RESEARCH METHODOLOGY

 RESEARCH METHODOLOGY

What is research?

Research is a systematic and organized process of inquiry, investigation, and exploration undertaken to generate new knowledge, enhance understanding, solve problems, or answer specific questions.

It involves a structured approach to gathering, analyzing, and interpreting information or data in a way that contributes to the existing body of knowledge in a particular field or discipline.

Research can take many forms, including scientific experiments, surveys, case studies, literature reviews, and more, and it is conducted across a wide range of academic, scientific, business, and practical domains.

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

A)According to its Purpose

1.Theoretical Research: It is also referred to as pure or basic research, focuses on generating knowledge, regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question. Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

For example, a philosophical dissertation, since the aim is to generate new approaches from existing data without considering how its findings can be applied or implemented in practice.

2.Applied Research: Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

2.Technological applied research: looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.

3.Scientific applied research: has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

For example, market research, because by examining consumption patterns, strategies can be developed for the development of new products and marketing campaign

B)According to your Depth of Scope

1.Exploratory Research: It is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

For example, an investigation of the role social media in the perception of self-image.

2.Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

For example, investigating how the public census of influential government officials differs between urban and non-urban areas.

3.Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

For example, investigating the brittle behaviour of a specific material when under compressive load.

4.Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

C)According to the Type of Data Used

Qualitative Research

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

For example, examining the effects of sleep deprivation on mood.

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them. This allows generalised conclusions to be projected over time

D)According to the Degree of Manipulation of Variables

Experimental Research

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

For example, randomised controlled trial studies for measuring the effectiveness of new pharmaceutical drugs on human subjects.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

For example, a study on the effects of the use of certain chemical substances in a particular population group can be considered a non-experimental study.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations. This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

For example, assessing the effectiveness of an intervention measure in reducing the spread of antibiotic-resistant bacteria.

E)According to the Type of Inference

Deductive Investigation

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal Study (also referred to as Diachronic Research)

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas.

For example, a cohort study that analyses changes in a particular indigenous population over a period of 15 years.

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

F)According to Sources of Information

Primary Research: This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research: Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher

Steps in Research:

Research involves a series of systematic steps that guide the researcher from the initial idea or question to the presentation of findings. These steps can vary somewhat depending on the specific research field and the nature of the research project, but here is a general outline of the typical steps in research:

Identification of Research Problem or Question: The research process begins with identifying a research problem or question that needs to be investigated. This may arise from curiosity, gaps in existing knowledge, practical concerns, or societal issues.

Review of Literature: Conduct a comprehensive review of existing literature to understand what has already been studied in the area of your research. This step helps you refine your research question and identify gaps in knowledge.

Formulation of Hypotheses or Research Objectives: Based on the research question and the literature review, formulate hypotheses (in quantitative research) or research objectives (in qualitative research) that outline what you aim to achieve with your research.

Design of Research Methods: Decide on the research methods and techniques you will use to collect data. This includes selecting data sources, determining the research approach (quantitative, qualitative, mixed methods), and designing data collection tools (e.g., surveys, questionnaires, interviews, experiments).

Sampling: If applicable, determine the sampling strategy and select the sample size. Sampling methods can include random sampling, stratified sampling, convenience sampling, and others, depending on the research design.

Data Collection: Collect data according to the methods and tools you have designed. This may involve conducting surveys, interviews, experiments, observations, or document analysis.

Data Analysis: Analyze the collected data using appropriate statistical or qualitative analysis techniques. This step varies depending on the research design and objectives. Common analysis methods include statistical tests, thematic analysis, content analysis, and coding.

Interpretation of Results: Interpret the findings from your data analysis in the context of your research objectives. Consider whether your results support or refute your hypotheses or research questions.

Discussion: Discuss the implications of your findings, their significance in the broader context, and how they relate to existing literature. Address any limitations of your study and suggest avenues for further research.

Conclusion: Summarize the key findings and conclusions of your research. Emphasize the contributions of your study to the field.

Recommendations: If applicable, provide practical recommendations based on your research findings. These recommendations can be used by policymakers, practitioners, or other stakeholders.

Citation and Referencing: Properly cite and reference all sources and studies that you used in your research to give credit and avoid plagiarism.

Report Writing: Prepare a well-structured research report or paper that includes an introduction, literature review, methodology, results, discussion, conclusion, and references.

Type I and Type II errors are concepts in hypothesis testing, which is a fundamental component of statistical analysis in research. These errors represent two different ways in which researchers can make mistakes when testing a hypothesis.

Type I Error (False Positive): Type I error occurs when a null hypothesis that is actually true is incorrectly rejected. In other words, it's the mistake of concluding that there is a significant effect, relationship, or difference when there is none in reality.

The probability of committing a Type I error is denoted as α (alpha), and it is also called the significance level. Common significance levels are 0.05 (5%) and 0.01 (1%). Lowering the significance level reduces the chances of a Type I error but increases the risk of Type II errors.

Example: Concluding that a new drug is effective when, in reality, it has no effect on a medical condition.

Type II Error (False Negative): Type II error occurs when a null hypothesis that is actually false is not rejected. In other words, it's the mistake of failing to detect a significant effect, relationship, or difference when it exists.

The probability of committing a Type II error is denoted as β (beta). The complement of β, which is 1 - β, is known as the statistical power of a test. A high power indicates a low risk of Type II error.

Example: Failing to conclude that a new drug is effective when, in reality, it has a positive effect on a medical condition.

 

Hypothesis:

A hypothesis is a specific and testable statement or proposition that suggests a potential answer or explanation to a research question. Hypotheses are more common in quantitative research, where researchers aim to establish relationships, make predictions, or test theories. A hypothesis typically includes an independent variable (the factor being manipulated or studied) and a dependent variable (the outcome being measured or observed).

Hypotheses can be categorized into several types based on their nature and the research context:

Null Hypothesis (H0): The null hypothesis represents a statement of no effect or no relationship between variables. It is often used to test whether any observed differences or relationships in data are due to chance or random variation. Researchers aim to either reject the null hypothesis in favor of an alternative hypothesis or fail to reject it based on statistical analysis.

Example: H0: There is no significant difference in test scores between students who receive tutoring and those who do not.

Alternative Hypothesis (Ha or H1): The alternative hypothesis is a statement that contradicts the null hypothesis. It suggests that there is a significant effect, relationship, or difference in the variables being studied. Researchers aim to provide evidence in support of the alternative hypothesis.

Example: Ha: Students who receive tutoring will achieve higher test scores than those who do not.

Directional Hypothesis: A directional hypothesis specifies the expected direction of the effect or relationship. It predicts whether a change will result in an increase or a decrease in a variable.

Example: Ha: Students who receive tutoring will achieve higher test scores than those who do not (predicting an increase).

Non-Directional Hypothesis: A non-directional hypothesis does not specify the expected direction of the effect or relationship. It simply predicts that a significant difference or relationship will exist without indicating whether it will be positive or negative.

Example: Ha: There is a significant difference in test scores between students who receive tutoring and those who do not (without specifying the direction).

 

Sampling:

Sampling is the process of selecting a subset or a sample from a larger population for the purpose of conducting research or drawing conclusions about that population. Sampling is an essential component of many research studies because it is often impractical or impossible to collect data from an entire population. Instead, researchers use various sampling techniques to gather data from a representative subset of the population. The goal of sampling is to obtain results from the sample that accurately reflect the characteristics or attributes of the larger population.

The choice of a particular sampling method depends on the research objectives, the characteristics of the population, available resources, and the desired level of precision. Here are some common sampling methods:

Random Sampling: here every individual or item in the population has an equal chance of being selected for the sample. This method minimizes bias and ensures that the sample is representative of the population. Random sampling can be done using random number generators or drawing lots.

Stratified Sampling: here, the population is divided into distinct subgroups or strata based on certain characteristics (e.g., age, gender, income). Then, a random sample is selected independently from each stratum. This method ensures that each subgroup is represented in the sample proportionally.

Systematic Sampling: it  involves selecting every nth individual or item from a list of the population. For example, if you have a population of 1,000 and you want a sample of 100, you would select every 10th person from the list. This method is straightforward and efficient but can introduce bias if there's a hidden pattern in the list.

Cluster Sampling: it involves dividing the population into clusters or groups (e.g., geographic regions, schools, households), randomly selecting some clusters, and then sampling all individuals or items within the selected clusters. Cluster sampling is practical when it is difficult to create a comprehensive list of the entire population.

Convenience Sampling: it involves selecting individuals or items for the sample based on their easy accessibility or availability. This method is quick and convenient but can introduce bias because it doesn't ensure that the sample is representative of the population.

Judgmental or Purposive Sampling: the researcher selects specific individuals or items deliberately based on certain criteria or characteristics relevant to the research. This method is often used in qualitative research or when specific expertise is needed.

Snowball Sampling: it is commonly used in studies involving hard-to-reach populations or social networks. The researcher starts with a few initial participants and asks them to refer other potential participants. This method is useful when the population is not well-defined or accessible through traditional means.

Quota Sampling: it involves dividing the population into categories or quotas based on specific characteristics (e.g., age, gender, ethnicity). The researcher then selects individuals non-randomly to fill the quotas until they are met. Quota sampling is often used in market research.

Volunteer or Self-Selection Sampling: In volunteer or self-selection sampling, individuals voluntarily choose to participate in the study. This method is commonly seen in online surveys or studies where participants opt-in. It can introduce significant bias because those who choose to participate may differ from those who do not.

Multi-Stage Sampling: Multi-stage sampling combines various sampling methods in a multi-step process. For example, researchers might use cluster sampling to select regions, followed by stratified sampling within those regions to select individuals. Multi-stage sampling is often used in complex surveys.