types of research descriptive vs analytical

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Descriptive vs. Analytical Research in Sociology: A Comparative Study

Table of Contents

When we delve into the world of research, particularly in fields like patterns of social relationships , social interaction, and culture.">sociology , we encounter a myriad of methods designed to uncover the layers of human society and behavior. Two of the most fundamental research methods are descriptive and analytical research . Each plays a crucial role in understanding our world, but they do so in distinctly different ways. So, what exactly are these methods, and how do they compare when applied in the realm of social studies? Let’s embark on a comparative journey to understand these methodologies better.

Understanding Descriptive Research

Descriptive research is akin to the meticulous work of an artist attempting to capture the intricate details of a landscape. It aims to accurately describe the characteristics of a particular population or phenomenon. By painting a picture of the ‘what’ aspect, this method helps researchers to understand the prevalence of certain attributes, behaviors, or issues within a group.

Key Features of Descriptive Research

• Snapshot in time: It often involves studying a single point or period, providing a snapshot rather than a motion picture.
• Surveys and observations : Common tools include surveys , observations, and case studies .
• Quantitative data: It leans heavily on quantitative data to present findings in numerical format.
• No hypothesis testing: Unlike other research types, it doesn’t typically involve hypothesis testing.

When to Use Descriptive Research

• Establishing a baseline : When there’s a need to set a reference point for future studies or track changes over time.
• Exploratory purposes: When little is known about a topic and there’s a need to gather initial information that could inform future research.
• Policy-making: When organizations or government bodies need factual data to inform decisions and policies.

Exploring Analytical Research

On the flip side, analytical research steps beyond mere description to explore the ‘why’ and ‘how’. It’s like a detective piecing together clues to not just recount events, but to understand the relationships and causations behind them. Analytical researchers critically evaluate information to draw conclusions and generalizations that extend beyond the immediate data.

Key Characteristics of Analytical Research

• Critical evaluation: It involves a deep analysis of the available information to form judgments.
• Qualitative and quantitative data: Uses both numerical data and non-numerical data for a more comprehensive analysis.
• Hypothesis-driven: This method often starts with a hypothesis that the research is designed to test.
• Seeking patterns: Aims to identify patterns, relationships, and causations.

When to Opt for Analytical Research

• Understanding complexities: When the research question is complex and requires understanding the interplay of various factors.
• Building upon previous research: When expanding on existing knowledge or challenging prevailing theories.
• Recommendations for action: When research is aimed at providing actionable insights or solutions to problems.

Comparing Descriptive and Analytical Research in Real-World Scenarios

Imagine a sociologist aiming to tackle a pressing social issue, such as the dynamics of homelessness in urban areas. Descriptive research would enable them to establish the scale and scope of homelessness, identifying key demographics and patterns. Analytical research, however, would take these findings and probe deeper into the causes, examining the social, economic, and political factors that contribute to the situation and what can be done to alleviate it.

Advantages and Limitations

Each research type has its own set of strengths and weaknesses. Descriptive research is powerful for mapping out the landscape but may fall short in explaining the underlying reasons for observed phenomena. Analytical research, with its depth, can provide those explanations, but it may be more time-consuming and complex to conduct.

Choosing the Right Approach

Deciding between descriptive and analytical research often comes down to the specific objectives of the study. It’s not uncommon for researchers to employ both methods within the same broader research project to maximize their understanding of a topic.

In conclusion, descriptive and analytical research are two sides of the same coin, offering different lenses through which we can view and interpret the intricacies of social phenomena. By understanding their distinctions and applications, researchers can better design studies that yield rich, actionable insights into the fabric of society.

What do you think? Could a blend of both descriptive and analytical research provide a more holistic understanding of social issues? Are there situations where one method is clearly preferable over the other?

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Research Methodologies & Methods

1 Logic of Inquiry in Social Research

• A Science of Society
• Comte’s Ideas on the Nature of Sociology
• Observation in Social Sciences
• Logical Understanding of Social Reality

2 Empirical Approach

• Empirical Approach
• Rules of Data Collection
• Cultural Relativism
• Problems Encountered in Data Collection
• Difference between Common Sense and Science
• What is Ethical?
• What is Normal?
• Understanding the Data Collected
• Managing Diversities in Social Research
• Problematising the Object of Study
• Conclusion: Return to Good Old Empirical Approach

3 Diverse Logic of Theory Building

• Concern with Theory in Sociology
• Concepts: Basic Elements of Theories
• Why Do We Need Theory?
• Hypothesis Description and Experimentation
• Controlled Experiment
• Designing an Experiment
• How to Test a Hypothesis
• Sensitivity to Alternative Explanations
• Rival Hypothesis Construction
• The Use and Scope of Social Science Theory
• Theory Building and Researcher’s Values

4 Theoretical Analysis

• Premises of Evolutionary and Functional Theories
• Critique of Evolutionary and Functional Theories
• Turning away from Functionalism
• What after Functionalism
• Post-modernism
• Trends other than Post-modernism

5 Issues of Epistemology

• Some Major Concerns of Epistemology
• Rationalism
• Phenomenology: Bracketing Experience

6 Philosophy of Social Science

• Foundations of Science
• Science, Modernity, and Sociology
• Rethinking Science
• Crisis in Foundation

7 Positivism and its Critique

• Heroic Science and Origin of Positivism
• Early Positivism
• Consolidation of Positivism
• Critiques of Positivism

8 Hermeneutics

• Methodological Disputes in the Social Sciences
• Tracing the History of Hermeneutics
• Hermeneutics and Sociology
• Philosophical Hermeneutics
• The Hermeneutics of Suspicion
• Phenomenology and Hermeneutics

9 Comparative Method

• Relationship with Common Sense; Interrogating Ideological Location
• The Historical Context
• Elements of the Comparative Approach

10 Feminist Approach

• Features of the Feminist Method
• Feminist Methods adopt the Reflexive Stance
• Feminist Discourse in India

11 Participatory Method

• Delineation of Key Features

12 Types of Research

• Basic and Applied Research
• Descriptive and Analytical Research
• Empirical and Exploratory Research
• Quantitative and Qualitative Research
• Explanatory (Causal) and Longitudinal Research
• Experimental and Evaluative Research
• Participatory Action Research

13 Methods of Research

• Evolutionary Method
• Comparative Method
• Historical Method
• Personal Documents

14 Elements of Research Design

• Structuring the Research Process

15 Sampling Methods and Estimation of Sample Size

• Classification of Sampling Methods
• Sample Size

16 Measures of Central Tendency

• Relationship between Mean, Mode, and Median
• Choosing a Measure of Central Tendency

17 Measures of Dispersion and Variability

• The Variance
• The Standard Deviation
• Coefficient of Variation

18 Statistical Inference- Tests of Hypothesis

• Statistical Inference
• Tests of Significance

19 Correlation and Regression

• Correlation
• Method of Calculating Correlation of Ungrouped Data
• Method Of Calculating Correlation Of Grouped Data

20 Survey Method

• Rationale of Survey Research Method
• History of Survey Research
• Defining Survey Research
• Sampling and Survey Techniques
• Operationalising Survey Research Tools
• Advantages and Weaknesses of Survey Research

21 Survey Design

• Preliminary Considerations
• Stages / Phases in Survey Research
• Formulation of Research Question
• Survey Research Designs
• Sampling Design

22 Survey Instrumentation

• Techniques/Instruments for Data Collection
• Questionnaire Construction
• Issues in Designing a Survey Instrument

23 Survey Execution and Data Analysis

• Problems and Issues in Executing Survey Research
• Data Analysis
• Ethical Issues in Survey Research

24 Field Research – I

• History of Field Research
• Ethnography
• Theme Selection
• Gaining Entry in the Field
• Key Informants
• Participant Observation

25 Field Research – II

• Interview its Types and Process
• Feminist and Postmodernist Perspectives on Interviewing
• Narrative Analysis
• Interpretation
• Case Study and its Types
• Life Histories
• Oral History
• PRA and RRA Techniques

26 Reliability, Validity and Triangulation

• Concepts of Reliability and Validity
• Three Types of “Reliability”
• Working Towards Reliability
• Procedural Validity
• Field Research as a Validity Check
• Method Appropriate Criteria
• Triangulation
• Ethical Considerations in Qualitative Research

27 Qualitative Data Formatting and Processing

• Qualitative Data Processing and Analysis
• Description
• Classification
• Making Connections
• Theoretical Coding
• Qualitative Content Analysis

28 Writing up Qualitative Data

• Problems of Writing Up
• Grasp and Then Render
• “Writing Down” and “Writing Up”
• Write Early
• Writing Styles
• First Draft

29 Using Internet and Word Processor

• What is Internet and How Does it Work?
• Internet Services
• Searching on the Web: Search Engines
• Accessing and Using Online Information
• Online Journals and Texts
• Statistical Reference Sites
• Data Sources
• Uses of E-mail Services in Research

30 Using SPSS for Data Analysis Contents

• Introduction
• Starting and Exiting SPSS
• Creating a Data File
• Univariate Analysis
• Bivariate Analysis

31 Using SPSS in Report Writing

• Why to Use SPSS
• Working with SPSS Output
• Copying SPSS Output to MS Word Document

32 Tabulation and Graphic Presentation- Case Studies

• Structure for Presentation of Research Findings
• Data Presentation: Editing, Coding, and Transcribing
• Case Studies
• Qualitative Data Analysis and Presentation through Software
• Types of ICT used for Research

33 Guidelines to Research Project Assignment

• Overview of Research Methodologies and Methods (MSO 002)
• Research Project Objectives
• Preparation for Research Project
• Stages of the Research Project
• Supervision During the Research Project
• Submission of Research Project
• Methodology for Evaluating Research Project

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• Descriptive Research Designs: Types, Examples & Methods

One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.

This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.

In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.

What is Descriptive Research?

Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.

This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. 

For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.

What Are The Types of Descriptive Research?

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer. 

This way, the researcher can describe the qualifications possessed by the employed demographics of this community. 

• Descriptive-normative survey

This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.

Examples of Descriptive Research

There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.

• Comparing Student Performance:

An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.

Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.

• Scientific Classification

During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.

For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc. 

All these classifications are made a result of descriptive research which describes what they are.

• Human Behavior

When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.

This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.

What are the Characteristics of Descriptive Research?  

The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:

• Quantitativeness

Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.

• Qualitativeness

It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.

• Uncontrolled variables

In descriptive research, researchers cannot control the variables like they do in experimental research.

• The basis for further research

The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.

This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.

Why Use Descriptive Research Design?  

Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.

• Define subject characteristics :

It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.

For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.

• Measure Data Trends

It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.

Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.

Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?

Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.

• Validate existing conditions

When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.

• Conducted Overtime

Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.

What are the Disadvantages of Descriptive Research?  

• Response and Non-response Bias

Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.

• The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
• A case-study or sample taken from a large population is not representative of the whole population.
• Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

What are the Data Collection Methods in Descriptive Research?  

There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.

1. Observational Method

The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.

It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.

Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods. 

Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.

2. Case Study Method

A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.

This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.

3. Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.

Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.

Survey research can be carried out both online and offline using the following methods

• Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
• Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.

What Are The Differences Between Descriptive and Correlational Research?  

Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.

Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).

Correlational research may be used in 2 situations;

(i) when trying to find out if there is a relationship between two variables, and

(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables. 

Below are some of the differences between correlational and descriptive research:

• Definitions :

Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables. 

• Characteristics :

Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.

• Predictions :

 Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.

Descriptive Research vs. Causal Research

Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation. 

It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.

Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.

Descriptive Research vs. Analytical Research

Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor. 

It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors. 

It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.

Descriptive Research vs. Exploratory Research

Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause. 

Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic. 

It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.

Read More – Exploratory Research: What are its Method & Examples?

Descriptive Research vs. Experimental Research

Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs. 

Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.

Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects. 

Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.

Related – Experimental vs Non-Experimental Research: 15 Key Differences

Descriptive Research vs. Explanatory Research

Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context. 

Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms. 

It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.

Descriptive Research vs. Inferential Research

Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study. 

Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.

Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample. 

It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.

Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .

It is also very useful in solving real-life problems in various fields of social science, physical science, and education.

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What are Analytical Study Designs?

• Research Process
• Peer Review

Analytical study designs can be experimental or observational and each type has its own features. In this article, you'll learn the main types of designs and how to figure out which one you'll need for your study.

Updated on September 19, 2022

A study design is critical to your research study because it determines exactly how you will collect and analyze your data. If your study aims to study the relationship between two variables, then an analytical study design is the right choice.

But how do you know which type of analytical study design is best for your specific research question? It's necessary to have a clear plan before you begin data collection. Lots of researchers, sadly, speed through this or don't do it at all.

When are analytical study designs used?

A study design is a systematic plan, developed so you can carry out your research study effectively and efficiently. Having a design is important because it will determine the right methodologies for your study. Using the right study design makes your results more credible, valid, and coherent.

Descriptive vs. analytical studies

Study designs can be broadly divided into either descriptive or analytical.

Descriptive studies describe characteristics such as patterns or trends. They answer the questions of what, who, where, and when, and they generate hypotheses. They include case reports and qualitative studies.

Analytical study designs quantify a relationship between different variables. They answer the questions of why and how. They're used to test hypotheses and make predictions.

Experimental and observational

Analytical study designs can be either experimental or observational. In experimental studies, researchers manipulate something in a population of interest and examine its effects. These designs are used to establish a causal link between two variables.

In observational studies, in contrast, researchers observe the effects of a treatment or intervention without manipulating anything. Observational studies are most often used to study larger patterns over longer periods.

Experimental study designs

Experimental study designs are when a researcher introduces a change in one group and not in another. Typically, these are used when researchers are interested in the effects of this change on some outcome. It's important to try to ensure that both groups are equivalent at baseline to make sure that any differences that arise are from any introduced change.

In one study, Reiner and colleagues studied the effects of a mindfulness intervention on pain perception . The researchers randomly assigned participants into an experimental group that received a mindfulness training program for two weeks. The rest of the participants were placed in a control group that did not receive the intervention.

Experimental studies help us establish causality. This is critical in science because we want to know whether one variable leads to a change, or causes another. Establishing causality leads to higher internal validity and makes results reproducible.

Experimental designs include randomized control trials (RCTs), nonrandomized control trials (non-RCTs), and crossover designs. Read on to learn the differences.

Randomized control trials

In an RCT, one group of individuals receives an intervention or a treatment, while another does not. It's then possible to investigate what happens to the participants in each group.

Another important feature of RCTs is that participants are randomly assigned to study groups. This helps to limit certain biases and retain better control. Randomization also lets researchers pinpoint any differences in outcomes to the intervention received during the trial. RTCs are considered the gold standard in biomedical research and are considered to provide the best kind of evidence.

For example, one RCT looked at whether an exercise intervention impacts depression . Researchers randomly placed patients with depressive symptoms into intervention groups containing different types of exercise (i.e., light, moderate, or strong). Another group received usual medications or no exercise interventions.

Results showed that after the 12-week trial, patients in all exercise groups had decreased depression levels compared to the control group. This means that by using an RCT design, researchers can now safely assume that the exercise variable has a positive impact on depression.

However, RCTs are not without drawbacks. In the example above, we don't know if exercise still has a positive impact on depression in the long term. This is because it's not feasible to keep people under these controlled settings for a long time.

Advantages of RCTs

• It is possible to infer causality
• Everything is properly controlled, so very little is left to chance or bias
• Can be certain that any difference is coming from the intervention

Disadvantages of RCTs

• Expensive and can be time-consuming
• Can take years for results to be available
• Cannot be done for certain types of questions due to ethical reasons, such as asking participants to undergo harmful treatment
• Limited in how many participants researchers can adequately manage in one study or trial
• Not feasible for people to live under controlled conditions for a long time

Nonrandomized controlled trials

Nonrandomized controlled trials are a type of nonrandomized controlled studies (NRS) where the allocation of participants to intervention groups is not done randomly . Here, researchers purposely assign some participants to one group and others to another group based on certain features. Alternatively, participants can sometimes also decide which group they want to be in.

For example, in one study, clinicians were interested in the impact of stroke recovery after being in an enriched versus non-enriched hospital environment . Patients were selected for the trial if they fulfilled certain requirements common to stroke recovery. Then, the intervention group was given access to an enriched environment (i.e. internet access, reading, going outside), and another group was not. Results showed that the enriched group performed better on cognitive tasks.

NRS are useful in medical research because they help study phenomena that would be difficult to measure with an RCT. However, one of their major drawbacks is that we cannot be sure if the intervention leads to the outcome. In the above example, we can't say for certain whether those patients improved after stroke because they were in the enriched environment or whether there were other variables at play.

Advantages of NRS's

• Good option when randomized control trials are not feasible
• More flexible than RCTs

Disadvantages of NRS's

• Can't be sure if the groups have underlying differences
• Introduces risk of bias and confounds

Crossover study

In a crossover design, each participant receives a sequence of different treatments. Crossover designs can be applied to RCTs, in which each participant is randomly assigned to different study groups.

For example, one study looked at the effects of replacing butter with margarine on lipoproteins levels in individuals with cholesterol . Patients were randomly assigned to a 6-week butter diet, followed by a 6-week margarine diet. In between both diets, participants ate a normal diet for 5 weeks.

These designs are helpful because they reduce bias. In the example above, each participant completed both interventions, making them serve as their own control. However, we don't know if eating butter or margarine first leads to certain results in some subjects.

Advantages of crossover studies

• Each participant serves as their own control, reducing confounding variables
• Require fewer participants, so they have better statistical power

Disadvantages of crossover studies

• Susceptible to order effects, meaning the order in which a treatment was given may have an effect
• Carry-over effects between treatments

Observational studies

In observational studies, researchers watch (observe) the effects of a treatment or intervention without trying to change anything in the population. Observational studies help us establish broad trends and patterns in large-scale datasets or populations. They are also a great alternative when an experimental study is not an option.

Unlike experimental research, observational studies do not help us establish causality. This is because researchers do not actively control any variables. Rather, they investigate statistical relationships between them. Often this is done using a correlational approach.

For example, researchers would like to examine the effects of daily fiber intake on bone density . They conduct a large-scale survey of thousands of individuals to examine correlations of fiber intake with different health measures.

The main observational studies are case-control, cohort, and cross-sectional. Let's take a closer look at each one below.

Case-control study

A case-control is a type of observational design in which researchers identify individuals with an existing health situation (cases) and a similar group without the health issue (controls). The cases and the controls are then compared based on some measurements.

Frequently, data collection in a case-control study is retroactive (i.e., backwards in time). This is because participants have already been exposed to the event in question. Additionally, researchers must go through records and patient files to obtain the records for this study design.

For example, a group of researchers examined whether using sleeping pills puts people at risk of Alzheimer's disease . They selected 1976 individuals that received a dementia diagnosis (“cases”) with 7184 other individuals (“controls”). Cases and controls were matched on specific measures such as sex and age. Patient data was consulted to find out how much sleeping pills were consumed over the course of a certain time.

Case-control is ideal for situations where cases are easy to pick out and compare. For instance, in studying rare diseases or outbreaks.

Advantages of case-control studies

• Feasible for rare diseases
• Cheaper and easier to do than an RCT

Disadvantages of case-control studies

• Relies on patient records, which could be lost or damaged
• Potential recall and selection bias

Cohort study (longitudinal)

A cohort is a group of people who are linked in some way. For instance, a birth year cohort is all people born in a specific year. In cohort studies, researchers compare what happens to individuals in the cohort that have been exposed to some variable compared with those that haven't on different variables. They're also called longitudinal studies.

The cohort is then repeatedly assessed on variables of interest over a period of time. There is no set amount of time required for cohort studies. They can range from a few weeks to many years.

Cohort studies can be prospective. In this case, individuals are followed for some time into the future. They can also be retrospective, where data is collected on a cohort from records.

One of the longest cohort studies today is The Harvard Study of Adult Development . This cohort study has been tracking various health outcomes of 268 Harvard graduates and 456 poor individuals in Boston from 1939 to 2014. Physical screenings, blood samples, brain scans and surveys were collected on this cohort for over 70 years. This study has produced a wealth of knowledge on outcomes throughout life.

A cohort study design is a good option when you have a specific group of people you want to study over time. However, a major drawback is that they take a long time and lack control.

Advantages of cohort studies

• Ethically safe
• Allows you to study multiple outcome variables
• Establish trends and patterns

Disadvantages of cohort studies

• Time consuming and expensive
• Can take many years for results to be revealed
• Too many variables to manage
• Depending on length of study, can have many changes in research personnel

Cross-sectional study

Cross-sectional studies are also known as prevalence studies. They look at the relationship of specific variables in a population in one given time. In cross-sectional studies, the researcher does not try to manipulate any of the variables, just study them using statistical analyses. Cross-sectional studies are also called snapshots of a certain variable or time.

For example, researchers wanted to determine the prevalence of inappropriate antibiotic use to study the growing concern about antibiotic resistance. Participants completed a self-administered questionnaire assessing their knowledge and attitude toward antibiotic use. Then, researchers performed statistical analyses on their responses to determine the relationship between the variables.

Cross-sectional study designs are ideal when gathering initial data on a research question. This data can then be analyzed again later. By knowing the public's general attitudes towards antibiotics, this information can then be relayed to physicians or public health authorities. However, it's often difficult to determine how long these results stay true for.

Advantages of cross-sectional studies

• Fast and inexpensive
• Provides a great deal of information for a given time point
• Leaves room for secondary analysis

Disadvantages of cross-sectional studies

• Requires a large sample to be accurate
• Not clear how long results remain true for
• Do not provide information on causality
• Cannot be used to establish long-term trends because data is only for a given time

So, how about your next study?

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Analytical Research: What is it, Importance + Examples

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

QuestionPro offers solutions for every issue and industry, making it more than just survey software. For handling data, we also have systems like our InsightsHub research library.

You may make crucial decisions quickly while using QuestionPro to understand your clients and other study subjects better. Make use of the possibilities of the enterprise-grade research suite right away!

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Critical Writing 101

Descriptive vs analytical vs critical writing.

By: Derek Jansen (MBA) | Expert Reviewed By: Dr Eunice Rautenbach | April 2017

Across the thousands of students we work with , descriptive writing (as opposed to critical or analytical writing) is an incredibly pervasive problem . In fact, it’s probably the biggest killer of marks in dissertations, theses and research papers . So, in this post, we’ll explain the difference between descriptive and analytical writing in straightforward terms, along with plenty of practical examples.

Descriptive vs Analytical Writing

Writing critically is one of the most important skills you’ll need to master for your academic journey, but what exactly does this mean?

Well, when it comes to writing, at least for academic purposes, there are two main types – descriptive writing and critical writing. Critical writing is also sometimes referred to as analytical writing, so we’ll use these two terms interchangeably.

To understand what constitutes critical (or analytical) writing, it’s useful to compare it against its opposite, descriptive writing. At the most basic level, descriptive writing merely communicates the “ what ”, “ where ”, “ when ” or “ who ”. In other words, it describes a thing, place, time or person. It doesn’t consider anything beyond that or explore the situation’s impact, importance or meaning. Here’s an example of a descriptive sentence:

  “Yesterday, the president unexpectedly fired the minister of finance.”

As you can see, this sentence just states what happened, when it happened and who was involved. Classic descriptive writing.

Contrasted to this, critical writing takes things a step further and unveils the “ so what? ” – in other words, it explains the impact or consequence of a given situation. Let’s stick with the same event and look at an example of analytical writing:

“The president’s unexpected firing of the well-respected finance minister had an immediate negative impact on investor confidence. This led to a sharp decrease in the value of the local currency, especially against the US dollar. This devaluation means that all dollar-based imports are now expected to rise in cost, thereby raising the cost of living for citizens, and reducing disposable income.”

As you can see in this example, the descriptive version only tells us what happened (the president fired the finance minister), whereas the critical version goes on to discuss some of the impacts of the president’s actions.

Ideally, critical writing should always link back to the broader objectives of the paper or project, explaining what each thing or event means in relation to those objectives. In a dissertation or thesis, this would involve linking the discussion back to the research aims, objectives and research questions – in other words, the golden thread .

Sounds a bit fluffy and conceptual? Let’s look at an example:

If your research aims involved understanding how the local environment impacts demand for specialty imported vegetables, you would need to explain how the devaluation of the local currency means that the imported vegetables would become more expensive relative to locally farmed options. This in turn would likely have a negative impact on sales, as consumers would turn to cheaper local alternatives.

As you can see, critical (or analytical) writing goes beyond just describing (that’s what descriptive writing covers) and instead focuses on the meaning of things, events or situations, especially in relation to the core research aims and questions.

Need a helping hand?

But wait, there’s more.

This “ what vs so what”  distinction is important in understanding the difference between description and analysis, but it is not the only difference – the differences go deeper than this. The table below explains some other key differences between descriptive and analytical writing.

Should I avoid descriptive writing altogether?

Not quite. For the most part, you’ll need some descriptive writing to lay the foundation for the critical, analytical writing. In other words, you’ll usually need to state the “what” before you can discuss the “so what”. Therefore, description is simply unavoidable and in fact quite essential , but you do want to keep it to a minimum and focus your word count on the analytical side of things.

As you write, a good rule of thumb is to identify every what (in other words, every descriptive point you make) and then check whether it is accompanied by a so what (in other words, a critical conclusion regarding its meaning or impact).

Of course, this won’t always be necessary as some conclusions are fairly obvious and go without saying. But, this basic practice should help you minimise description, maximise analysis, and most importantly, earn you marks!

Let’s recap.

So, the key takeaways for this post are as follows:

• Descriptive writing focuses on the what , while critical/analytical writing focuses on the so what .
• Analytical writing should link the discussion back to the research aims, objectives or research questions (the golden thread).
• Some amount of description will always be needed, but aim to minimise description and maximise analysis to earn higher marks.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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19 Comments

Thank you so much. This was helpful and a switch from the bad writing habits to the good habits.

Great to hear that, Sarah. Glad you found it useful!

I am currently working on my Masters Thesis and found this extremely informative and helpful. Thank you kindly.

I’m currently a University student and this is so helpful. Thank you.

It really helped me to get the exact meaning of analytical writing. Differences between the two explains it well

Thank you! this was very useful

With much appreciation, I say thank you. Your explanations are down to earth. It has been helpful.

Very helpful towards my theses journey! Many thanks 👍

very helpful

very helpful indeed

Thanks Derek for the useful coaching

Thank you for sharing this. I was stuck on descriptive now I can do my corrections. Thank you.

I was struggling to differentiate between descriptive and analytical writing. I googled and found this as it is so helpful. Thank you for sharing.

I am glad to see this differences of descriptive against analytical writing. This is going to improve my masters dissertation

Thanks in deed. It was helpful

Thank you so much. I’m now better informed

Busy with MBA in South Africa, this is very helpful as most of the writing requires one to expound on the topics. thanks for this, it’s a salvation from watching the blinking cursor for hours while figuring out what to write to hit the 5000 word target 😂

It’s been fantastic and enriching. Thanks a lot, GRAD COACH.

Wonderful explanation of descriptive vs analytic writing with examples. This is going to be greatly helpful for me as I am writing my thesis at the moment. Thank you Grad Coach. I follow your YouTube videos and subscribed and liked every time I watch one.

Very useful piece. thanks

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Home » Descriptive Analytics – Methods, Tools and Examples

Descriptive Analytics – Methods, Tools and Examples

Table of Contents

Descriptive Analytics

Definition:

Descriptive analytics focused on describing or summarizing raw data and making it interpretable. This type of analytics provides insight into what has happened in the past. It involves the analysis of historical data to identify patterns, trends, and insights. Descriptive analytics often uses visualization tools to represent the data in a way that is easy to interpret.

Descriptive Analytics in Research

Descriptive analytics plays a crucial role in research, helping investigators understand and describe the data collected in their studies. Here’s how descriptive analytics is typically used in a research setting:

• Descriptive Statistics: In research, descriptive analytics often takes the form of descriptive statistics . This includes calculating measures of central tendency (like mean, median, and mode), measures of dispersion (like range, variance, and standard deviation), and measures of frequency (like count, percent, and frequency). These calculations help researchers summarize and understand their data.
• Visualizing Data: Descriptive analytics also involves creating visual representations of data to better understand and communicate research findings . This might involve creating bar graphs, line graphs, pie charts, scatter plots, box plots, and other visualizations.
• Exploratory Data Analysis: Before conducting any formal statistical tests, researchers often conduct an exploratory data analysis, which is a form of descriptive analytics. This might involve looking at distributions of variables, checking for outliers, and exploring relationships between variables.
• Initial Findings: Descriptive analytics are often reported in the results section of a research study to provide readers with an overview of the data. For example, a researcher might report average scores, demographic breakdowns, or the percentage of participants who endorsed each response on a survey.
• Establishing Patterns and Relationships: Descriptive analytics helps in identifying patterns, trends, or relationships in the data, which can guide subsequent analysis or future research. For instance, researchers might look at the correlation between variables as a part of descriptive analytics.

Descriptive Analytics Techniques

Descriptive analytics involves a variety of techniques to summarize, interpret, and visualize historical data. Some commonly used techniques include:

Statistical Analysis

This includes basic statistical methods like mean, median, mode (central tendency), standard deviation, variance (dispersion), correlation, and regression (relationships between variables).

Data Aggregation

It is the process of compiling and summarizing data to obtain a general perspective. It can involve methods like sum, count, average, min, max, etc., often applied to a group of data.

Data Mining

This involves analyzing large volumes of data to discover patterns, trends, and insights. Techniques used in data mining can include clustering (grouping similar data), classification (assigning data into categories), association rules (finding relationships between variables), and anomaly detection (identifying outliers).

Data Visualization

This involves presenting data in a graphical or pictorial format to provide clear and easy understanding of the data patterns, trends, and insights. Common data visualization methods include bar charts, line graphs, pie charts, scatter plots, histograms, and more complex forms like heat maps and interactive dashboards.

This involves organizing data into informational summaries to monitor how different areas of a business are performing. Reports can be generated manually or automatically and can be presented in tables, graphs, or dashboards.

Cross-tabulation (or Pivot Tables)

It involves displaying the relationship between two or more variables in a tabular form. It can provide a deeper understanding of the data by allowing comparisons and revealing patterns and correlations that may not be readily apparent in raw data.

Descriptive Modeling

Some techniques use complex algorithms to interpret data. Examples include decision tree analysis, which provides a graphical representation of decision-making situations, and neural networks, which are used to identify correlations and patterns in large data sets.

Descriptive Analytics Tools

Some common Descriptive Analytics Tools are as follows:

Excel: Microsoft Excel is a widely used tool that can be used for simple descriptive analytics. It has powerful statistical and data visualization capabilities. Pivot tables are a particularly useful feature for summarizing and analyzing large data sets.

Tableau: Tableau is a data visualization tool that is used to represent data in a graphical or pictorial format. It can handle large data sets and allows for real-time data analysis.

Power BI: Power BI, another product from Microsoft, is a business analytics tool that provides interactive visualizations with self-service business intelligence capabilities.

QlikView: QlikView is a data visualization and discovery tool. It allows users to analyze data and use this data to support decision-making.

SAS: SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

SPSS: SPSS (Statistical Package for the Social Sciences) is a software package used for statistical analysis. It’s widely used in social sciences research but also in other industries.

Google Analytics: For web data, Google Analytics is a popular tool. It allows businesses to analyze in-depth detail about the visitors on their website, providing valuable insights that can help shape the success strategy of a business.

R and Python: Both are programming languages that have robust capabilities for statistical analysis and data visualization. With packages like pandas, matplotlib, seaborn in Python and ggplot2, dplyr in R, these languages are powerful tools for descriptive analytics.

Looker: Looker is a modern data platform that can take data from any database and let you start exploring and visualizing.

When to use Descriptive Analytics

Descriptive analytics forms the base of the data analysis workflow and is typically the first step in understanding your business or organization’s data. Here are some situations when you might use descriptive analytics:

Understanding Past Behavior: Descriptive analytics is essential for understanding what has happened in the past. If you need to understand past sales trends, customer behavior, or operational performance, descriptive analytics is the tool you’d use.

Reporting Key Metrics: Descriptive analytics is used to establish and report key performance indicators (KPIs). It can help in tracking and presenting these KPIs in dashboards or regular reports.

Identifying Patterns and Trends: If you need to identify patterns or trends in your data, descriptive analytics can provide these insights. This might include identifying seasonality in sales data, understanding peak operational times, or spotting trends in customer behavior.

Informing Business Decisions: The insights provided by descriptive analytics can inform business strategy and decision-making. By understanding what has happened in the past, you can make more informed decisions about what steps to take in the future.

Benchmarking Performance: Descriptive analytics can be used to compare current performance against historical data. This can be used for benchmarking and setting performance goals.

Auditing and Regulatory Compliance: In sectors where compliance and auditing are essential, descriptive analytics can provide the necessary data and trends over specific periods.

Initial Data Exploration: When you first acquire a dataset, descriptive analytics is useful to understand the structure of the data, the relationships between variables, and any apparent anomalies or outliers.

Examples of Descriptive Analytics

Examples of Descriptive Analytics are as follows:

Retail Industry: A retail company might use descriptive analytics to analyze sales data from the past year. They could break down sales by month to identify any seasonality trends. For example, they might find that sales increase in November and December due to holiday shopping. They could also break down sales by product to identify which items are the most popular. This analysis could inform their purchasing and stocking decisions for the next year. Additionally, data on customer demographics could be analyzed to understand who their primary customers are, guiding their marketing strategies.

Healthcare Industry: In healthcare, descriptive analytics could be used to analyze patient data over time. For instance, a hospital might analyze data on patient admissions to identify trends in admission rates. They might find that admissions for certain conditions are higher at certain times of the year. This could help them allocate resources more effectively. Also, analyzing patient outcomes data can help identify the most effective treatments or highlight areas where improvement is needed.

Finance Industry: A financial firm might use descriptive analytics to analyze historical market data. They could look at trends in stock prices, trading volume, or economic indicators to inform their investment decisions. For example, analyzing the price-earnings ratios of stocks in a certain sector over time could reveal patterns that suggest whether the sector is currently overvalued or undervalued. Similarly, credit card companies can analyze transaction data to detect any unusual patterns, which could be signs of fraud.

Advantages of Descriptive Analytics

Descriptive analytics plays a vital role in the world of data analysis, providing numerous advantages:

• Understanding the Past: Descriptive analytics provides an understanding of what has happened in the past, offering valuable context for future decision-making.
• Data Summarization: Descriptive analytics is used to simplify and summarize complex datasets, which can make the information more understandable and accessible.
• Identifying Patterns and Trends: With descriptive analytics, organizations can identify patterns, trends, and correlations in their data, which can provide valuable insights.
• Inform Decision-Making: The insights generated through descriptive analytics can inform strategic decisions and help organizations to react more quickly to events or changes in behavior.
• Basis for Further Analysis: Descriptive analytics lays the groundwork for further analytical activities. It’s the first necessary step before moving on to more advanced forms of analytics like predictive analytics (forecasting future events) or prescriptive analytics (advising on possible outcomes).
• Performance Evaluation: It allows organizations to evaluate their performance by comparing current results with past results, enabling them to see where improvements have been made and where further improvements can be targeted.
• Enhanced Reporting and Dashboards: Through the use of visualization techniques, descriptive analytics can improve the quality of reports and dashboards, making the data more understandable and easier to interpret for stakeholders at all levels of the organization.
• Immediate Value: Unlike some other types of analytics, descriptive analytics can provide immediate insights, as it doesn’t require complex models or deep analytical capabilities to provide value.

Disadvantages of Descriptive Analytics

While descriptive analytics offers numerous benefits, it also has certain limitations or disadvantages. Here are a few to consider:

• Limited to Past Data: Descriptive analytics primarily deals with historical data and provides insights about past events. It does not predict future events or trends and can’t help you understand possible future outcomes on its own.
• Lack of Deep Insights: While descriptive analytics helps in identifying what happened, it does not answer why it happened. For deeper insights, you would need to use diagnostic analytics, which analyzes data to understand the root cause of a particular outcome.
• Can Be Misleading: If not properly executed, descriptive analytics can sometimes lead to incorrect conclusions. For example, correlation does not imply causation, but descriptive analytics might tempt one to make such an inference.
• Data Quality Issues: The accuracy and usefulness of descriptive analytics are heavily reliant on the quality of the underlying data. If the data is incomplete, incorrect, or biased, the results of the descriptive analytics will be too.
• Over-reliance on Descriptive Analytics: Businesses may rely too much on descriptive analytics and not enough on predictive and prescriptive analytics. While understanding past and present data is important, it’s equally vital to forecast future trends and make data-driven decisions based on those predictions.
• Doesn’t Provide Actionable Insights: Descriptive analytics is used to interpret historical data and identify patterns and trends, but it doesn’t provide recommendations or courses of action. For that, prescriptive analytics is needed.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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8 Types of Data Analysis

Data analysis is an aspect of  data science and data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and modeling data to draw useful insights from it.

What Are the Different Types of Data Analysis?

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including business, science and social science, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in  decision-making , providing a better, faster and more efficacious system that minimizes risks and reduces  human biases .

That said, there are different kinds of data analysis catered with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book  R for Data Science :

• Hypothesis Generation — This involves looking deeply at the data and combining your domain knowledge to generate hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation — This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

Types of Data Analysis

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data. Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries about samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the  Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

2. Diagnostic Analysis 

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies.  

Diagnostic Analysis Example

A footwear store wants to review its website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.   

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection.

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses.

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or standard deviation to your estimation.
• The  accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the  central limit theorem .

Inferential Analysis Example

The idea of drawing an inference about the population at large with a smaller sample size is intuitive. Many statistics you see on the media and the internet are inferential; a prediction of an event based on a small sample. For example, a psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven-to-nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions.

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 US election is a popular topic and many  prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

Predictive analysis takes data from the past and present to make predictions about the future.

More on Data: Explaining the Empirical for Normal Distribution

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome.

Causal analysis is about finding out the causal relationship between variables, and examining how a change in one variable affects another.

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Many graduate-level research and complex topics are suitable examples, but to put it in simple terms, let’s say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

Mechanistic analysis is in some ways a predictive analysis, but modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science .

8. Prescriptive Analysis 

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. AI systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed. 

When to Use the Different Types of Data Analysis 

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

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Home » Research Methodology » The Basic Types of Research

The Basic Types of Research

Research is an academic activity and as such the term should be used in a technical sense. According to Clifford woody research companies 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. Research is, thus, an original contribution to the existing stock of knowledge making for its advancement. It is the persuit of truth with the help of study, observation, comparison and experiment.

Research may be classified into different types for the sake of better understanding of the concept. Several bases can be adopted for classification such as nature of data, branch of knowledge, extent of coverage, place of investigation, method employed, time frame and so on. The basic types of research are as follows:

1. Descriptive vs. Analytical

Descriptive research includes surveys and fact-finding enquiries of different kinds. The major purpose of descriptive research is description of the state of affairs as it exists at present. In social science and business research we quite often use the term Ex post facto research for descriptive research studies. The main characteristic of this method is that the researcher has no control over the variables; he can only report what has happened or what is happening. Most ex post facto research projects are used for descriptive studies in which the researcher seeks to measure such items as, for example, frequency of shopping, preferences of people, or similar data. Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The methods of research utilized in descriptive research are survey methods of all kinds, including comparative and correlational methods. In analytical research, on the other hand, the researcher has to use facts or information already available, and analyze these to make a critical evaluation of the material.

2. Applied vs. Fundamental

Research can either be applied (or action) research or fundamental (to basic or pure) research. Applied research aims at finding a solution for an immediate problem facing a society or an industrial/business organisation, whereas fundamental research is mainly concerned with generalizations and with the formulation of a theory. “Gathering knowledge for knowledge’s sake is termed ‘pure’ or ‘basic’ research.” Research concerning some natural phenomenon or relating to pure mathematics are examples of fundamental research. Similarly, research studies, concerning human behavior carried on with view to make generalizations about human behavior, are also examples of fundamental research, but research aimed at certain conclusions facing a concrete social or business problem is an example of applied research. Research to identify social, economic or political trends that may effect a particular institution or copy research or the marketing research are examples of applied research. Thus, the central aim of applied research is to discover a solution for some pressing practical problems. Whereas basic research is directed towards finding information that has a broad base of applications and thus, adds to the already existing organized body of scientific knowledge.

3. Quantitative vs. Qualitative

Quantitative research is based on the measurement of quantity or amount. It is applicable to phenomena that can be expressed in terms of quantity. Qualitative research, on the other hand, is concerned with qualitative phenomenon, i.e., phenomena relating to or involving quality or kind. For instance, when we are interested in investigating the reasons for human behavior, we quite often talk of ‘Motivation Research’, an important type of qualitative research. This type of research aims at discovering the underlying motives and desires, using in depth interviews for the purpose. Other techniques of such research are word association tests, sentence completion tests, story completion tests and similar other projective techniques. Attitude or opinion research, i.e., research designed to find out how people feel or what they think about a particular subject or institution is also qualitative research. Qualitative research is specially important in the behavioral sciences where the aim is to discover the underlying motives of human behavior. Through such research we can analyse the various factors which motivate people to behave in a particular manner or which make people like or dislike a particular thing. It may be stated, however, that to apply qualitative research in practice is relatively a difficult job and therefore, while doing such research, one should seek guidance from experimental psychologists.

4. Conceptual vs. Empirical

Conceptual research is that related to some abstract idea(s) or theory. It is generally used by philosophers and thinkers to develop new concepts or to reinterpret existing ones. On the other hand, empirical research relies an experience or observation alone, often without due regard for system and theory. It is data based research, coming up with conclusions which arc capable of being verified by observation or experiment. We can also call it as experimental type of research, in such a research it is necessary to get at facts firsthand, at their source, and actively to go about doing certain things to stimulate the production of desired information. In such research, the researcher must first provide himself with a working hypothesis or guess as to the probable results. He then works to get enough facts (data) to prove or disprove his hypothesis. He then sets up experimental designs which he thinks will manipulate the persons or the materials concerned so as to bring forth the desired information. Such research is thus characterized by the experimenter’s control over the variables under study and his deliberate manipulation of one of them to study its effects. Empirical research is appropriate when proof is sought that certain variables affect other variables in some way. Evidence gathered through experiments or empirical studies is today considered to be the most powerful support possible for a given hypothesis.

5. Some Other Types of Research

All other types of research are variations of one or more of the above stated approaches, based on either the purpose of research , or the time required to accomplish research, on the environment in which research is done, or on the basis of some other similar factor. Form the point of view of time, we can think of research either as one-time research or longitudinal research. In the former case the research is confined to a single time-period, whereas in the latter case the research is carried on over several time-periods. Research can be field-setting research or laboratory research or simulation research, depending upon the environment in which it is to be carried out. Research can as well be understood as clinical or diagnostic research. Such research follow case-study methods or in-depth approaches to reach the basic causal relations. Such studies usually go deep into the causes of things or events that interest us, using very small samples and very deep probing data gathering devices. The research may be exploratory or it may be formalized. The objective of exploratory research is the development of hypotheses rather than their testing, whereas formalized research studies are those with substantial structure and with specific hypotheses to be tested. Historical research is that which utilizes historical sources like documents, remains, etc. to study events or ideas of the past, including the philosophy of persons and groups at any remote point of time. Research can also be classified as conclusion-oriented and decision oriented. While doing conclusion oriented research, a researcher is free to pick up a problem, redesign the enquiry as he proceeds and is prepared to conceptualize as he wishes. Decision-oriented research is always for the need of a decision maker and the researcher in this case is not free to embark upon research according to his own inclination. Operations research is an example of decision oriented research since it is a scientific method of providing executive departments with a quantitative basis for decisions regarding operations under their control.

Related posts:

• Using Different Types of Surveys for Data Collection in Research
• Basic Laws of Sampling
• The Research Problem
• The Purpose of Research
• Significance of Research
• Writing the Research Report
• Classification and Tabulation of Data in Research
• Documentary Sources of Information in Research
• Guide to the Development of Research Questionnaires
• Simple Random Sampling in Research

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