowledge systems Techniques of
gathering
data
Mainly questionnaires, observations, tests and experiments
Mainly interviews, participant observation, pictures, photographs, diaries and documents
A combination of techniques in the other two paradigms
Techniques based on philosophic sagacity, ethno philosophy, language frameworks, indigenous knowledge systems and talk stories and talk circles
Selection of Research Paradigms and Research Methods
Research Paradigms
Research Approach Research Methods Examples
Positivist / Postpositivist
Quantitative -Surveys: longitudinal, cross-sectional, correlational -Experimental and Quasi-experimental -Ex-post facto research
- Attitude of distance learners towards online based education
- Relationship between students’ motivation and their academic achievement.
- Effect of intelligence on the academic performances of primary school learners Constructive / Interpretative
Qualitative -Biographical -Phenomenological -Ethnographical -case study
- A study of autobiography of a great statesman.
- A study of dropout among the female students
- A case study of an open distance learning Institution in a country. Critical / Transformative
Critical and action- oriented
-Ideology critique -action research
- A study of development of education during the British rule in India
- Absenteeism among standard five students of a primary school
THE RESEARCH PROCESS PARADIGM:
- Problem Formulation - The first step in the Research Process is defined or redefining the study will be based. The research problem may be something the agency identifies as a problem, some knowledge or information that is needed by the agency.
- Exploration - Now that the problem has been defined, the researcher must learn more about the topic under investigation. To do this, the researcher must review the literature related to the research problem. This step provides foundational knowledge about the problem area. The review of literature also educates the researcher about what studies have been conducted in the past, how these studies were conducted, and the conclusions in the problem area.
- Designing the Instrument – The researcher prepares a measurement tool/device (survey, test, questionnaire or scales, etc.) designed to obtain data on a topic of interest from research subjects.
- Designing the Study - The arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure. The design includes an outline of what the researcher will do from writing the hypothesis and its operational implications to the final analysis of data.
- Sampling Resource and Budget Allocation - A sample can be defined as a small piece of group. The researcher must decide the way of selecting a sample which is known as SAMPLE DESIGN. Sample can be of various types such as:- i) Simple/Random Sampling ii) Systematic Sampling iii) Quota Sampling. The researcher also creates a simple budget plan for the research project expenses or expenses associated with the research.
Problem Formulation
Designing theExploration Instrument
Designing the Study
Sampling Resource and Budget Allocation
Research Proposal Preparation
Pilot Testing and Validation
Data CollectionAnalysis and Interpretation
Research Reporting and Publication
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- The key variables are the terms to be operationally defined if an Operational Definition of Terms section is necessary.
- The key variables provide focus to the Methods section.
- The Instrument will measure the key variables. These key variables must be directly measured or manipulated for the research study to be valid.
Variables represent the measurable traits that can change over the course of a scientific experiment. The key to designing any experiment is to look at what research variables could affect the outcome. In all there are six basic variable types: dependent, independent, intervening, moderator, controlled and extraneous variables.
DEPENDENT AND INDEPENDENT VARIABLE - The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design. A researcher must determine which variable needs to be manipulated to generate quantifiable results. In general, experiments purposefully change one variable, which is the independent variable. But a variable that changes in direct response to the independent variable is the dependent variable. Say there’s an experiment to test whether changing the position of an ice cube affects its ability to melt. The change in an ice cube's position represents the independent variable. The result of whether the ice cube melts or not is the dependent variable.
INTERVENING AND MODERATOR VARIABLE - Intervening variables link the independent and dependent variables, but as abstract processes, they are not directly observable during the experiment. For example, if studying the use of a specific teaching technique for its effectiveness, the technique represents the independent variable, while the completion of the technique's objectives by the study participants represents the dependent variable, while the actual processes used internally by the students to learn the subject matter represents the intervening variables. By modifying the effect of the intervening variables (the unseen processes) moderator variables influence the relationship between the independent and dependent variables. Researchers measure moderator variables and take them into consideration during the experiment.
EXTRANEOUS VARIABLE - A well-designed experiment eliminates as many unmeasured extraneous variables as possible. This makes it easier to observe the relationship between the independent and dependent variables. These extraneous variables, also known as unforeseen factors, can affect the interpretation of experimental results. Lurking variables, as a subset of extraneous variables represent the unforeseen factors in the experiment. Another type of lurking variable includes the confounding variable, which can render the results of the experiment useless or invalid. Sometimes a confounding variable could be a variable not previously considered. Not being aware of the confounding variable’s influence skews the experimental results. For example, say the surface chosen to conduct the ice-cube experiment was on a salted road, but the experimenters did not realize the salt was there and sprinkled unevenly, causing some ice cubes to melt faster. Because the salt affected the experiment's results, it's both a lurking variable and a confounding variable.
CONTROLLED VARIABLES - Language learning and teaching are very complex processes. It is not possible to consider every variable in a single study. Therefore, the variables that are not measured in a particular study must be held constant, neutralized/balanced, or eliminated, so they will not have a biasing effect on the other variables. Variables that have been controlled in this way are called controlled variables.
After the key variables have been identified, the researcher needs to identify how those variables will be studied, which is the heart of the Research Design. There are four primary research designs:
Descriptive : Describes the current state of variables.
Causal Comparative : Examines the effect of one variable that cannot be manipulated on other variables.
Correlational : Describes the relationship between variables. Correlational studies must examine two variables that have continuous values.
Experimental and Quasi-Experimental : Examines the effect of a variable that the researcher manipulates on other variables.
Once the key variables and the research design have been identified, the rest of the study falls into place.
- The purpose, research questions, and hypotheses will be written about the variables based on the research design.
- The Instruments will be developed to measure the key variables and is written to describe the instruments.
- The Procedures section describes the treatment for experimental studies and/or how the instrument will be administered.
- The Method of Data Analysis describes how the data is summarized and tested based on the research questions and hypotheses.
Isolating Controlled Variables
Controlled variables are often referred to as constants, or constant variables.
A failure to isolate the controlled variables, in any experimental design, will seriously compromise the internal validity. This oversight may lead to confounding variables ruining the experiment, wasting time and resources, and damaging the researcher's reputation.
In any experimental design, a researcher will be manipulating one variable, the independent variable, and studying how that affects the dependent variables.
Most experimental designs measures only one or two variables at a time. Any other factor, which could potentially influence the results, must be correctly controlled. Its effect upon the results must be standardized, or eliminated, exerting the same influence upon the different sample groups.
It is important to ensure that all possible variables are isolated, because an error may occur if an unknown factor influences the dependent variable. This is where the null hypothesis is correctly rejected, but for the wrong reason.
In addition, inadequate monitoring of controlled variables is one of the most common causes of researchers wrongly assuming that a correlation leads to causality.
Controlled variables are the road to failure in an experimental design, if not identified and eliminated. Designing the experiment with controls in mind is often more crucial than determining the independent variable.
Poor controls can lead to confounding variables, and will damage the internal validity of the experiment. Ensuring that certain research variables are controlled increases the reliability and validity of the experiment, by ensuring that other causal effects are eliminated. This safeguard makes it easier for other researchers to repeat the experiment and comprehensively test the results.
Control Groups
Control groups are used to determine if the independent variable actually affects the dependent variable. The control group demonstrates what happens when the independent variable is not applied. The control group helps researchers balance the effects of being in an experiment with the effects of the independent variable. This helps to ensure that there are no random variables also influencing behavior. In an experiment monitoring productivity, for instance, it was hypothesized that additional lighting would increase productivity in factory workers. When workers were observed in additional lighting they were more productive, but only because they were being watched. If a control group was also observed with no additional lighting this effect would have been obvious.
Random Assignment
To minimize the chances that an unintended variable influences the results, subjects must be assigned randomly to different treatment groups. Random assignment is used to ensure that any preexisting differences among the subjects do not impact the experiment. By distributing differences randomly between the conditions, random assignment lowers the chances that factors like age, socioeconomic status, personality measures, and other individual variables will affect the overall group’s response to the independent variable. Theoretically, the baseline of both the experimental and control groups will be the same before the experiment starts. Therefore, if there is a difference in the behavior of the two groups at the end of the experiment, the only reason would be the treatment given to the experimental group. In this way, an experiment can prove a cause-and-effect connection between the independent and dependent variables.
HYPOTHESIS
A hypothesis has classical been referred to as an educated guess, a proposed solution to a problem.
A research hypothesis is the statement created by researchers when they speculate upon the outcome of a research or experiment.
It describes in concrete (rather than theoretical) terms what you expect will happen in your study.
- Be falsifiable – is it possible to prove or disprove the statement?
- Make a prediction or speculate on an outcome
- Be practicable – can you measure the variables in question?
- Hypothesize about a proposed relationship between two variables, or an intervention into this relationship
The important thing to remember about stating hypotheses is that to formulate the prediction (directional or not), and then to formulate a second hypothesis that is mutually exclusive of the first and incorporates all possible alternative outcomes for that case. When the study analysis is completed, the idea is that to choose between the two hypotheses. If the prediction was correct, then it would (usually) reject the null hypothesis and accept the alternative. If the original prediction was not supported in the data, then it will accept the null hypothesis and reject the alternative. The logic of hypothesis testing is based on these two basic principles:
- the formulation of two mutually exclusive hypothesis statements that, together, exhaust all possible outcomes
- the testing of these so that one is necessarily accepted and the other rejected
Stating a hypothesis is a convoluted, awkward and formalistic way to ask research questions. But it encompasses a long tradition in statistics called the hypothetical-deductive model. The hypothetical- deductive model or method is a proposed description of scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known.
Examples of Hypothesis:
- (^) If I replace the battery in my car, then my car will get better gas mileage.
- If I eat more vegetables, then I will lose weight faster.
- If I add fertilizer to my garden, then my plants will grow faster.
- If I brush my teeth every day, then I will not develop cavities.
- (^) If I take my vitamins every day, then I will not feel tired.
- If 50 mL of water are added to my plants each day and they grow, then adding 100 mL of water each day will make them grow even more.
