analytical research means

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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:

Aspect	Descriptive Research	Analytical Research
Objective	Describe and document characteristics or phenomena.	Analyze and interpret data to understand relationships or causality.
Focus	“What” questions	“Why” and “How” questions
Data Analysis	Summarizing information	Statistical research, hypothesis testing, qualitative research
Goal	Provide an accurate and comprehensive description	Gain insights, make inferences, provide explanations or predictions
Causal Relationships	Not the primary focus	Examining underlying factors, causes, or effects
Examples	Surveys, observations, case-control study, content analysis	Experiments, statistical research, qualitative analysis

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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Analytical vs. Descriptive

What's the difference.

Analytical and descriptive are two different approaches used in various fields of study. Analytical refers to the process of breaking down complex ideas or concepts into smaller components to understand their underlying principles or relationships. It involves critical thinking, logical reasoning, and the use of evidence to support arguments or conclusions. On the other hand, descriptive focuses on providing a detailed account or description of a particular phenomenon or event. It aims to present facts, observations, or characteristics without any interpretation or analysis. While analytical aims to uncover the "why" or "how" behind something, descriptive aims to provide a comprehensive picture of what is being studied. Both approaches have their own merits and are often used in combination to gain a deeper understanding of a subject matter.

Attribute	Analytical	Descriptive
Definition	Focuses on breaking down complex problems into smaller components and analyzing them individually.	Focuses on describing and summarizing data or phenomena without attempting to explain or analyze them.
Goal	To understand the underlying causes, relationships, and patterns in data or phenomena.	To provide an accurate and objective description of data or phenomena.
Approach	Uses logical reasoning, critical thinking, and data analysis techniques.	Relies on observation, measurement, and data collection.
Focus	Emphasizes on the "why" and "how" questions.	Emphasizes on the "what" questions.
Subjectivity	Objective approach, minimizing personal bias.	Subjective approach, influenced by personal interpretation.
Examples	Statistical analysis, data mining, hypothesis testing.	Surveys, observations, case studies.

Further Detail

Introduction.

When it comes to research and data analysis, two common approaches are analytical and descriptive methods. Both methods have their own unique attributes and serve different purposes in understanding and interpreting data. In this article, we will explore the characteristics of analytical and descriptive approaches, highlighting their strengths and limitations.

Analytical Approach

The analytical approach focuses on breaking down complex problems or datasets into smaller components to gain a deeper understanding of the underlying patterns and relationships. It involves the use of logical reasoning, critical thinking, and statistical techniques to examine data and draw conclusions. The primary goal of the analytical approach is to uncover insights, identify trends, and make predictions based on the available information.

One of the key attributes of the analytical approach is its emphasis on hypothesis testing. Researchers using this method formulate hypotheses based on existing theories or observations and then collect and analyze data to either support or refute these hypotheses. By systematically testing different variables and their relationships, the analytical approach allows researchers to make evidence-based claims and draw reliable conclusions.

Another important attribute of the analytical approach is its reliance on quantitative data. This method often involves the use of statistical tools and techniques to analyze numerical data, such as surveys, experiments, or large datasets. By quantifying variables and measuring their relationships, the analytical approach provides a rigorous and objective framework for data analysis.

Furthermore, the analytical approach is characterized by its focus on generalizability. Researchers using this method aim to draw conclusions that can be applied to a broader population or context. By using representative samples and statistical inference, the analytical approach allows researchers to make inferences about the larger population based on the analyzed data.

However, it is important to note that the analytical approach has its limitations. It may overlook important contextual factors or qualitative aspects of the data that cannot be easily quantified. Additionally, the analytical approach requires a strong understanding of statistical concepts and techniques, making it more suitable for researchers with a background in quantitative analysis.

Descriptive Approach

The descriptive approach, on the other hand, focuses on summarizing and presenting data in a meaningful and informative way. It aims to provide a clear and concise description of the observed phenomena or variables without necessarily seeking to establish causal relationships or make predictions. The primary goal of the descriptive approach is to present data in a manner that is easily understandable and interpretable.

One of the key attributes of the descriptive approach is its emphasis on data visualization. Researchers using this method often employ charts, graphs, and other visual representations to present data in a visually appealing and accessible manner. By using visual aids, the descriptive approach allows for quick and intuitive understanding of the data, making it suitable for a wide range of audiences.

Another important attribute of the descriptive approach is its flexibility in dealing with different types of data. Unlike the analytical approach, which primarily focuses on quantitative data, the descriptive approach can handle both quantitative and qualitative data. This makes it particularly useful in fields where subjective opinions, narratives, or observations play a significant role.

Furthermore, the descriptive approach is characterized by its attention to detail. Researchers using this method often provide comprehensive descriptions of the variables, including their distribution, central tendency, and variability. By presenting detailed summaries, the descriptive approach allows for a thorough understanding of the data, enabling researchers to identify patterns or trends that may not be immediately apparent.

However, it is important to acknowledge that the descriptive approach has its limitations as well. It may lack the rigor and statistical power of the analytical approach, as it does not involve hypothesis testing or inferential statistics. Additionally, the descriptive approach may be more subjective, as the interpretation of the data relies heavily on the researcher's judgment and perspective.

In conclusion, the analytical and descriptive approaches have distinct attributes that make them suitable for different research purposes. The analytical approach emphasizes hypothesis testing, quantitative data analysis, and generalizability, allowing researchers to draw evidence-based conclusions and make predictions. On the other hand, the descriptive approach focuses on data visualization, flexibility in handling different data types, and attention to detail, enabling researchers to present data in a clear and concise manner. Both approaches have their strengths and limitations, and the choice between them depends on the research objectives, available data, and the researcher's expertise. By understanding the attributes of each approach, researchers can make informed decisions and employ the most appropriate method for their specific research needs.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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.

Descriptive Writing	Analytical writing
States what happened (the event).	Explain what the impact of the event was (especially in relation to the research question/s).
Explains what a theory says.	Explains how this is relevant to the key issue(s) and research question(s).
Notes the methods used.	Explains whether these methods were relevant or not.
States what time/date something happened.	Explains why the timing is important/relevant.
Explains how something works.	Explains whether and why this is positive or negative.
Provides various pieces of information.	Draws a conclusion in relation to the various pieces of information.

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 ...

22 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

Brilliantly explained – thank you.

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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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Descriptive vs Analytical

Explaining & comparing both methods, descriptive research.

Descriptive research is defined as a research method that describes the characteristics of the population or phenomenon that is being studied. This methodology focuses more on the “what” of the research subject rather than the “why” of the research subject.

In other words, descriptive research primarily focuses on describing the nature of a demographic segment, without focusing on “why” a certain phenomenon occurs. That means, it “describes” the subject of the research, without covering “why” it happens.

Types of Descriptive Research

Naturalistic observation.

Naturalistic observation is, in contrast to analog observation, a research tool in which a subject is observed in its natural habitat without any manipulation by the observer. During naturalistic observation, researchers take great care to avoid interfering with the behavior they are observing by using unobtrusive methods.

Naturalistic observation involves two main differences that set it apart from other forms of data gathering. In the context of a naturalistic observation, the environment is in no way being manipulated by the observer nor was it created by the observer.

The essence of survey research can be explained as “questioning individuals on a topic or topics and then describing their responses”. Survey research is often used to assess thoughts, opinions, and feelings. Surveys can be specific and limited, or they can have more global, widespread goals.

Case Studies

A case study is a research method involving an up-close, in-depth, and detailed examination of a subject of study (the case), as well as its related contextual conditions. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.

Analytical Research

In Analytical Research, the researcher has to use facts or information already available, and analyze them to make a critical evaluation of the material.

It involves the in-depth study and evaluation of available information in an attempt to explain complex phenomenon.

Analytical Researches primarily concerned with testing hypothesis and specifying and interpreting relationships, by analyzing the facts or information already available.

Types of Analytical Research

Historical research.

It is the study of past records and other information sources, with a view to find the origin and development of a phenomenon and to discover the trends in the past, in order to understand the present and to anticipate the future.

Philosophical Research

It is the research of the fundamental nature of knowledge, reality and existence. It is the research of the theoretical basis of a branch of knowledge or experience.

It is the process of a formal assessment of a research with the intention of instituting or making any change in it if necessary.

Research Synthesis

It is the process through which two or more research studies are assessed with the objective of summarizing the evidence relating to a particular question.

More Informative Resources

• DESCRIPTIVE RESEARCH DESIGN
• DESCRIPTIVE RESEARCH DEFINITION
• OVERVIEW OF DESCRIPTIVE RESEARCH
• WHAT IS ANALYTICAL RESEARCH?
• DESCRIPTIVE AND ANALYTICAL RESEARCH
• ANALYTICAL RESEARCH FORUM 2018 (ARF18)

Descriptive Research Explained

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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 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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• Introduction

Data collection

data analysis

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• Academia - Data Analysis
• U.S. Department of Health and Human Services - Office of Research Integrity - Data Analysis
• Chemistry LibreTexts - Data Analysis
• IBM - What is Exploratory Data Analysis?
• Table Of Contents

data analysis , the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data , generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making . Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research . With the rise of “ big data ,” the storage of vast quantities of data in large databases and data warehouses, there is increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

Datasets are collections of information. Generally, data and datasets are themselves collected to help answer questions, make decisions, or otherwise inform reasoning. The rise of information technology has led to the generation of vast amounts of data of many kinds, such as text, pictures, videos, personal information, account data, and metadata, the last of which provide information about other data. It is common for apps and websites to collect data about how their products are used or about the people using their platforms. Consequently, there is vastly more data being collected today than at any other time in human history. A single business may track billions of interactions with millions of consumers at hundreds of locations with thousands of employees and any number of products. Analyzing that volume of data is generally only possible using specialized computational and statistical techniques.

The desire for businesses to make the best use of their data has led to the development of the field of business intelligence , which covers a variety of tools and techniques that allow businesses to perform data analysis on the information they collect.

For data to be analyzed, it must first be collected and stored. Raw data must be processed into a format that can be used for analysis and be cleaned so that errors and inconsistencies are minimized. Data can be stored in many ways, but one of the most useful is in a database . A database is a collection of interrelated data organized so that certain records (collections of data related to a single entity) can be retrieved on the basis of various criteria . The most familiar kind of database is the relational database , which stores data in tables with rows that represent records (tuples) and columns that represent fields (attributes). A query is a command that retrieves a subset of the information in the database according to certain criteria. A query may retrieve only records that meet certain criteria, or it may join fields from records across multiple tables by use of a common field.

Frequently, data from many sources is collected into large archives of data called data warehouses. The process of moving data from its original sources (such as databases) to a centralized location (generally a data warehouse) is called ETL (which stands for extract , transform , and load ).

• The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
• The transformation step is the process of cleaning the data so that they fit the analytical need for the data and the schema of the data warehouse. This may involve changing formats for certain fields, removing duplicate records, or renaming fields, among other processes.
• Finally, the clean data are loaded into the data warehouse, where they may join vast amounts of historical data and data from other sources.

After data are effectively collected and cleaned, they can be analyzed with a variety of techniques. Analysis often begins with descriptive and exploratory data analysis. Descriptive data analysis uses statistics to organize and summarize data, making it easier to understand the broad qualities of the dataset. Exploratory data analysis looks for insights into the data that may arise from descriptions of distribution, central tendency, or variability for a single data field. Further relationships between data may become apparent by examining two fields together. Visualizations may be employed during analysis, such as histograms (graphs in which the length of a bar indicates a quantity) or stem-and-leaf plots (which divide data into buckets, or “stems,” with individual data points serving as “leaves” on the stem).

Data analysis frequently goes beyond descriptive analysis to predictive analysis, making predictions about the future using predictive modeling techniques. Predictive modeling uses machine learning , regression analysis methods (which mathematically calculate the relationship between an independent variable and a dependent variable), and classification techniques to identify trends and relationships among variables. Predictive analysis may involve data mining , which is the process of discovering interesting or useful patterns in large volumes of information. Data mining often involves cluster analysis , which tries to find natural groupings within data, and anomaly detection , which detects instances in data that are unusual and stand out from other patterns. It may also look for rules within datasets, strong relationships among variables in the data.

Overview of Analytic Studies

Introduction

We search for the determinants of health outcomes, first, by relying on descriptive epidemiology to generate hypotheses about associations between exposures and outcomes. Analytic studies are then undertaken to test specific hypotheses. Samples of subjects are identified and information about exposure status and outcome is collected. The essence of an analytic study is that groups of subjects are compared in order to estimate the magnitude of association between exposures and outcomes.

In their book entitled "Epidemiology Matters" Katherine Keyes and Sandro Galea discuss three fundamental options for studying samples from a population as illustrated in the video below (duration 8:30).

Learning Objectives

After successfully completing this section, the student will be able to:

• Describe the difference between descriptive and scientific/analytic epidemiologic studies in terms of information/evidence provided for medicine and public health.
• Define and explain the distinguishing features of a cohort study.
• Describe and identify the types of epidemiologic questions that can be addressed by cohort studies.
• Define and distinguish among prospective and retrospective cohort studies using the investigator as the point of reference.  
• Define and explain the distinguishing features of a case-control study.
• Explain the distinguishing features of an intervention study.
• Identify the study design when reading an article or abstract.

Cohort Type Studies

A cohort is a "group." In epidemiology a cohort is a group of individuals who are followed over a period of time, primarily to assess what happens to them, i.e., their health outcomes. In cohort type studies one identifies individuals who do not have the outcome of interest initially, and groups them in subsets that differ in their exposure to some factor, e.g., smokers and non-smokers. The different exposure groups are then followed over time in order to compare the incidence of health outcomes, such as lung cancer or heart disease. As an example, the Framingham Heart Study enrolled a cohort of 5,209 residents of Framingham, MA who were between the ages of 30-62 and who did not have cardiovascular disease when they were enrolled. These subjects differed from one another in many ways: whether they smoked, how much they smoked, body mass index, eating habits, exercise habits, gender, family history of heart disease, etc. The researchers assessed these and many other characteristics or "exposures" soon after the subjects had been enrolled and before any of them had developed cardiovascular disease. The many "baseline characteristics" were assessed in a number of ways including questionnaires, physical exams, laboratory tests, and imaging studies (e.g., x-rays). They then began "following" the cohort, meaning that they kept in contact with the subjects by phone, mail, or clinic visits in order to determine if and when any of the subjects developed any of the "outcomes of interest," such as myocardial infarction (heart attack), angina, congestive heart failure, stroke, diabetes and many other cardiovascular outcomes.

Over time some subjects eventually began to develop some of the outcomes of interest. Having followed the cohort in this fashion, it was eventually possible to use the information collected to evaluate many hypotheses about what characteristics were associated with an increased risk of heart disease. For example, if one hypothesized that smoking increased the risk of heart attacks, the subjects in the cohort could be sorted based on their smoking habits, and one could compare the subset of the cohort that smoked to the subset who had never smoked. For each such comparison that one wanted to make the cohort could be grouped according to whether they had a given exposure or not, and one could measure and compare the frequency of heart attacks (i.e., the incidence) between the groups. Incidence provides an estimate of risk, so if the incidence of heart attacks is 3 times greater in smokers compared to non-smokers, it suggests an association between smoking and risk of developing a heart attack. (Various biases might also be an explanation for an apparent association. We will learn about these later in the course.) The hallmark of analytical studies, then, is that they collect information about both exposure status and outcome status, and they compare groups to identify whether there appears to be an association or a link.

The Population "At Risk"

From the discussion above, it should be obvious that one of the basic requirements of a cohort type study is that none of the subjects have the outcome of interest at the beginning of the follow-up period, and time must pass in order to determine the frequency of developing the outcome.

• For example, if one wanted to compare the risk of developing uterine cancer between postmenopausal women receiving hormone-replacement therapy and those not receiving hormones, one would consider certain eligibility criteria for the members prior to the start of the study: 1) they should be female, 2) they should be post-menopausal, and 3) they should have a uterus. Among post-menopausal women there might be a number who had had a hysterectomy already, perhaps for persistent bleeding problems or endometriosis. Since these women no longer have a uterus, one would want to exclude them from the cohort, because they are no longer at risk of developing this particular type of cancer.
• Similarly, if one wanted to compare the risk of developing diabetes among nursing home residents who exercised and those who did not, it would be important to test the subjects for diabetes at the beginning of the follow-up period in order to exclude all subjects who already had diabetes and therefore were not "at risk" of developing diabetes.

Eligible subjects have to meet certain criteria to be included as subjects in a study (inclusion criteria). One of these would be that they did not have any of the diseases or conditions that the investigators want to study, i.e., the subjects must be "at risk," of developing the outcome of interest, and the members of the cohort to be followed are sometimes referred to as "the population at risk."

However, at times decisions about who is "at risk" and eligible get complicated.

Example #1: Suppose the outcome of interest is development of measles. There may be subjects who:

• Already were known to have had clinically apparent measles and are immune to subsequent measles infection
• Had sub-clinical cases of measles that went undetected (but the subject may still be immune)
• Had a measles vaccination that conferred immunity
• Had a measles vaccination that failed to confer immunity

In this case the eligibility criteria would be shaped by the specific scientific questions being asked. One might want to compare subjects known to have had clinically apparent measles to those who had not had clinical measles and had not had a measles vaccination. Or, one could take blood sample from all potential subjects in order to measure their antibody titers (levels) to the measles virus.

Example #2: Suppose you are studying an event that can occur more that once, such as a heart attack. Again, the eligibility criteria should be shaped to fit the scientific questions that are being answered. If one were interested in the risk of a first myocardial infarction, then obviously subjects who had already had a heart attack would not be eligible for study. On the other hand, if one were interested in tertiary prevention of heart attacks, the study cohort would include people who had had heart attacks or other clinical manifestations of heart disease, and the outcome of interest would be subsequent significant cardiac events or death. 

Prospective and Retrospective Cohort Studies

Cohort studies can be classified as prospective or retrospective based on when outcomes occurred in relation to the enrollment of the cohort.

Prospective Cohort Studies

In a prospective study like the Nurses Health Study baseline information is collected from all subjects in the same way using exactly the same questions and data collection methods for all subjects. The investigators design the questions and data collection procedures carefully in order to obtain accurate information about exposures before disease develops in any of the subjects. After baseline information is collected, subjects in a prospective cohort study are then followed "longitudinally," i.e. over a period of time, usually for years, to determine if and when they become diseased and whether their exposure status changes. In this way, investigators can eventually use the data to answer many questions about the associations between "risk factors" and disease outcomes. For example, one could identify smokers and non-smokers at baseline and compare their subsequent incidence of developing heart disease. Alternatively, one could group subjects based on their body mass index (BMI) and compare their risk of developing heart disease or cancer.

Key Concept: The distinguishing feature of a prospective cohort study is that at the time that the investigators begin enrolling subjects and collecting baseline exposure information, none of the subjects has developed any of the outcomes of interest.

 Examples of Prospective Cohort Studies

• The Framingham Heart Study Home Page
• The Nurses Health Study Home Page

Pitfall: Note that in these prospective cohort studies a comparison of incidence between the groups can only take place after enough time has elapsed so that some subjects developed the outcomes of interest. Since the data analysis occurs after some outcomes have occurred, some students mistakenly would call this a retrospective study, but this is incorrect. The analysis always occurs after a certain number of events have taken place. The characteristic that distinguishes a study as prospective is that the subjects were enrolled, and baseline data was collected before any subjects developed an outcome of interest.

Retrospective Cohort Studies

In contrast, retrospective studies are conceived after some people have already developed the outcomes of interest. The investigators jump back in time to identify a cohort of individuals at a point in time before they have developed the outcomes of interest, and they try to establish their exposure status at that point in time. They then determine whether the subject subsequently developed the outcome of interest.

Suppose investigators wanted to test the hypothesis that working with the chemicals involved in tire manufacturing increases the risk of death. Since this is a fairly rare exposure, it would be advantageous to use a special exposure cohort such as employees of a large tire manufacturing factory. The employees who actually worked with chemicals used in the manufacturing process would be the exposed group, while clerical workers and management might constitute the "unexposed" group. However, rather than following these subjects for decades, it would be more efficient to use employee health and employment records over the past two or three decades as a source of data. In essence, the investigators are jumping back in time to identify the study cohort at a point in time before the outcome of interest (death) occurred. They can classify them as "exposed" or "unexposed" based on their employment records, and they can use a number of sources to determine subsequent outcome status, such as death (e.g., using health records, next of kin, National Death Index, etc.).

Key Concept: The distinguishing feature of a retrospective cohort study is that the investigators conceive the study and begin identifying and enrolling subjects .

Retrospective cohort studies like the one described above are very efficient for studying rare or unusual exposures, but there are many potential problems here. Sometimes exposure status is not clear when it is necessary to go back in time and use whatever data is available, especially because the data being used was not designed to answer a health question. Even if it was clear who was exposed to tire manufacturing chemicals based on employee records, it would also be important to take into account (or adjust for) other differences that could have influenced mortality, i.e., confounding factors. For example, it might be important to know whether the subjects smoked, or drank, or what kind of diet they ate. However, it is unlikely that a retrospective cohort study would have accurate information on these many other risk factors.

The video below provides a brief (7:31) explanation of the distinction between retrospective and prospective cohort studies.

Link to a transcript of the video

Intervention Studies (Clinical Trials)

Intervention studies (clinical trials) are experimental research studies that compare the effectiveness of medical treatments, management strategies, prevention strategies, and other medical or public health interventions. Their design is very similar to that of a prospective cohort study. However, in cohort studies exposure status is determined by genetics, self-selection, or life circumstances, and the investigators just observe differences in outcome between those who have a given exposure and those who do not. In clinical trials  exposure status  (the treatment type)  is assigned by the investigators . Ideally, assignment of subjects to one of the comparison groups should be done randomly in order to produce equal distributions of potentially confounding factors. Sometimes a group receiving a new treatment is compared to an untreated group, or a group receiving a placebo or a sham treatment. Sometimes, a new treatment is compared to an untreated group or to a group receiving an established treatment. For more on this topic see the module on Intervention Studies.

In summary, the characteristic that distinguishes a clinical trial from a cohort study is that the investigator assigns the exposure status in a clinical trial, while subjects' genetics, behaviors, and life circumstances determine their exposures in a cohort study.

Key Concept: Common features of both prospective and retrospective cohort studies.

Summarizing Data in a Cohort Study

Investigators often use contingency tables to summarize data. In essence, the table is a matrix that displays the combinations of exposure and outcome status. If one were summarizing the results of a study with two possible exposure categories and two possible outcomes, one would use a "two by two" table in which the numbers in the four cells indicate the number of subjects within each of the 4 possible categories of risk and disease status.

For example, consider data from a retrospective cohort study conducted by the Massachusetts Department of Public Health (MDPH) during an investigation of an outbreak of Giardia lamblia in Milton, MA in 2003. The descriptive epidemiology indicated that almost all of the cases belonged to a country club in Milton. The club had an adult swimming pool and a wading pool for toddlers, and the investigators suspected that the outbreak may have occurred when an infected child with a dirty diaper contaminated the water in the kiddy pool. This hypothesis was tested by conducting a retrospective cohort study. The cases of Giardia lamblia had already occurred and had been reported to MDPH via the infectious disease surveillance system (for more information on surveillance, see the Surveillance module). The investigation focused on an obvious cohort - 479 members of the country club who agreed to answer the MDPH questionnaire. The questionnaire asked, among many other things, whether the subject had been exposed to the kiddy pool. The incidence of subsequent Giardia infection was then compared between subjects who been exposed to the kiddy pool and those who had not.

The table below summarizes the findings. A total of 479 subjects completed the questionnaire, and 124 of them indicated that they had been exposed to the kiddy pool. Of these, 16 subsequently developed Giardia infection, but 108 did not. Among the 355 subjects who denied kiddy pool exposure, 14 developed Giardia infection, and the other 341 did not.

	16	108	124	16/124 = 12.9%
	14	341	365	14/365 = 3,9%

 Organization of the data this way makes it easier to compute the cumulative incidence in each group (12.9% and 3.9% respectively). The incidence in each group provides an estimate of risk, and the groups can be compared in order to estimate the magnitude of association. (This will be addressed in much greater detail in the module on Measures of Association.) One way of quantifying the association is to calculate the relative risk, i.e., dividing the incidence in the exposed group by the incidence in the unexposed group). In this case, the risk ratio is (12.9% / 3.9%) = 3.3. This suggest that subjects who swam in the kiddy pool had 3.3 times the risk of getting Giardia infections compared to those who did not, suggesting that the kiddy pool was the source.

Unanswered Questions

If the kiddy pool was the source of contamination responsible for this outbreak, why was it that:

• Only 16 people exposed to the kiddy pool developed the infection?
• There were 14 Giardia cases among people who denied exposure to the kiddy pool?

Before you look at the answer, think about it and try to come up with a possible explanation.

Likely Explanation

Optional Links of Potential Interest

Link to the 2003 Giardia outbreak

Link to CDC page on Organizing Data

Possible Pitfall: Contingency tables can be oriented in several ways, and this can cause confusion when calculating measures of association.

There is no standard rule about how to set up contingency tables, and you will see them set up in different ways.

• With exposure status in rows and outcome status in columns
• With exposure status in columns and outcome status in rows
• With exposed group first followed by non-exposed group
• With non-exposed group first followed by exposed group

If you aren't careful, these different orientations can result in errors in calculating measures of association. One way to avoid confusion is to always set up your contingency tables in the same way. For example, in these learning modules the contingency tables almost always indicate outcome status in columns listing subjects who have the outcome of interest to the left of subjects who do not have the outcome, and exposure status of the exposed (or most exposed) group is listed in a row above those who are unexposed (or have less exposure).

The table below illustrates this arrangement.

	Those With the Outcome	Those Without the Outcome	Total
Exposed (or most exposed)
Non-exposed (or least exposed)

Case-Control Studies

Cohort studies have an intuitive logic to them, but they can be very problematic when:

• The outcomes being investigated are rare;
• There is a long time period between the exposure of interest and the development of the disease; or
• It is expensive or very difficult to obtain exposure information from a cohort.

In the first case, the rarity of the disease requires enrollment of very large numbers of people. In the second case, the long period of follow-up requires efforts to keep contact with and collect outcome information from individuals. In all three situations, cost and feasibility become an important concern.

A case-control design offers an alternative that is much more efficient. The goal of a case-control study is the same as that of cohort studies, i.e. to estimate the magnitude of association between an exposure and an outcome. However, case-control studies employ a different sampling strategy that gives them greater efficiency.   As with a cohort study, a case-control study attempts to identify all people who have developed the disease of interest in the defined population. This is not because they are inherently more important to estimating an association, but because they are almost always rarer than non-diseased individuals, and one of the requirements of accurate estimation of the association is that there are reasonable numbers of people in both the numerators (cases) and denominators (people or person-time) in the measures of disease frequency for both exposed and reference groups. However, because most of the denominator is made up of people who do not develop disease, the case-control design avoids the need to collect information on the entire population by selecting a sample of the underlying population.

Rothman describes the case-control strategy as follows: 

"Case-control studies are best understood by considering as the starting point a , which represents a hypothetical study population in which a cohort study might have been conducted. The is the population that gives rise to the cases included in the study. If a cohort study were undertaken, we would define the exposed and unexposed cohorts (or several cohorts) and from these populations obtain denominators for the incidence rates or risks that would be calculated for each cohort. We would then identify the number of cases occurring in each cohort and calculate the risk or incidence rate for each. In a case-control study the same cases are identified and classified as to whether they belong to the exposed or unexposed cohort. Instead of obtaining the denominators for the rates or risks, however, a control group is sampled from the entire source population that gives rise to the cases. Individuals in the control group are then classified into exposed and unexposed categories. The purpose of the control group is to determine the relative size of the exposed and unexposed components of the source population."

To illustrate this consider the following hypothetical scenario in which the source population is Plymouth County in Massachusetts, which has a total population of 6,647 (hypothetical). Thirteen people in the county have been diagnosed with an unusual disease and seven of them have a particular exposure that is suspected of being an important contributing factor. The chief problem here is that the disease is quite rare.

If I somehow had exposure and outcome information on all of the subjects in the source population and looked at the association using a cohort design, it might look like this:

	Diseased	Non-diseased	Total
Exposed	7	1,000	1,007
Non-exposed	6	5,634	5,640

Therefore, the incidence in the exposed individuals would be 7/1,007 = 0.70%, and the incidence in the non-exposed individuals would be 6/5,640 = 0.11%. Consequently, the risk ratio would be 0.70/0.11=6.52, suggesting that those who had the risk factor (exposure) had 6.5 times the risk of getting the disease compared to those without the risk factor. This is a strong association.

In this hypothetical example, I had data on all 6,647 people in the source population, and I could compute the probability of disease (i.e., the risk or incidence) in both the exposed group and the non-exposed group, because I had the denominators for both the exposed and non-exposed groups.

The problem , of course, is that I usually don't have the resources to get the data on all subjects in the population. If I took a random sample of even 5-10% of the population, I might not have any diseased people in my sample.

An alternative approach would be to use surveillance databases or administrative databases to find most or all 13 of the cases in the source population and determine their exposure status. However, instead of enrolling all of the other 5,634 residents, suppose I were to just take a sample of the non-diseased population. In fact, suppose I only took a sample of 1% of the non-diseased people and I then determined their exposure status. The data might look something like this:

	Diseased	Non-diseased	Total
Exposed	7	10	unknown
Non-exposed	6	56	unknown

With this sampling approach I can no longer compute the probability of disease in each exposure group, because I no longer have the denominators in the last column. In other words, I don't know the exposure distribution for the entire source population. However, the small control sample of non-diseased subjects gives me a way to estimate the exposure distribution in the source population. So, I can't compute the probability of disease in each exposure group, but I can compute the odds of disease in the case-control sample.

The Odds Ratio

The odds of disease among the exposed sample are 7/10, and the odds of disease in the non-exposed sample are 6/56. If I compute the odds ratio, I get (7/10) / (5/56) = 6.56, very close to the risk ratio that I computed from data for the entire population. We will consider odds ratios and case-control studies in much greater depth in a later module. However, for the time being the key things to remember are that:

• The sampling strategy for a case-control study is very different from that of cohort studies, despite the fact that both have the goal of estimating the magnitude of association between the exposure and the outcome.
• In a case-control study there is no "follow-up" period. One starts by identifying diseased subjects and determines their exposure distribution; one then takes a sample of the source population that produced those cases in order to estimate the exposure distribution in the overall source population that produced the cases. [In cohort studies none of the subjects have the outcome at the beginning of the follow-up period.]
• In a case-control study, you cannot measure incidence, because you start with diseased people and non-diseased people, so you cannot calculate relative risk.
• The case-control design is very efficient. In the example above the case-control study of only 79 subjects produced an odds ratio (6.56) that was a very close approximation to the risk ratio (6.52) that was obtained from the data in the entire population.
• Case-control studies are particularly useful when the outcome is rare is uncommon in both exposed and non-exposed people.

The Difference Between "Probability" and "Odds"?

• The odds are defined as the probability that the event will occur divided by the probability that the event will not occur.

If the probability of an event occurring is Y, then the probability of the event not occurring is 1-Y. (Example: If the probability of an event is 0.80 (80%), then the probability that the event will not occur is 1-0.80 = 0.20, or 20%.

The odds of an event represent the ratio of the (probability that the event will occur) / (probability that the event will not occur). This could be expressed as follows:

Odds of event = Y / (1-Y)

So, in this example, if the probability of the event occurring = 0.80, then the odds are 0.80 / (1-0.80) = 0.80/0.20 = 4 (i.e., 4 to 1).

• If a race horse runs 100 races and wins 25 times and loses the other 75 times, the probability of winning is 25/100 = 0.25 or 25%, but the odds of the horse winning are 25/75 = 0.333 or 1 win to 3 loses.
• If the horse runs 100 races and wins 5 and loses the other 95 times, the probability of winning is 0.05 or 5%, and the odds of the horse winning are 5/95 = 0.0526.
• If the horse runs 100 races and wins 50, the probability of winning is 50/100 = 0.50 or 50%, and the odds of winning are 50/50 = 1 (even odds).
• If the horse runs 100 races and wins 80, the probability of winning is 80/100 = 0.80 or 80%, and the odds of winning are 80/20 = 4 to 1.

NOTE that when the probability is low, the odds and the probability are very similar.

On Sept. 8, 2011 the New York Times ran an article on the economy in which the writer began by saying "If history is a guide, the odds that the American economy is falling into a double-dip recession have risen sharply in recent weeks and may even have reached 50 percent." Further down in the article the author quoted the economist who had been interviewed for the story. What the economist had actually said was, "Whether we reach the technical definition [of a double-dip recession] I think is probably close to 50-50."

Question: Was the author correct in saying that the "odds" of a double-dip recession may have reached 50 percent?

Key Concept: In a study that is designed and conducted as a case-control study, you cannot calculate incidence. Therefore, you cannot calculate risk ratio or risk difference. You can only calculate an odds ratio. However, in certain situations a case-control study is the only feasible study design.

Which Study Design Is Best?

Decisions regarding which study design to use rest on a number of factors including::

• Uncommon Outcome: If the outcome of interest is uncommon or rare, a case-control study would usually be best.
• Uncommon Exposure: When studying an uncommon exposure, the investigators need to enroll an adequate number of subjects who have that exposure. In this situation a cohort study is best.
• Ethics of Assigning Subjects to an Exposure: If you wanted to study the association between smoking and lung cancer, It wouldn't be ethical to conduct a clinical trial in which you randomly assigned half of the subjects to smoking.
• Resources: If you have limited time, money, and personnel to gather data, it is unlikely that you will be able to conduct a prospective cohort study. A case-control study or a retrospective cohort study would be better options. The best one to choose would be dictated by whether the outcome was rare or the exposure of interest was rare.

There are some situations in which more than one study design could be used.

Smoking and Lung Cancer: For example, when investigators first sought to establish whether there was a link between smoking and lung cancer, they did a study by finding hospital subjects who had lung cancer and a comparison group of hospital patients who had diseases other than cancer. They then compared the prior exposure histories with respect to smoking and many other factors. They found that past smoking was much more common in the lung cancer cases, and they concluded that there was an association. The advantages to this approach were that they were able to collect the data they wanted relatively quickly and inexpensively, because they started with people who already had the disease of interest.

The short video below provides a nice overview of epidemiological studies.

However, there were several limitations to the study they had done. The study design did not allow them to measure the incidence of lung cancer in smokers and non-smokers, so they couldn't measure the absolute risk of smoking. They also didn't know what other diseases smoking might be associated with, and, finally, they were concerned about some of the biases that can creep into this type of study.

As a result, these investigators then initiated another study. They invited all of the male physicians in the United Kingdom to fill out questionnaires regarding their health status and their smoking status. They then focused on the healthy physicians who were willing to participate, and the investigators mailed follow-up questionnaires to them every few years. They also had a way of finding out the cause of death for any subjects who became ill and died. The study continued for about 50 years. Along the way the investigators periodically compared the incidence of death among non-smoking physicians and physicians who smoked small, moderate or heavy amounts of tobacco.

These studies were useful, because they were able to demonstrate that smokers had an increased risk of over 20 different causes of death. They were also able to measure the incidence of death in different categories, so they knew the absolute risk for each cause of death. Of course, the downside to this approach was that it took a long time, and it was very costly. So, both a case-control study and a prospective cohort study provided useful information about the association between smoking and lung cancer and other diseases, but there were distinct advantages and limitations to each approach. 

Hepatitis Outbreak in Marshfield, MA

In 2004 there was an outbreak of hepatitis A on the South Shore of Massachusetts. Over a period of a few weeks there were 20 cases of hepatitis A that were reported to the MDPH, and most of the infected persons were residents of Marshfield, MA. Marshfield's health department requested help in identifying the source from MDPH. The investigators quickly performed descriptive epidemiology. The epidemic curve indicated a point source epidemic, and most of the cases lived in the Marshfield area, although some lived as far away as Boston. They conducted hypothesis-generating interviews, and taken together, the descriptive epidemiology suggested that the source was one of five or six food establishments in the Marshfield area, but it wasn't clear which one. Consequently, the investigators wanted to conduct an analytic study to determine which restaurant was the source. Which study design should have been conducted? Think about the scenario, and then open the "Quiz Me" below and choose your answer.

Link to more on the hepatitis outbreak

Case-control studies are particularly efficient for rare diseases because they begin by identifying a sufficient number of diseased people (or people have some "outcome" of interest) to enable you to do an analysis that tests associations. Case-control studies can be done in just about any circumstance, but they are particularly useful when you are dealing with rare diseases or disease for which there is a very long latent period, i.e. a long time between the causative exposure and the eventual development of disease. 

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What Is Analytical Research?

Analytical research is a specific type of research that involves critical thinking skills and the evaluation of facts and information relative to the research being conducted. A variety of people including students, doctors and psychologists use analytical research during studies to find the most relevant information. From analytical research, a person finds out critical details to add new ideas to the material being produced.

Research of any type is a method to discover information. Within analytical research articles, data and other important facts that pertain to a project is compiled; after the information is collected and evaluated, the sources are used to prove a hypothesis or support an idea. Using critical thinking skills (a method of thinking that involves identifying a claim or assumption and deciding if it is true or false) a person is able to effectively pull out small details to form greater assumptions about the material.

Some researchers conduct analytical research to find supporting evidence to current research being done in order to make the work more reliable. Other researchers conduct analytical research to form new ideas about the topic being studied. Analytical research is conducted in a variety of ways including literary research, public opinion, scientific trials and Meta-analysis.

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Economics 145                                                                                                                 Prof. Yang

Chapter 1 Basic Concepts of Research in Economics

In the first section, we start with the definition of various terms relating to research. Terms to be discussed are ‘research’, ‘research methods’ and ‘research methodology’ and, finally, a brief discussion of various types of research. In the second section, we will discuss what is economics and what economists do. This discussion is presented at the outset to illustrate and highlight various skills needed to carry out economic analysis. What is attempted here is to highlight the importance and interdependence of economic theory and measurement in the study of economics. [Further discussion of ‘theory without measurement’ versus ‘measurement without theory’ comes here]. In the third section, we introduce basic concepts of research. As is well known, the method of research or analysis economists use in carrying out their task is the scientific method, which is used in all of science. Therefore, it is important to discuss science in general and its method, namely scientific method. We should note, however, that there is no such a thing as scientific method, because there are many variations. Scientific method essentially refers to the general or generalized process called the "scientific approach" to obtaining new and reliable knowledge. What we attempt to do in this section is to discuss the key terms and concepts of the scientific method, before we delve into research procedures in the next chapter. These concepts include theory and model, variables, assumptions, parameters, the hypothesis, and the testing of hypothesis among others.

The term "research" is often loosely defined and thus used in a similar way. This unfortunate development results from a misconception about what is research. To properly understand what is research, it is good to start with common misconceptions about research. First, fact transferal is not research. Consider a typical high school research project. The teacher assigns a "research project" on some topic. The students went to the library, checked out several books, and might have copied several pertinent pages from the book. The typical student organized collected information and wrote up the "research report". What these students did is information gathering and organization; it is nothing more or nothing less. No doubt the student went through some motions associated with research. But finding fact and fact transferal alone is not research. Transfer of information from one source, namely books and pertinent pages, to another source, namely the so-called research report, is nothing more than fact transferal, but not research. To my distress I find many college students repeat this same mistake by submitting a ‘research report’ which is nothing but fact transferal from one source to their report. A second misconception about research is that research is related to laboratory research (for example, in chemistry or biology in the natural sciences). When people hear term the "research", they often conjure up this image. But research is not limited to certain fields of study; it is characterized by the methods used.

What, then, is research? (1996?) defines research as "studious inquiry or examination; : investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts, or practical application of such new or revised theories or laws".

Research methods provide the specific details of how one accomplishes a research task (procedures and methods); It provides specific and detailed procedures of how to initiate, carry out, and complete a research task by mainly focusing on how to do it. Research methodology deals with general approaches or guidelines to conducting research. It provides the principles for organizing, planning, designing, and conducting research, but it cannot tell you in detail how to conduct a specific, individual research.

In carrying out an applied and quantitative economic research, there are several necessary backgrounds. The researcher should first have solid training in economic theory, quantitative methods(statistics and econometrics), data analysis techniques, and adequate training in micro-computer technology, as well as some training in research methods. Unfortunately, undergraduate students beginning their research most often do not have these backgrounds. Therefore, they are going to be overwhelmed and intimidated by the lack of necessary skills. Many often give up in frustration, even if they are willing to persevere and to learn these skills. What is sorely need is a practical guide to initiate, conduct, and complete an applied and quantitative economic research. One proven and effective way of learning these various skills which economists use is .

: The distinction between basic and applied research is largely by the focus of its application. This distinction comes from basic science vs. applied science. Example: physics and engineering. Basic research focuses on determining or establishing the basic or fundamental relationships within a discipline without paying attention to any practical applications to the real world. In contrast, applied research is usually conducted to solve a particular and concrete problem.

: The distinction between descriptive and analytical research is based on the question it asks. Descriptive research attempts to determine, describe, or identify is, while analytical research attempts to establish it is that way or how it came to be. The descriptive research uses description, classification, measurement, and comparison to describe

what phenomena are. The analytical research usually concerns itself with cause-effect relationships. Examples. Examining the fluctuations of U. S. international trade balance during 1974-1995 is an example of descriptive research; while explaining and U.S. trade balance move in a particular way over time is an example of analytical research. Another example: Starting from late 1986, the value of U.S. dollar value has steadily increased against the Japanese yen and German Mark. Examining the magnitude of this trend in the value of U.S. dollar is another example of descriptive research; while explaining and this surge in the value of the U.S. dollar is occuring. If one attempts to explain and this surge in the value of U.S. dollar is going to affect the U.S. economy,as well as the economies of Japan and Germany, this is another example of analytical research.

: By disciplinary research, we mean research "designed to improve a discipline" as Johnson(1986) defines it. It dwells on theories, relationships, and analytical procedures and techniques within the discipline. Examples: Economic research or social research. By subject-matter research, we mean research "on a subject of interest" within a discipline. Example: Research in resource economics or in international economics. By problem-solving research, we mean research "designed to solve a specific problem for a specific decision maker". It is often multidisciplinary. Example: A multidisciplinary study of on the demand for new mass transit involving economics, sociology, and civil engineering. Or a multidisciplinary study of new medical surgery involving medical doctors, engineers, and an economist.

 

?

Case and Fair define economics as "the study of how individuals and societies choose to use the scarce resources that nature and previous generations have provided" Since it deals with the behavior of human beings and their interactions, it is a social science. Stiglitz also defines it as a social science, but he adds that "it studies the social problem of choice from a scientific viewpoint, which means that it is built on a systematic exploration of the problem of choice". This systematic exploration involves both the formulation of and the examination of to test the validity of theories. This latter definition of economics by Stiglitz is helpful in identifying two major tasks of economists: They are facts and trends in data and and interpreting certain or whole aspect of the economy. From the above definitions of economics, the importance of both theory and measurement is obviously the essential ingredients of meaningful and serious economic research. Theory without measurement is unfruitful and empty; measurement without theory is equally meaningless.

A consists of a set of assumptions (or hypotheses), and conclusions derived from those assumptions. Theories are logical exercises: if the assumptions hold, then the results follow. Examples. When theory is formally presented, it is called model. Examples of model car, ship, or airplane to understand how economists use models. Bamoul and Bliner’s definition of theory: "A theory is a deliberate simplification of relationships whose purpose is to explain how those relationships work. It is an explanation of the mechanism behind observed phenomena. The relationship is usually couched in terms of the relationship between or among variables. What is variable? A variable is anything which varies over time and space; then the measurement of variable in terms of numbers or facts expressed in quantitative terms are data.

The are then quantitative information on various aspects of economics and business. We need to learn how to describe and analyze data. The description of data is one area of statistics: descriptive statistics. Data analysis involves the analysis of statistical data of one variable (univariate distribution). Data analysis also analyzes the relationship between two variables (bivariate distribution). Data analysis may involve the analysis of among more than two variables (multivariate distribution). The presentation of data can either be tabular and graphical. In this part, both the description and computation, illustration, using any easily available computer softwares are to be presented side by side.

In a certain sense, economics is really what economists do. Two major tasks on which economists spend their energies are: First, observing facts and trends in data and, second, explaining the relationship(s) between them in a cause-effect fashion.

A is a verifiable observation or phenomenon. It is an observed fact that retail gasoline prices in California are higher than elsewhere in the U.S. The problem is then to explain why and how this is so. A fact(s) is usually presented in terms of variable(s). A is a quantity of something which varies and the researcher is interested in. There are two types of variables: discrete (or discontinuous) and continuous variables. When a variable(s) is continuous, the researcher can identify a in it. Examples of trend are a linear trend, parabolic or quadratic trend.

There are several publications containing fact and trends of economic variables on the overall U.S. economic conditions. The first is the and the annual publication by the Council of Economic Advisors. Two other well-kown publications on more current economic situations are a monthly publication prepared by the Council of Economic Advisors and , another monthly publication by the Federal Reserve Bank of Cleaveland. To obtain specific information on some specific aspect of the economy, say health care or energy, one needs to consult specialized publications.

Some examples of interesting facts and trends in the US economy during the last two decades are:

@ The Budget deficit and national debt have increased faster than before.

          @ Foreign exchange rates have fluctuated more widely than before.

When a fact or phenomenon is measured (quantified), organized, and presented in a desirable fashion, we call them . Data provide factual information. They are usually regarded as reliable subject to the implicit understanding that there may be some sampling or measurement errors in the process of collection.

To learn how to work with macroeconomics data on the U.S. economy, consider the following set of major economics data on the U. S. economy for the period from 1960 through 1995.

Spread sheet data & How to Use Excel for Data Analysis [Ed’s work]

Measurement of variables in level, first difference, index numbers, percentage changes, per capital measures, converting nominal to real variables, fixed weight vs. chain weight GDP measure, adjustments to data (seasonal adjustments, various moving averages, normalized variables etc. Use of spread sheet (Excel) to manipulate data and graph.

When we examine facts and trend in the variable (s), we often present information in a table or in a graph. When we examine facts and trend, we need to be mindful of the distinction between c vs. between the two variables.

Even though fact or phenomena provides useful factual knowledge, fact alone is not interesting.

Even though facts are interesting, they are not very useful by themselves.

As C.S. Peirce expressed so clearly, "a person can stare stupidly at phenomena; but in the absence of imagination they will not connect themselves together in any rational way"* More interesting and useful are raising analytical questions regarding facts. With the first fact on rapid increase in health-care spending, one can ask and health-care spending increased faster than spending in other sectors? Relating to this question, one can also ask and the price of health-care increased faster than other prices?

Observing another fact, say the large fluctuations in foreign exchange rates, one may ask whether or not, and and a change in the international monetary arrangement might affect the pattern of change in exchange rate. To answer this question, we need to have a measure for various exchange rates. They are bilateral versus multilateral, or the effective exchange rate, and the nominal versus real exchange rate.

Given the first fact, the problem is to explain retail gasoline prices have increased during this particular period and to explain retail gasoline prices in California are substantially higher than in the United States. Explaining the relationship between the share of health care expenditure out of GDP requires the use of the scientific method. Therefore, it is necessary for us to be familiar with concepts of research , such as hypothesis, theory, and model. We also have to learn operational terms of scientific method, such as variables, assumptions, parameters, and functional form.

Explaining and predicting requires the formulation of economic theories as to specific issue under consideration and testing the validity of theories.

**H.R.’s earlier writing on what is a theory and how to do it goes here.

 

Having finished a brief discussion of the meaning of research and related terms, we are now ready to take up the discussion of the scientific method. Let us start by stating that there are essentially two methods of obtaining knowledge: scientific and non-scientific methods. Let us start with non-scientific method.

: The first method of gaining knowledge is through senses, experience, intuition, and revelation, all of these may be classified as non-scientific methods. Some gain knowledge through physical - sight, sound, touch, taste, and smell -, and .

Some knowledge is obtained by senses and experiences. When one gets too close to a fire and gets burned once, he or she gains the knowledge that it is dangerous to be too close to the fire. Other gain knowledge by intuition or revelation.

Some rely on intuition as a source of knowledge. is the strong hunch or feeling that what one perceives to be the case is indeed true. If one strongly believes what one perceives is real and true, knowledge thus is obtained. While there is no reason to doubt the truthfulness of the knowledge obtained by intuition, like knowledge obtained from senses and experiences, it is subjective.

Some knowledge are obtained by revelation. is the presentation of the truth from a supernatural source, such as deity.

Knowledge acquired via experience, intuition, revelation, and even measurement remain as private knowledge. The validity of knowledge obtained through nonscientific methods cannot be subject to objective testing.

: The second method of obtaining knowledge is the scientific method. This method of gaining knowledge is learning by reasoning. It is considered today to be the most reliable method of gaining knowledge. In contrast with the scientific method, the validity of knowledge obtained by scientific method can be subject to testing.

In all science, research proceeds within the framework of the scientific method. According to Lastrucci, "science may be defined as an objective, logical, and systematic method of analysis of phenomena devised to permit the accumulation of reliable knowledge". His definition of science contains all essential elements of scientific methods. First it defines science as [for details, see page 7 " ]. Second, it highlights three major characteristics of the scientific methods as and Let’s explore the meaning of these three characteristics. First, scientific method is , not in the sense of being value free. But it is objective in the sense that the analysts are not biased or prejudiced or subject to personal whims. Second, scientific method is in the sense that science follows logical reasoning. Logical reasoning is thinking in reasonable fashion. It is sufficient to point out two types of reasoning process, namely deductive and inductive logic. A full discussion on the logical process will be presented below. Third and finally, the search for truth in science is This means that researchers follow a systematic set of procedures through which knowledge is gained.

Let us now turn our attention to the in scientific method. First, note that there are two types of reasoning process, namely deductive and inductive logic.

logic is the process of reasoning from general conditions or premises using assumptions to specific conclusions. Economic theory rests largely on deductive logic. We establish a set of assumptions about conditions and behaviors to arrive at conclusions through logical process. [example]

Examples: utility maximization in the consumer behavior; profit maximization in the producer behavior.

logic is reasoning form the specific outcomes to a generalized conclusions. This is usually done by observing many individual experiences and cases to formulate a general conclusion. The inductive logic of reasoning is followed in most empirical economic research. [example]

We from facts and assumptions to a . "A is a tentative assertion of a relationship between factors or events that is subject to verification or rejection." In short, a hypothesis is a testable proposition of the relationship between or among variables.

How is related to y? And theory to law?

is a single statement that attempts to explain a single interesting or puzzling phenomenon. In other words, a hypothesis is a testable proposition on an interesting or puzzling phenomenon. It usually takes the form of an educated guess or conjecture. Usually the hypothesis is based on facts and assumptions.

is a whole system of thought (or systematic explanation) that refers to many phenomena and whose parts are related to one another in deductive, logical form.

A theory that has been subjected to extensive testing over time and across space, and that has won virtually universal acceptance, is called a . For instance, the law of supply and demand refers to the commonly observed phenomena that, in a free market, the forces of supply and demand generally push the price toward its equilibrium level, the price at which the quantity supplied and quantity demanded are equal. Another example is the law of diminishing marginal returns.

In the previous section, we first learned that the essential element of science lies in its method of analysis, and that the three main characteristics of the scientific method are that it is objective, logical, and systematic. Then we learned about the process of scientific method beginning from, say, deductive logic to hypothesis, from hypothesis to theory, and from theory to law. Now we need to learn the operational aspects of the scientific method by focusing on terms and concepts of research. In particular, we need to learn basic concepts such as assumptions, variables, parameters, and functional forms. Finally, we need to learn something about ceteris paribus.

In our study of gasoline price hike, we first that consumers are "rational" in that their decision-making in the purchase of gasoline is consistent with maximization of consumer satisfaction. Likewise, suppliers of gasoline (retail gasoline merchant) are also assumed to be rational in that their decision-making in pricing, inventory, etc. is consistent with profit maximization. We theorize that the retail gasoline price is jointly determined by the forces of demand and supply of gasoline. On the demand side, the quantity of gasoline demanded depends inversely on the retail price of gasoline, positively consumer income, and a host of other factors. On the supply side, the quantity of gasoline supplied depends positively on the price of gasoline price, cost of production (including price of crude oil and refinery cost), and a host of other factors, including the gasoline tax and environmental cost. Once we introduce the role of assumption in the research, it appropriate to further introduce related basic concepts of research: variables, functional relationship and parameters.

A is a quantity of something which varies and you are interested in. Price of gasoline is a variable to an economic analyst studying the recent gasoline price increase in 1996 but not to most motorists and not even to an economic analyst studying the relation between stock and bond prices.

The researcher chooses his or her variables. Choosing variables correctly is one of the first essential step of carrying out research. Therefore, choose variables with extreme care. To choose variable correctly, one has to know the two types of variables: the dependent variable and independent variable.

The is that quantity whose change the researcher wants to find out, explain, or predict. In the cause-effect relationship, the variable is the dependent variable. In the study of the demand for gasoline, the quantity of gasoline demanded is the dependent variable, because the quantity of gasoline demanded changes in response to changes in gasoline price, consumer income, and other demand side factors. This researcher is ultimately interested in measuring the impacts of these changes on gasoline price In some cases, however, the dependent variable is not quantity, but represents a qualitative choice. An example of the latter is decision to buy or not; or marry him (or her) or not.

The is a variable whose effect upon the dependent variable one is trying to understand, explain, and predict. In the cause-effect relationship, it is the variable. Using the study of the demand for gasoline, the independent variables are the price of gasoline, consumer income, and other variables such as fuel efficiency and population characteristics.

The concept of is a bit more tricky to define. A parameter is a quantity measuring the response of the dependent variable to change in the independent variable, and is usually assumed to remain constant during the period of study. To illustrate, Figure 2.1 shows the hypothetical relationship between the quantity of gasoline demanded and gasoline price over time. On the vertical axis, the quantity of gasoline demanded measured in gallons. On the horizontal axis, the price of gasoline is measured in U.S. dollars per gallon The a scatter plot of cross represents the combination of the quantity of gasoline demanded and corresponding gasoline price in each year over the period. The line is drawn through the scatter plot of the quantity of gasoline demanded and gasoline price as closely as possible. The line represents a best guess of the average relationship between the observed quantity of gasoline demanded and corresponding gasoline price during the period of study.

The algebraic relationship Q = a + bP is relationship between the two variables Q and P. It is linear both in the parameters and variables. Parameters like a or 1/a. are non-linear; variables like Q and 1/Q are examples of non-linear.

When there is a relationship between two variables, we say that " dependent variable is a function of independent variable " in the mathematical language. By this we mean to say that the magnitude of y depends on the magnitude of x. But when the dependent variable y is determined by not only by x but also by other independent variables, we say that y is a function of x , x , and x . Or in short y = f(x , x , x ).

In the gasoline demand example, the quantity of gasoline demanded (Q ) depends upon ( or is a linear function of) price of gasoline (P ) and consumer income (Y). Algebraically, we write: Q = (P , Y).

Linear functional form of this relationship can be written as Q = a + bP + cY. Since parameters a, b, and c enter the equation are linear, the relationship is linear. But the relationship may be other than linear functional forms. They include the log-linear model, semi-log models, and reciprocal models.

Most - but not all- economic relationship is the relationship between the dependent variable and one or more independent variable. The value of the dependent variable that is observed for subject in the study depends on three elements: First, it depends on the level of the independent variable; second on the influence of the independent variable(s) on the dependent variable, and finally on the influences of all other variables that may act upon the value of the subject but you are not interested in and thus excluded in the specification of the functional relationship. These excluded variables are assumed to remain the same during the period of study.

When variables are related in certain fashion, there are two kinds of relationships. When one variable is related with another variable, we say that there exists correlation between the two variables. More precisely, or more precisely measures the degree of a linear association between the variables. When one measures correlation among more than two variables, one measures When analysts are interested in measuring the degree of correlation. If they move together in the same direction, there is a positive correlation; if they move in opposite directions, there is negative correlation.

The share of health care spending in GDP and the relative price of health care are positively correlated. More examples of correlation.

When there exists a cause and effect relationship between two or more variables, we say that causation runs from variable to variable. then means changes in one variable brings about changes in another variable. The cause-effect relationship is often called the dependence relationship. Using the example of share of health-care spending out of GDP and relative price of health-care, health care share is the effect or dependent variable; and the relative price of health care is cause and independent variable. Does that mean that the health care share is always the dependent variable and the relative price of healthcare is always the independent variable? In other words, is the dependence or causality inherent in the variable themselves? Obviously, answer is no.

But then does a researcher decide on which variable depends on what variable? It is deliberately chosen and imposed by researchers, usually from theory or existing studies on the subject. Importance of determining the causal-relationship in research: Art of specifying causal relationship? Experience and knowledge?

In the study of gasoline demand, the quantity of gasoline demanded is assumed to depend on the price of gasoline. There are two reasons for the direction of causality running from the gasoline price to the quantity of gasoline demanded, but not the other way around. When we study the individual demand for gasoline, the price of gasoline is determined by aggregate decisions of all consumers and suppliers. When there are many consumers in the market, a reasonable assumption, the role played by an individual consumer is small enough to be ignored in the determination of retail gasoline price. Analysts then are interested in studying how an individual consumer respond to changes in gasoline price.

When economists are interested in studying the feedback effect from aggregate quantity of gasoline demanded to gasoline price, they build simultaneous equation model where the forces of aggregate demand and supply are allowed to interact in determining the gasoline price. -two stage method of estimating gasoline price. Regression analysis to study the dependence relationship.

Most economists consider economics as a science because they follow the scientific method. Nobel prize in natural science (physics, chemistry, and medicine). Some economists argue that their discipline does not qualify as science See Tregarten, p. 15 for specific reasons and ensuing controversy

Regardless of the merit or demerit of whether economics should be viewed as science or not, it is true that economic research uses the scientific method. It is good for us to learn the language and process of the scientific method.

John Taylor’s Econ 1A book example of US health care study in circular flow:

Gasoline price in circular flow: consumers, retail & wholesale gasoline market, refinery, and crude oil producers with gasoline tax and environmental cost.

about people’s behavior , institutions, and their environment affecting decision making

[Jullion Simon, p. 37]

: We deduce

is a single statement that attempts to explain or predict

Theory is an entire system of thought that refers to many phenomena and whose parts can be related to one another in deductive logical form.

Theory and Model

Relationships among the key concepts of research, such as variables, parameters, and functional forms can be easily illustrated using algebraic relationship with graphical illustration.

In algebraic terms, the formula for a straight line is Q equals plus . More concisely, Q = a + bP where Q is the price of gasoline and P is the price of gasoline and where a and b are parameters, representing the intercept and slope of the algebraic relationship. The intercept term tells us what the demand for gasoline would be if the price of gasoline is zero. But this is a mechanical interpretation of the intercept, and it is reasonable to interpret the intercept as the net influence of other variables in the relationship, namely consumer income and other variables in the case of gasoline demand. The slope coefficient "b" tells us how much the quantity of gasoline demanded would change as consumer income change on the average.

The parameter are usually assumed to be constant. But they may change over time and across space. In another time period or in another country, the parameter values may not be the same as those above.

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Analytical studies: a framework for quality improvement design and analysis

Conducting studies for learning is fundamental to improvement. Deming emphasised that the reason for conducting a study is to provide a basis for action on the system of interest. He classified studies into two types depending on the intended target for action. An enumerative study is one in which action will be taken on the universe that was studied. An analytical study is one in which action will be taken on a cause system to improve the future performance of the system of interest. The aim of an enumerative study is estimation, while an analytical study focuses on prediction. Because of the temporal nature of improvement, the theory and methods for analytical studies are a critical component of the science of improvement.

Introduction: enumerative and analytical studies

Designing studies that make it possible to learn from experience and take action to improve future performance is an essential element of quality improvement. These studies use the now traditional theory established through the work of Fisher, 1 Cox, 2 Campbell and Stanley, 3 and others that is widely used in biomedicine research. These designs are used to discover new phenomena that lead to hypothesis generation, and to explore causal mechanisms, 4 as well as to evaluate efficacy and effectiveness. They include observational, retrospective, prospective, pre-experimental, quasiexperimental, blocking, factorial and time-series designs.

In addition to these classifications of studies, Deming 5 defined a distinction between analytical and enumerative studies which has proven to be fundamental to the science of improvement. Deming based his insight on the distinction between these two approaches that Walter Shewhart had made in 1939 as he helped develop measurement strategies for the then-emerging science of ‘quality control.’ 6 The difference between the two concepts lies in the extrapolation of the results that is intended, and in the target for action based on the inferences that are drawn.

A useful way to appreciate that difference is to contrast the inferences that can be made about the water sampled from two different natural sources ( figure 1 ). The enumerative approach is like the study of water from a pond. Because conditions in the bounded universe of the pond are essentially static over time, analyses of random samples taken from the pond at a given time can be used to estimate the makeup of the entire pond. Statistical methods, such as hypothesis testing and CIs, can be used to make decisions and define the precision of the estimates.

Environment in enumerative and analytical study. Internal validity diagram from Fletcher et al . 7

The analytical approach, in contrast, is like the study of water from a river. The river is constantly moving, and its physical properties are changing (eg, due to snow melt, changes in rainfall, dumping of pollutants). The properties of water in a sample from the river at any given time may not describe the river after the samples are taken and analysed. In fact, without repeated sampling over time, it is difficult to make predictions about water quality, since the river will not be the same river in the future as it was at the time of the sampling.

Deming first discussed these concepts in a 1942 paper, 8 as well as in his 1950 textbook, 9 and in a 1975 paper used the enumerative/analytical terminology to characterise specific study designs. 5 While most books on experimental design describe methods for the design and analysis of enumerative studies, Moen et al 10 describe methods for designing and learning from analytical studies. These methods are graphical and focus on prediction of future performance. The concept of analytical studies became a key element in Deming's ‘system of profound knowledge’ that serves as the intellectual foundation for improvement science. 11 The knowledge framework for the science of improvement, which combines elements of psychology, the Shewhart view of variation, the concept of systems, and the theory of knowledge, informs a number of key principles for the design and analysis of improvement studies:

• Knowledge about improvement begins and ends in experimental data but does not end in the data in which it begins.
• Observations, by themselves, do not constitute knowledge.
• Prediction requires theory regarding mechanisms of change and understanding of context.
• Random sampling from a population or universe (assumed by most statistical methods) is not possible when the population of interest is in the future.
• The conditions during studies for improvement will be different from the conditions under which the results will be used. The major source of uncertainty concerning their use is the difficulty of extrapolating study results to different contexts and under different conditions in the future.
• The wider the range of conditions included in an improvement study, the greater the degree of belief in the validity and generalisation of the conclusions.

The classification of studies into enumerative and analytical categories depends on the intended target for action as the result of the study:

• Enumerative studies assume that when actions are taken as the result of a study, they will be taken on the material in the study population or ‘frame’ that was sampled.

More specifically, the study universe in an enumerative study is the bounded group of items (eg, patients, clinics, providers, etc) possessing certain properties of interest. The universe is defined by a frame, a list of identifiable, tangible units that may be sampled and studied. Random selection methods are assumed in the statistical methods used for estimation, decision-making and drawing inferences in enumerative studies. Their aim is estimation about some aspect of the frame (such as a description, comparison or the existence of a cause–effect relationship) and the resulting actions taken on this particular frame. One feature of an enumerative study is that a 100% sample of the frame provides the complete answer to the questions posed by the study (given the methods of investigation and measurement). Statistical methods such as hypothesis tests, CIs and probability statements are appropriate to analyse and report data from enumerative studies. Estimating the infection rate in an intensive care unit for the last month is an example of a simple enumerative study.

• Analytical studies assume that the actions taken as a result of the study will be on the process or causal system that produced the frame studied, rather than the initial frame itself. The aim is to improve future performance.

In contrast to enumerative studies, an analytical study accepts as a given that when actions are taken on a system based on the results of a study, the conditions in that system will inevitably have changed. The aim of an analytical study is to enable prediction about how a change in a system will affect that system's future performance, or prediction as to which plans or strategies for future action on the system will be superior. For example, the task may be to choose among several different treatments for future patients, methods of collecting information or procedures for cleaning an operating room. Because the population of interest is open and continually shifts over time, random samples from that population cannot be obtained in analytical studies, and traditional statistical methods are therefore not useful. Rather, graphical methods of analysis and summary of the repeated samples reveal the trajectory of system behaviour over time, making it possible to predict future behaviour. Use of a Shewhart control chart to monitor and create learning to reduce infection rates in an intensive care unit is an example of a simple analytical study.

The following scenarios give examples to clarify the nature of these two types of studies.

Scenario 1: enumerative study—observation

To estimate how many days it takes new patients to see all primary care physicians contracted with a health plan, a researcher selected a random sample of 150 such physicians from the current active list and called each of their offices to schedule an appointment. The time to the next available appointment ranged from 0 to 180 days, with a mean of 38 days (95% CI 35.6 to 39.6).

This is an enumerative study, since results are intended to be used to estimate the waiting time for appointments with the plan's current population of primary care physicians.

Scenario 2: enumerative study—hypothesis generation

The researcher in scenario 1 noted that on occasion, she was offered an earlier visit with a nurse practitioner (NP) who worked with the physician being called. Additional information revealed that 20 of the 150 physicians in the study worked with one or more NPs. The next available appointment for the 130 physicians without an NP averaged 41 days (95% CI 39 to 43 days) and was 18 days (95% CI 18 to 26 days) for the 20 practices with NPs, a difference of 23 days (a 56% shorter mean waiting time).

This subgroup analysis suggested that the involvement of NPs helps to shorten waiting times, although it does not establish a cause–effect relationship, that is, it was a ‘hypothesis-generating’ study. In any event, this was clearly an enumerative study, since its results were to understand the impact of NPs on waiting times in the particular population of practices. Its results suggested that NPs might influence waiting times, but only for practices in this health plan during the time of the study. The study treated the conditions in the health plan as static, like those in a pond.

Scenario 3: enumerative study—comparison

To find out if administrative changes in a health plan had increased member satisfaction in access to care, the customer service manager replicated a phone survey he had conducted a year previously, using a random sample of 300 members. The percentage of patients who were satisfied with access had increased from 48.7% to 60.7% (Fisher exact test, p<0.004).

This enumerative comparison study was used to estimate the impact of the improvement work during the last year on the members in the plan. Attributing the increase in satisfaction to the improvement work assumes that other conditions in the study frame were static.

Scenario 4: analytical study—learning with a Shewhart chart

Each primary care clinic in a health plan reported its ‘time until the third available appointment’ twice a month, which allowed the quality manager to plot the mean waiting time for all of the clinics on Shewhart charts. Waiting times had been stable for a 12-month period through August, but the manager then noted a special cause (increase in waiting time) in September. On stratifying the data by region, she found that the special cause resulted from increases in waiting time in the Northeast region. Discussion with the regional manager revealed a shortage of primary care physicians in this region, which was predicted to become worse in the next quarter. Making some temporary assignments and increasing physician recruiting efforts resulted in stabilisation of this measure.

Documenting common and special cause variation in measures of interest through the use of Shewhart charts and run charts based on judgement samples is probably the simplest and commonest type of analytical study in healthcare. Such charts, when stable, provide a rational basis for predicting future performance.

Scenario 5: analytical study—establishing a cause–effect relationship

The researcher mentioned in scenarios 1 and 2 planned a study to test the existence of a cause–effect relationship between the inclusion of NPs in primary care offices and waiting time for new patient appointments. The variation in patient characteristics in this health plan appeared to be great enough to make the study results useful to other organisations. For the study, she recruited about 100 of the plan's practices that currently did not use NPs, and obtained funding to facilitate hiring NPs in up to 50 of those practices.

The researcher first explored the theories on mechanisms by which the incorporation of NPs into primary care clinics could reduce waiting times. Using important contextual variables relevant to these mechanisms (practice size, complexity, use of information technology and urban vs rural location), she then developed a randomised block, time-series study design. The study had the power to detect an effect of a mean waiting time of 5 days or more overall, and 10 days for the major subgroups defined by levels of the contextual variables. Since the baseline waiting time for appointments varied substantially across practices, she used the baseline as a covariate.

After completing the study, she analysed data from baseline and postintervention periods using stratified run charts and Shewhart charts, including the raw measures and measures adjusted for important covariates and effects of contextual variables. Overall waiting times decreased 12 days more in practices that included NPs than they did in control practices. Importantly, the subgroup analyses according to contextual variables revealed conditions under which the use of NPs would not be predicted to lead to reductions in waiting times. For example, practices with short baseline waiting times showed little or no improvement by employing NPs. She published the results in a leading health research journal.

This was an analytical study because the intent was to apply the learning from the study to future staffing plans in the health plan. She also published the study, so its results would be useful to primary care practices outside the health plan.

Scenario 6: analytical study—implementing improvement

The quality-improvement manager in another health plan wanted to expand the use of NPs in the plan's primary care practices, because published research had shown a reduction in waiting times for practices with NPs. Two practices in his plan already employed NPs. In one of these practices, Shewhart charts of waiting time by month showed a stable process averaging 10 days during the last 2 years. Waiting time averaged less than 7 days in the second practice, but a period when one of the physicians left the practice was associated with special causes.

The quality manager created a collaborative among the plan's primary care practices to learn how to optimise the use of NPs. Physicians in the two sites that employed NPs served as subject matter experts for the collaborative. In addition to making NPs part of their care teams, participating practices monitored appointment supply and demand, and tested other changes designed to optimise response to patient needs. Thirty sites in the plan voluntarily joined the collaborative and hired NPs. After 6 months, Shewhart charts indicated that waiting times in 25 of the 30 sites had been reduced to less than 7 days. Because waiting times in these practices had been stable over a considerable period of time, the manager predicted that future patients would continue to experience reduced times for appointments. The quality manger began to focus on a follow-up collaborative among the backlog of 70 practices that wanted to join.

This project was clearly an analytical study, since its aim was specifically to improve future waiting-time performance for participating sites and other primary care offices in the plan. Moreover, it focused on learning about the mechanisms through which contextual factors affect the impact of NPs on primary care office functions, under practice conditions that (like those in a river) will inevitably change over time.

Statistical theory in enumerative studies is used to describe the precision of estimates and the validity of hypotheses for the population studied. But since these statistical methods provide no support for extrapolation of the results outside the population that was studied, the subject experts must rely on their understanding of the mechanisms in place to extend results outside the population.

In analytical studies, the standard error of a statistic does not address the most important source of uncertainty, namely, the change in study conditions in the future. Although analytical studies need to take into account the uncertainty due to sampling, as in enumerative studies, the attributes of the study design and analysis of the data primarily deal with the uncertainty resulting from extrapolation to the future (generalisation to the conditions in future time periods). The methods used in analytical studies encourage the exploration of mechanisms through multifactor designs, contextual variables introduced through blocking and replication over time.

Prior stability of a system (as observed in graphic displays of repeated sampling over time, according to Shewhart's methods) increases belief in the results of an analytical study, but stable processes in the past do not guarantee constant system behaviour in the future. The next data point from the future is the most important on a graph of performance. Extrapolation of system behaviour to future times therefore still depends on input from subject experts who are familiar with mechanisms of the system of interest, as well as the important contextual issues. Generalisation is inherently difficult in all studies because ‘whereas the problems of internal validity are solvable within the limits of the logic of probability statistics, the problems of external validity are not logically solvable in any neat, conclusive way’ 3 (p. 17).

The diverse activities commonly referred to as healthcare improvement 12 are all designed to change the behaviour of systems over time, as reflected in the principle that ‘not all change is improvement, but all improvement is change.’ The conditions in the unbounded systems into which improvement interventions are introduced will therefore be different in the future from those in effect at the time the intervention is studied. Since the results of improvement studies are used to predict future system behaviour, such studies clearly belong to the Deming category of analytical studies. Quality improvement studies therefore need to incorporate repeated measurements over time, as well as testing under a wide range of conditions (2, 3 and 10). The ‘gold standard’ of analytical studies is satisfactory prediction over time.

Conclusions and recommendations

In light of these considerations, some important principles for drawing inferences from improvement studies include 10 :

• The analysis of data, interpretation of that analysis and actions taken as a result of the study should be closely tied to the current knowledge of experts about mechanisms of change in the relevant area. They can often use the study to discover, understand and evaluate the underlying mechanisms.
• The conditions of the study will be different from the future conditions under which the results will be used. Assessment by experts of the magnitude of this difference and its potential impact on future events should be an integral part of the interpretation of the results of the intervention.
• Show all the data before aggregation or summary.
• Plot the outcome data in the order in which the tests of change were conducted and annotate with information on the interventions.
• Use graphical displays to assess how much of the variation in the data can be explained by factors that were deliberately changed.
• Rearrange and subgroup the data to study other sources of variation (background and contextual variables).
• Summarise the results of the study with appropriate graphical displays.

Because these principles reflect the fundamental nature of improvement—taking action to change performance, over time, and under changing conditions—their application helps to bring clarity and rigour to improvement science.

Acknowledgments

The author is grateful to F Davidoff and P Batalden for their input to earlier versions of this paper.

Competing interests: None.

Provenance and peer review: Not commissioned; externally peer reviewed.

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Everything You Need To Know About Analytical Research

Written By : Pitch N Hire

Wed May 01 2024

Research is vital in any field. It helps in finding out information about various subjects. It is a systematic process of collecting data, documenting critical information, analyzing data, and interpreting it. It employs different methodologies to perform various tasks. Its main task is to collect, compose and analyze data on the subject matter. It can be defined as the process of creating new knowledge or applying existing knowledge to invent new concepts.

Research methods are classified into different categories based on the methods, nature of the study, purpose, and research design. Based on the nature of the study, research is classified into two parts- descriptive research and analytical research. This article will cover the subject matter of analytical research. 

Now, you must be thinking about what is analytical research. It is that kind of research in which secondary data are used to critically examine the study. Researchers used already existing information for research analysis. Different types of analytical research designs are used for critically evaluating the information extracted from the data of the existing research.

Effect of Analytical Studies on Education Trails

Students, research scholars, doctors, psychologists, etc. take the help of analytical research for taking out important information for their research studies. It helps in adding new concepts and ideas to the already produced material. Various kinds of analytical research designs are used to add value to the study material. It is conducted using various methods such as literary research, public opinion, meta-analysis, scientific trials, etc.

When you come across a question of what is analytical research, you can define it as a tool that is used to add reliability to the work. This is generally conducted to provide support to an idea or hypothesis. It employs critical thinking to extract the small details. This helps in building big assumptions about the subject matter or the material of the study. It emphasizes comprehending the cause-effect relationship between variables. 

Analytical Research Designs

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The first is cohort studies and the second is a case-control study. In cohort studies, people of different groups with different levels of exposure are observed over time to analyze the occurrence of an outcome. It is a forward-direction and prospective kind of study. It is easier to determine the outcome risk among unexposed and exposed groups. 

It resembles the experimental design. Whereas in case-control studies, researchers enlist two groups, cases, and controls, and then bring out the history of exposure of each group. It is a backward-direction and retrospective study. It consumes less time and is comparatively cheaper than cohort studies. It is the primary study design that is used to examine the relationship between a particular exposure and an outcome.

Methods of Conducting Analytical Research 

Analytical Research saves time, money, and lives and helps in achieving objectives effectively. It can be conducted using the following methods:

Literary Research 

Literary Research is one of the methods of conducting analytical research. It means finding new ideas and concepts from already existing literary work. It requires you to invent something new, a new way of interpreting the already available information to discuss it. It is the backbone of various research studies. Its function is to find out all the literary information, preserve them with different methodologies and analyze them. It provides hypotheses in the already existing research and also helps in analyzing modern-day research. It helps in analyzing unsolved or doubtful theories.

Meta-Analysis Research

Meta-Analysis is an epidemiological, formal, and quantitative research design that helps in the systematic assessment of previous research results to develop a conclusion about the body of research. It is a subset of systematic reviews. It analyzes the strength of the evidence. It helps in examining the variability or heterogeneity. It includes a quantitative review of the body of literature. It is PRISMA and its aim is to identify the existence of effects, finding the negative and positive kinds of effects. Its results can improve the accuracy of the estimates of effects.

Scientific Trials

Scientific Trials research is conducted on people. It is of two types, observational studies and the second is clinical traits. It finds new possibilities for clinical traits. It aims to find out medical strategies. It also helps in determining whether medical treatment and devices are safe it not. It searches for a better way of treating, diagnosing, screening, and treatment of the disease. It is a scientific study that involves 4 stages. It is conducted to find if a new treatment method is safe, effective, and efficient in people or not.

It aims to examine or analyze surgical, medical, and behavioral interventions. There are different types of scientific trials such as cohort studies, case-control studies, treatment trials, cross-sectional studies, screening trials, pilot trials, prevention trials, etc. 

Analytical Research is that kind of research that utilizes the already available data for extracting information. Its main aim is to divide a topic or a concept into smaller pieces to understand it in a better way and then assemble those parts in a way that is understandable by you. You can conduct analytical research by using the methods discussed in the article. It involves ex-ante research. It means analyzing the phenomenon. 

It is of different types such as historical research, philosophical research, research synthesis, and reviews. Also, it intends to comprehend the causal relation between phenomena. It works within the limited variables and involves in-depth research and analysis of the available data. 

Therefore, it is crucial for any data because it adds relevance to it and makes it authentic. It supports and validates a hypothesis. It helps companies in making quick and effective decision-making about the product and services provided by them.

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Different Types of Research Methods

• Mallika Rangaiah
• Dec 22, 2021
• Updated on: Nov 21, 2023

Unlike what a layman generally presumes, Research is not just about determining a hypothesis and unraveling a conclusion for that hypothesis. Every research approach that we take up falls under the category of a type of methodology and every methodology is exclusive and intricate in its depth. 

So what are these research methodologies and how do the researchers make use of them? This is what we are going to explore through this blog. Before we attempt to understand these methods, let us understand what research methodology actually means. 

What are Research Methods ?

Firstly, let's understand why we undertake research? What exactly is the point of it? 

Research is mainly done to gain knowledge to support a survey or quest regarding a particular conception or theory and to reach a resolute conclusion regarding the same.  Research is generally an approach for gaining knowledge which is required to interpret, write, delve further and to distribute data. 

For ensuring that a fulfilling experience is delivered, it is essential that the Research is premium in its quality and that’s where Research Methods come to the rescue. 

(Recommended blog - Research Market Analysis )

Types of Research Methods

An area is selected, a specific hypothesis is determined and a defined conclusion is required to be achieved. But how is this conclusion reached? What is the approach that can be taken up? As per CR Kothari’s book “Research Methodology Methods and Techniques” (The Second Revised Edition),  the basic types of Research Methods are the following : 

Descriptive Research

Descriptive Research is a form of research that incorporates surveys as well as different varieties of fact-finding investigations. This form of research is focused on describing the prevailing state of affairs as they are. Descriptive Research is also termed as Ex post facto research. 

This research form emphasises on factual reporting, the researcher cannot control the involved variables and can only report the details as they took place or as they are taking place. 

Researchers mainly make use of a descriptive research approach for purposes such as when the research is aimed at deciphering characteristics, frequencies or trends. 

Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The descriptive research methods are mainly, observations, surveys as well as case studies. 

(Speaking of variables, have you ever wondered - What are confounding variables? )

Analytical Research

Analytical Research is a form of research where the researcher has to make do with the data and factual information available at their behest and interpret this information to undertake an acute evaluation of the data. 

This form of research is often undertaken by researchers to uncover some evidence that supports their present research and which makes it more authentic. It is also undertaken for concocting fresh ideas relating to the topic on which the research is based. 

From conducting meta analysis, literary research or scientific trials and learning public opinion, there are many methods through which this research is done. 

Applied Research

When a business or say, the society is faced with an issue that needs an immediate solution or resolution, Applied Research is the research type that comes to the rescue. 

We primarily make use of Applied Research when it comes to resolving the issues plaguing our daily lives, impacting our work, health or welfare. This research type is undertaken to uncover solutions for issues relating to varying sectors like education, engineering, psychology or business. 

For instance, a company might employ an applied researcher for concluding the best possible approach of selecting employees that would be the best fit for specific positions in the company. 

The crux of Applied Research is to figure out the solution to a certain growing practical issue. 

The 3 Types of Applied Research are mainly 

Evaluation Research - Research where prevailing data regarding the topic is interpreted to arrive at proper decisions

Research and Development - Where the focus is on setting up fresh products or services which focus on the target market requirements

Action Research - Which aims at offering practical solutions for certain business issues by giving them proper direction, are the 3 types of Applied Research. 

(Related blog - Target Marketing using AI )

Fundamental Research

This is a Research type that is primarily concerned with formulating a theory or understanding a particular natural phenomenon. Fundamental Research aims to discover information with an extensive application base, supplementing the existing concepts in a certain field or industry. 

Research on pure mathematics or research regarding generalisation of the behavior of humans are also examples of Fundamental Research. This form of research is mainly carried out in sectors like Education, Psychology and Science. 

For instance, in Psychology fundamental research assists the individual or the company in gaining better insights regarding certain behaviors such as deciphering how consumption of caffeine can possibly impact the attention span of a student or how culture stereotypes can possibly trigger depression. 

Quantitative Research

Quantitative Research, as the name suggests, is based on the measurement of a particular amount or quantity of a particular phenomenon. It focuses on gathering and interpreting numerical data and can be adopted for discovering any averages or patterns or for making predictions.

This form of Research is number based and it lies under the two main Research Types. It makes use of tables, data and graphs to reach a conclusion. The outcomes generated from this research are measurable and can be repeated unlike the outcomes of qualitative research. This research type is mainly adopted for scientific and field based research.

Quantitative research generally involves a large number of people and a huge section of data and has a lot of scope for accuracy in it. 

These research methods can be adopted for approaches like descriptive, correlational or experimental research.

Descriptive research - The study variables are analyzed and a summary of the same is seeked.

Correlational Research - The relationship between the study variables is analyzed. 

Experimental Research - It is deciphered to analyse whether a cause and effect relationship between the variables exists. 

Quantitative research methods

• Experiment Research - This method controls or manages independent variables for calculating the effect it has on dependent variables. 
• Survey - Surveys involve inquiring questions from a certain specified number or set of people either online, face to face or over the phone. 
• (Systematic) observation - This method involves detecting any occurrence and monitoring it in a natural setting. 
• Secondary research : This research focuses on making use of data which has been previously collected for other purposes such as for say, a national survey. 

(Related blog - Hypothesis Testing )

Qualitative Research

As the name suggests, this form of Research is more considered with the quality of a certain phenomenon, it dives into the “why” alongside the “what”. For instance, let’s consider a gender neutral clothing store which has more women visiting it than men. 

Qualitative research would be determining why men are not visiting the store by carrying out an in-depth interview of some potential customers in this category.

This form of research is interested in getting to the bottom of the reasons for human behaviour, i.e understanding why certain actions are taken by people or why they think certain thoughts. 

Through this research the factors influencing people into behaving in a certain way or which control their preferences towards a certain thing can be interpreted.

An example of Qualitative Research would be Motivation Research . This research focuses on deciphering the rooted motives or desires through intricate methods like in depth interviews. It involves several tests like story completion or word association. 

Another example would be Opinion Research . This type of research is carried out to discover the opinion and perspective of people regarding a certain subject or phenomenon.

This is a theory based form of research and it works by describing an issue by taking into account the prior concepts, ideas and studies. The experience of the researcher plays an integral role here.

The Types of Qualitative Research includes the following methods :

Qualitative research methods

• Observations: In this method what the researcher sees, hears of or encounters is recorded in detail.
• Interviews: Personally asking people questions in one-on-one conversations.
• Focus groups: This involves asking questions and discussions among a group of people to generate conclusions from the same. 
• Surveys: In these surveys unlike the quantitative research surveys, the questionnaires involve extensive open ended questions that require elaborate answers. 
• Secondary research: Gathering the existing data such as images, texts or audio or video recordings. This can involve a text analysis, a research of a case study, or an In-depth interview.

Conceptual Research

This research is related to an abstract idea or a theory. It is adopted by thinkers and philosophers with the aim of developing a new concept or to re-examine the existing concepts. 

Conceptual Research is mainly defined as a methodology in which the research is conducted by observing and interpreting the already present information on a present topic. It does not include carrying out any practical experiments. 

This methodology has often been adopted by famous Philosophers like Aristotle, Copernicus, Einstein and Newton for developing fresh theories and insights regarding the working of the world and for examining the existing ones from a different perspective. 

The concepts were set up by philosophers to observe their environment and to sort, study, and summarise the information available. 

Empirical Research

This is a research method that focuses solely on aspects like observation and experience, without focusing on the theory or system. It is based on data and it can churn conclusions that can be confirmed or verified through observation and experiment. Empirical Research is mainly undertaken to determine proof that certain variables are affecting the others in a particular way.   

This kind of research can also be termed as Experimental Research. In this research it is essential that all the facts are received firsthand, directly from the source so that the researcher can actively go and carry out the actions and manipulate the concerned materials to gain the information he requires.

In this research a hypothesis is generated and then a path is undertaken to confirm or invalidate this hypothesis. The control that the researcher holds over the involved variables defines this research. The researcher can manipulate one of these variables to examine its effect.

(Recommended blog - Data Analysis )

Other Types of Research

All research types apart from the ones stated above are mainly variations of them, either in terms of research purpose or in the terms of the time that is required for accomplishing the research, or say, the research environment. 

If we take the perspective of time, research can be considered as either One-time research or Longitudinal Research. 

One time Research : The research is restricted to a single time period. 

Longitudinal Research : The research is executed over multiple time periods. 

A research can also be set in a field or a laboratory or be a simulation, it depends on the environment that the research is based on. 

We’ve also got Historical Research which makes use of historical sources such as documents and remains for examining past events and ideas. This also includes the philosophy of an individual and groups at a particular time. 

Research may be clinical or diagnostic . These kinds of research generally carry out case study or in-depth interview approaches to determine basic causal relationships. 

Research can also be Exploratory or Formalized. 

Exploratory Research: This is a research that is more focused on establishing hypotheses than on deriving the result. This form of Research focuses on understanding the prevailing issue but it doesn’t really offer defining results. 

Formalized research: This is a research that has a solid structure and which also has specific hypotheses for testing. 

We can also classify Research as conclusion-oriented and decision-oriented. 

Conclusion Oriented Research: In this form of research, the researcher can select an issue, revamp the enquiry as he continues and visualize it as per his requirements. 

Decision-oriented research: This research depends on the requirement of the decision maker and offers less freedom to the research to conduct it as he pleases. 

The common and well known research methods have been listed in this blog. Hopefully this blog will give the readers and present and future researchers proper knowledge regarding important methods they can adopt to conduct their Research.

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Methodology

• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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