advantages and disadvantages of descriptive research ppt

Microbe Notes

Descriptive Studies- Types, Applications, Advantages, Limitations

A simple description of the health status of a community, based on routinely available data or on data obtained in special surveys, is often the first step in an epidemiological investigation. Such a study is termed as a descriptive study.
A descriptive study – as the name implies – describes the distributions of disease, injury or health in a population, outlining the burden of disease or the extent of exposure.
Pure descriptive studies make no attempt to analyze the links between exposure and effect.
They summarize patterns of disease or of disease determinants in terms of time, place and person.
They are usually based on mortality statistics and may examine patterns of death by age, sex, race or ethnicity during specified time periods or in various countries.
They describe a health outcome by different characteristics of a person (race, age, or sex, for example), place (geographic location), and time (a specific year or a span of time). For example, the case fatality of cholera in 1854 in London was 40% (John Snow, the cholera outbreak in London).
The results are used to understand a population’s health status, generate hypotheses about the causes of diseases, and inform program planning and evaluation. In other words, descriptive epidemiology describes the distribution of disease.

Table of Contents

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Types of Descriptive Studies

The types of descriptive studies include:

Case reports or case series
Correlational or ecologic studies
Cross-sectional studies
Prevalence surveys

Descriptive studies that examine individuals can take the form of case reports (a report of a single case of an unusual disease or association), case series (a description of several similar cases) and cross-sectional studies. Descriptive studies that examine populations, or groups, as the unit of observation, are known as ecological studies.

Applications of Descriptive Studies

Descriptive epidemiology identifies non-random variation in the distribution of disease, injury or health.
Their function is to describe the “who, what, why, when, where” without regard to the hypothesis, highlighting patterns of disease and associated factors.
This allows the public health practitioner to generate testable hypotheses regarding why such variation occurs.
Descriptive epidemiology identifies who is affected, when, and where the situation is occurring in the community or population of interest. This is an important tool for health services planning and programming.
Pure descriptive studies are rare, but descriptive data in reports of health statistics are a useful source of ideas for epidemiological studies.
Limited descriptive information (such as that provided in a case series) in which the characteristics of several patients with a specific disease are described but are not compared with those of a reference population, often stimulates the initiation of a more detailed epidemiological study.

For example, the description in 1981 of four young men with a previously rare form of pneumonia was the first in a wide range of epidemiological studies on the condition that became known as the acquired immunodeficiency syndrome (AIDS).

Advantages of Descriptive Studies

Descriptive (including ecological) studies are generally relatively quick, easy and cheap to conduct.
Exposure data often only available at the area level.
Differences in exposure between areas may be bigger than at the individual level, and so are more easily examined.
Utilization of geographical information systems to examine the spatial framework of disease and exposure.

Limitations

A descriptive study is limited to a description of the occurrence of a disease in a population.
It is unable to test hypotheses.
Weaknesses of case reports and case series are that they have no comparison (control) group, they cannot be tested for statistical associations, and they are especially prone to publication bias (especially where case reports/series describe the effectiveness of an intervention).
Park, K. (n.d.). Park’s textbook of preventive and social medicine.
Gordis, L. (2014). Epidemiology (Fifth edition.). Philadelphia, PA: Elsevier Saunders.
https://wiki.ecdc.europa.eu/fem/w/wiki/descriptive-studies
https://cursos.campusvirtualsp.org/mod/tab/view.php?id=34133&forceview=1
http://sphweb.bumc.bu.edu/otlt/MPH-Modules/EP/EP713_DescriptiveEpi/EP713_DescriptiveEpi_print.html
https://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/descriptive-studies-ecological-studies

About Author

Sagar Aryal

Study designs: Part 2 – Descriptive studies

Rakesh aggarwal.

Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Priya Ranganathan

1 Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

One of the first steps in planning a research study is the choice of study design. The available study designs are divided broadly into two types – observational and interventional. Of the various observational study designs, the descriptive design is the simplest. It allows the researcher to study and describe the distribution of one or more variables, without regard to any causal or other hypotheses. This article discusses the subtypes of descriptive study design, and their strengths and limitations.

INTRODUCTION

In our previous article in this series,[ 1 ] we introduced the concept of “study designs”– as “the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.” Study designs are primarily of two types – observational and interventional, with the former being loosely divided into “descriptive” and “analytical.” In this article, we discuss the descriptive study designs.

WHAT IS A DESCRIPTIVE STUDY?

A descriptive study is one that is designed to describe the distribution of one or more variables, without regard to any causal or other hypothesis.

TYPES OF DESCRIPTIVE STUDIES

Descriptive studies can be of several types, namely, case reports, case series, cross-sectional studies, and ecological studies. In the first three of these, data are collected on individuals, whereas the last one uses aggregated data for groups.

Case reports and case series

A case report refers to the description of a patient with an unusual disease or with simultaneous occurrence of more than one condition. A case series is similar, except that it is an aggregation of multiple (often only a few) similar cases. Many case reports and case series are anecdotal and of limited value. However, some of these bring to the fore a hitherto unrecognized disease and play an important role in advancing medical science. For instance, HIV/AIDS was first recognized through a case report of disseminated Kaposi's sarcoma in a young homosexual man,[ 2 ] and a case series of such men with Pneumocystis carinii pneumonia.[ 3 ]

In other cases, description of a chance observation may open an entirely new line of investigation. Some examples include: fatal disseminated Bacillus Calmette–Guérin infection in a baby born to a mother taking infliximab for Crohn's disease suggesting that adminstration of infliximab may bring about reactivation of tuberculosis,[ 4 ] progressive multifocal leukoencephalopathy following natalizumab treatment – describing a new adverse effect of drugs that target cell adhesion molecule α4-integrin,[ 5 ] and demonstration of a tumor caused by invasive transformed cancer cells from a colonizing tapeworm in an HIV-infected person.[ 6 ]

Cross-sectional studies

Studies with a cross-sectional study design involve the collection of information on the presence or level of one or more variables of interest (health-related characteristic), whether exposure (e.g., a risk factor) or outcome (e.g., a disease) as they exist in a defined population at one particular time. If these data are analyzed only to determine the distribution of one or more variables, these are “descriptive.” However, often, in a cross-sectional study, the investigator also assesses the relationship between the presence of an exposure and that of an outcome. Such cross-sectional studies are referred to as “analytical” and will be discussed in the next article in this series.

Cross-sectional studies can be thought of as providing a “snapshot” of the frequency and characteristics of a disease in a population at a particular point in time. These are very good for measuring the prevalence of a disease or of a risk factor in a population. Thus, these are very helpful in assessing the disease burden and healthcare needs.

Let us look at a study that was aimed to assess the prevalence of myopia among Indian children.[ 7 ] In this study, trained health workers visited schools in Delhi and tested visual acuity in all children studying in classes 1–9. Of the 9884 children screened, 1297 (13.1%) had myopia (defined as spherical refractive error of −0.50 diopters (D) or worse in either or both eyes), and the mean myopic error was −1.86 ± 1.4 D. Furthermore, overall, 322 (3.3%), 247 (2.5%) and 3 children had mild, moderate, and severe visual impairment, respectively. These parts of the study looked at the prevalence and degree of myopia or of visual impairment, and did not assess the relationship of one variable with another or test a causative hypothesis – these qualify as a descriptive cross-sectional study. These data would be helpful to a health planner to assess the need for a school eye health program, and to know the proportion of children in her jurisdiction who would need corrective glasses.

The authors did, subsequently in the paper, look at the relationship of myopia (an outcome) with children's age, gender, socioeconomic status, type of school, mother's education, etc. (each of which qualifies as an exposure). Those parts of the paper look at the relationship between different variables and thus qualify as having “analytical” cross-sectional design.

Sometimes, cross-sectional studies are repeated after a time interval in the same population (using the same subjects as were included in the initial study, or a fresh sample) to identify temporal trends in the occurrence of one or more variables, and to determine the incidence of a disease (i.e., number of new cases) or its natural history. Indeed, the investigators in the myopia study above visited the same children and reassessed them a year later. This separate follow-up study[ 8 ] showed that “new” myopia had developed in 3.4% of children (incidence rate), with a mean change of −1.09 ± 0.55 D. Among those with myopia at the time of the initial survey, 49.2% showed progression of myopia with a mean change of −0.27 ± 0.42 D.

Cross-sectional studies are usually simple to do and inexpensive. Furthermore, these usually do not pose much of a challenge from an ethics viewpoint.

However, this design does carry a risk of bias, i.e., the results of the study may not represent the true situation in the population. This could arise from either selection bias or measurement bias. The former relates to differences between the population and the sample studied. The myopia study included only those children who attended school, and the prevalence of myopia could have been different in those did not attend school (e.g., those with severe myopia may not be able to see the blackboard and hence may have been more likely to drop out of school). The measurement bias in this study would relate to the accuracy of measurement and the cutoff used. If the investigators had used a cutoff of −0.25 D (instead of −0.50 D) to define myopia, the prevalence would have been higher. Furthermore, if the measurements were not done accurately, some cases with myopia could have been missed, or vice versa, affecting the study results.

Ecological studies

Ecological (also sometimes called as correlational) study design involves looking for association between an exposure and an outcome across populations rather than in individuals. For instance, a study in the United States found a relation between household firearm ownership in various states and the firearm death rates during the period 2007–2010.[ 9 ] Thus, in this study, the unit of assessment was a state and not an individual.

These studies are convenient to do since the data have often already been collected and are available from a reliable source. This design is particularly useful when the differences in exposure between individuals within a group are much smaller than the differences in exposure between groups. For instance, the intake of particular food items is likely to vary less between people in a particular group but can vary widely across groups, for example, people living in different countries.

However, the ecological study design has some important limitations.First, an association between exposure and outcome at the group level may not be true at the individual level (a phenomenon also referred to as “ecological fallacy”).[ 10 ] Second, the association may be related to a third factor which in turn is related to both the exposure and the outcome, the so-called “confounding”. For instance, an ecological association between higher income level and greater cardiovascular mortality across countries may be related to a higher prevalence of obesity. Third, migration of people between regions with different exposure levels may also introduce an error. A fourth consideration may be the use of differing definitions for exposure, outcome or both in different populations.

Descriptive studies, irrespective of the subtype, are often very easy to conduct. For case reports, case series, and ecological studies, the data are already available. For cross-sectional studies, these can be easily collected (usually in one encounter). Thus, these study designs are often inexpensive, quick and do not need too much effort. Furthermore, these studies often do not face serious ethics scrutiny, except if the information sought to be collected is of confidential nature (e.g., sexual practices, substance use, etc.).

Descriptive studies are useful for estimating the burden of disease (e.g., prevalence or incidence) in a population. This information is useful for resource planning. For instance, information on prevalence of cataract in a city may help the government decide on the appropriate number of ophthalmologic facilities. Data from descriptive studies done in different populations or done at different times in the same population may help identify geographic variation and temporal change in the frequency of disease. This may help generate hypotheses regarding the cause of the disease, which can then be verified using another, more complex design.

DISADVANTAGES

As with other study designs, descriptive studies have their own pitfalls. Case reports and case-series refer to a solitary patient or to only a few cases, who may represent a chance occurrence. Hence, conclusions based on these run the risk of being non-representative, and hence unreliable. In cross-sectional studies, the validity of results is highly dependent on whether the study sample is well representative of the population proposed to be studied, and whether all the individual measurements were made using an accurate and identical tool, or not. If the information on a variable cannot be obtained accurately, for instance in a study where the participants are asked about socially unacceptable (e.g., promiscuity) or illegal (e.g., substance use) behavior, the results are unlikely to be reliable.

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Descriptive Research Design – Overview

Published 16 October, 2023

advantages and disadvantages of descriptive research ppt

Descriptive research is an observational method that focuses on identifying patterns in data without making inferences about cause and effect relationships between variables. The purpose of this blog post is to provide a brief description of descriptive research design including its advantages and disadvantages and methods of conducting descriptive research.

What is Descriptive Research?

Descriptive research is a process of systematically describing and analyzing something’s features, properties or characteristics. Descriptive research provides numerical descriptions that identify what the thing being studied looks like in terms of its size, location, and frequency.

This type of research will help you in defining the characteristics of the population on which you have performed the study. A descriptive research design enables you to develop an in-depth understanding of the topic or subjects. In such a type of investigation, you can’t have control over variables.

By performing descriptive research, you will be able to study participants in a natural setting. Descriptive research basically includes describing the behavior of people to whom you have select as a participant in the research process .

In addition to this , descriptive research also allows you to describe the other various aspects of your investigation. An important feature is that you can employ different types of variables but you only need a single variable for performing the descriptive investigation. It is a type of study which includes observation as a technique for gathering facts about the study. You can perform descriptive research for analyzing the relationship between two different variables.

For example, A company whose sale of specific products such as home decor products is going down. Management, in order to analyze the reason for the same, needs to conduct descriptive research. Survey Research is the data collection technique that a research team in an organization can use for collecting the view of people about the decline in the sale of home décor products.

When to Use Descriptive Research Design

Descriptive research is suitable when the aim of the study is to identify characteristics, frequencies, trends, categories, and the behavior of people.

In addition to this, the descriptive research design is appropriate to use when you don’t have much knowledge about the research topics or problems.

This type of study can be used before you start researching why something happens so that we have an idea on how it occurs, where are most likely places this will happen at and who might experience these things more often than others.

Advantages of Descriptive Research

One of the biggest advantages of descriptive research is that it allows you to analyze facts and helps you in developing an in-depth understanding of the research problem .
Another benefit of descriptive research is that it enables you to determine the behavior of people in a natural setting.
In such a type of investigation, you can utilize both qualitative and quantitative research methods for gathering facts.
Descriptive research is cost-effective and quick. It can also be used for many different purposes, which makes it a very versatile method of gathering data.
You need less time for performing such types of research .
With descriptive research, you can get rich data that’s great for future studies. Use it to develop hypotheses or your research objective too!

Disadvantages of Descriptive Research

The biggest disadvantage of descriptive research is that you cannot use statistical tools or techniques for verifying problems.
Respondents can be affected by the presence of an observer and may engage in pretending. This is called the “observer effect.” In some cases, respondents are less likely to give accurate responses if they feel that a question will assess intimate matters.
There are high chances of biases in the research findings .
Due to the observational nature, it is quite difficult to repeat the research process .
By performing descriptive research you can find the root cause of the problem.

Methods of Descriptive Research Design

You can utilize both Qualitative and Quantitative methods for performing descriptive research. It is very much essential for you to make the choice of a suitable research design for investigation as the reliability and validity of the research outcomes are completely based on it. There are three different methods that you can use in descriptive research are:

It is the method that includes a detailed description of the subject or topic. The survey is the method by utilizing which you can collect a huge volume of facts about the topic or subject.

You can use a survey technique for directly accumulating information about the perception of people about the topic. The methods which can be applied for performing a survey in descriptive research are questionnaires, telephonic and personal interviews . In descriptive studies, generally, open-ended questions are included in a questionnaire.

2. Observation

It is basically a technique that the researcher utilities for observing and recording participants. By utilizing this technique you can easily view the subject in a natural setting.

Observations are a way of gathering data that can be used to understand how people act in real-life situations. These observations give researchers the opportunity to see behaviors and phenomena without having them rely on honesty or accuracy from respondents, which is often useful for psychologists, social scientists, and market research companies. Furthermore, observations play an important role in understanding things such as physical entities before developing models hypotheses, or theories – because they provide systematic descriptions of what’s being investigated

For example, an investigation is performed for gathering information about the buying decision-making procedure by customers. The investigator for collecting the facts about the topic has observed people in shopping malls while they are making the purchase of specific products or services. By using the observation technique you can ensure the accuracy and honesty in the information provided by respondents.

3. Case study

You can use the case study methods in research for gathering an in-depth understanding of specific phenomena. It is the method that would enable you to study the situation which takes place rarely

Case studies are a great way to provide detailed information about an individual (such as yourself), group, event, or organization. Instead of gathering data across time and space in order to identify patterns, case studies gather extensive detailed data to identify the characteristics of a narrowly defined subject.

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Bridging the Gap: Overcome these 7 flaws in descriptive research design

Descriptive research design is a powerful tool used by scientists and researchers to gather information about a particular group or phenomenon. This type of research provides a detailed and accurate picture of the characteristics and behaviors of a particular population or subject. By observing and collecting data on a given topic, descriptive research helps researchers gain a deeper understanding of a specific issue and provides valuable insights that can inform future studies.

In this blog, we will explore the definition, characteristics, and common flaws in descriptive research design, and provide tips on how to avoid these pitfalls to produce high-quality results. Whether you are a seasoned researcher or a student just starting, understanding the fundamentals of descriptive research design is essential to conducting successful scientific studies.

Table of Contents

What Is Descriptive Research Design?

The descriptive research design involves observing and collecting data on a given topic without attempting to infer cause-and-effect relationships. The goal of descriptive research is to provide a comprehensive and accurate picture of the population or phenomenon being studied and to describe the relationships, patterns, and trends that exist within the data.

Descriptive research methods can include surveys, observational studies , and case studies, and the data collected can be qualitative or quantitative . The findings from descriptive research provide valuable insights and inform future research, but do not establish cause-and-effect relationships.

Importance of Descriptive Research in Scientific Studies

1. understanding of a population or phenomenon.

Descriptive research provides a comprehensive picture of the characteristics and behaviors of a particular population or phenomenon, allowing researchers to gain a deeper understanding of the topic.

2. Baseline Information

The information gathered through descriptive research can serve as a baseline for future research and provide a foundation for further studies.

3. Informative Data

Descriptive research can provide valuable information and insights into a particular topic, which can inform future research, policy decisions, and programs.

4. Sampling Validation

Descriptive research can be used to validate sampling methods and to help researchers determine the best approach for their study.

5. Cost Effective

Descriptive research is often less expensive and less time-consuming than other research methods , making it a cost-effective way to gather information about a particular population or phenomenon.

6. Easy to Replicate

Descriptive research is straightforward to replicate, making it a reliable way to gather and compare information from multiple sources.

Key Characteristics of Descriptive Research Design

The primary purpose of descriptive research is to describe the characteristics, behaviors, and attributes of a particular population or phenomenon.

2. Participants and Sampling

Descriptive research studies a particular population or sample that is representative of the larger population being studied. Furthermore, sampling methods can include convenience, stratified, or random sampling.

3. Data Collection Techniques

Descriptive research typically involves the collection of both qualitative and quantitative data through methods such as surveys, observational studies, case studies, or focus groups.

4. Data Analysis

Descriptive research data is analyzed to identify patterns, relationships, and trends within the data. Statistical techniques , such as frequency distributions and descriptive statistics, are commonly used to summarize and describe the data.

5. Focus on Description

Descriptive research is focused on describing and summarizing the characteristics of a particular population or phenomenon. It does not make causal inferences.

6. Non-Experimental

Descriptive research is non-experimental, meaning that the researcher does not manipulate variables or control conditions. The researcher simply observes and collects data on the population or phenomenon being studied.

When Can a Researcher Conduct Descriptive Research?

A researcher can conduct descriptive research in the following situations:

To better understand a particular population or phenomenon
To describe the relationships between variables
To describe patterns and trends
To validate sampling methods and determine the best approach for a study
To compare data from multiple sources.

Types of Descriptive Research Design

1. survey research.

Surveys are a type of descriptive research that involves collecting data through self-administered or interviewer-administered questionnaires. Additionally, they can be administered in-person, by mail, or online, and can collect both qualitative and quantitative data.

2. Observational Research

Observational research involves observing and collecting data on a particular population or phenomenon without manipulating variables or controlling conditions. It can be conducted in naturalistic settings or controlled laboratory settings.

3. Case Study Research

Case study research is a type of descriptive research that focuses on a single individual, group, or event. It involves collecting detailed information on the subject through a variety of methods, including interviews, observations, and examination of documents.

4. Focus Group Research

Focus group research involves bringing together a small group of people to discuss a particular topic or product. Furthermore, the group is usually moderated by a researcher and the discussion is recorded for later analysis.

5. Ethnographic Research

Ethnographic research involves conducting detailed observations of a particular culture or community. It is often used to gain a deep understanding of the beliefs, behaviors, and practices of a particular group.

Advantages of Descriptive Research Design

1. provides a comprehensive understanding.

Descriptive research provides a comprehensive picture of the characteristics, behaviors, and attributes of a particular population or phenomenon, which can be useful in informing future research and policy decisions.

2. Non-invasive

Descriptive research is non-invasive and does not manipulate variables or control conditions, making it a suitable method for sensitive or ethical concerns.

3. Flexibility

Descriptive research allows for a wide range of data collection methods , including surveys, observational studies, case studies, and focus groups, making it a flexible and versatile research method.

4. Cost-effective

Descriptive research is often less expensive and less time-consuming than other research methods. Moreover, it gives a cost-effective option to many researchers.

5. Easy to Replicate

Descriptive research is easy to replicate, making it a reliable way to gather and compare information from multiple sources.

6. Informs Future Research

The insights gained from a descriptive research can inform future research and inform policy decisions and programs.

Disadvantages of Descriptive Research Design

1. limited scope.

Descriptive research only provides a snapshot of the current situation and cannot establish cause-and-effect relationships.

2. Dependence on Existing Data

Descriptive research relies on existing data, which may not always be comprehensive or accurate.

3. Lack of Control

Researchers have no control over the variables in descriptive research, which can limit the conclusions that can be drawn.

The researcher’s own biases and preconceptions can influence the interpretation of the data.

5. Lack of Generalizability

Descriptive research findings may not be applicable to other populations or situations.

6. Lack of Depth

Descriptive research provides a surface-level understanding of a phenomenon, rather than a deep understanding.

7. Time-consuming

Descriptive research often requires a large amount of data collection and analysis, which can be time-consuming and resource-intensive.

7 Ways to Avoid Common Flaws While Designing Descriptive Research

1. Clearly define the research question

A clearly defined research question is the foundation of any research study, and it is important to ensure that the question is both specific and relevant to the topic being studied.

2. Choose the appropriate research design

Choosing the appropriate research design for a study is crucial to the success of the study. Moreover, researchers should choose a design that best fits the research question and the type of data needed to answer it.

3. Select a representative sample

Selecting a representative sample is important to ensure that the findings of the study are generalizable to the population being studied. Researchers should use a sampling method that provides a random and representative sample of the population.

4. Use valid and reliable data collection methods

Using valid and reliable data collection methods is important to ensure that the data collected is accurate and can be used to answer the research question. Researchers should choose methods that are appropriate for the study and that can be administered consistently and systematically.

5. Minimize bias

Bias can significantly impact the validity and reliability of research findings. Furthermore, it is important to minimize bias in all aspects of the study, from the selection of participants to the analysis of data.

6. Ensure adequate sample size

An adequate sample size is important to ensure that the results of the study are statistically significant and can be generalized to the population being studied.

7. Use appropriate data analysis techniques

The appropriate data analysis technique depends on the type of data collected and the research question being asked. Researchers should choose techniques that are appropriate for the data and the question being asked.

Have you worked on descriptive research designs? How was your experience creating a descriptive design? What challenges did you face? Do write to us or leave a comment below and share your insights on descriptive research designs!

extremely very educative

Indeed very educative and useful. Well explained. Thank you

Simple,easy to understand

Excellent and easy to understand queries and questions get answered easily. Its rather clear than any confusion. Thanks a million Shritika Sirisilla.

Easy to understand. Well written , educative and informative

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Descriptive Research Design: Survey and Observation

Published by Cleopatra Jennings Modified over 5 years ago

Presentation on theme: "Descriptive Research Design: Survey and Observation"— Presentation transcript:

2.06 Understand data-collection methods to evaluate their appropriateness for the research problem/issue.

Descriptive Designs: Survey and Observation

Observation, Focus Groups, and Other Qualitative Measures

Agenda Recap Survey Methods  Types of survey Methods  Advantages and Disadvantages  Criteria for Choosing  Increasing Response Rates Observation Methods.

Knowledge is Power Marketing Information System (MIS) determines what information managers need and then gathers, sorts, analyzes, stores, and distributes.

The Market Research Process

Chapter 5 Decision support systems and marketing research.

Chapter 33 Conducting Marketing Research. The Marketing Research Process 1. Define the Problem 2. Obtaining Data 3. Analyze Data 4. Rec. Solutions 5.

Agenda for Define Key Terms Read & Take Notes The Persuaders

Exploratory Research Design: Secondary Data

Decision Support Systems and Marketing Research

Descriptive Research Design: Survey and Observation.

Descriptive Research Design

About project

Types of Descriptive Research: Methods and Examples

In order to understand what descriptive research is, one must first understand the different types of research methods. Descriptive research can be defined as a method used to describe something, usually in great detail. This type of research is often used in the sciences, such as in biology or psychology.

What is Descriptive Research?

Descriptive research is a type of research that is used to describe a population or phenomenon. This type of research is often used in the social sciences, but can be used in other disciplines as well.

Descriptive research can be either quantitative or qualitative in nature.

Quantitative descriptive research

Qualitative descriptive research, examples of descriptive research, characteristics of descriptive research, quantitative in nature, observational.

Descriptive research is observational in that it simply observes and records what is happening. It does not try to explain why something is happening or to manipulate variables.

Uncontrolled Variables

Basis for further research, cross-sectional studies.

Cross-sectional studies are typically used to do descriptive research. An observational study method known as a cross-sectional study involves obtaining data on various variables at the person level at a specific period.

Types of Descriptive Research

Researchers can develop hypotheses through case studies that can broaden the scope of evaluation when researching the phenomenon.

Observations

Focus group studies.

Focus group studies involve assembling a small group of individuals to engage in a discussion on a specific topic or product. A researcher typically moderates the session, and the conversation is recorded for subsequent analysis.

Ethnographic Studies

Pros of descriptive research, comprehensive, various data collection techniques.

The case study method, observational method, and survey method are just a few of the many data collection techniques that can be utilized in descriptive research. Quick and economical.

High External Validity

Quick and inexpensive, cons of descriptive research, unable to validate or test research question.

Due to the fact that the data acquired does not assist in elucidating the reason of the phenomenon being examined, the descriptive technique of research cannot be utilized to test or validate the research problem.

Risk of Sampling Error

Absence of dependability, possibility of false responses.

People’s reactions are crucial to descriptive research, especially when employing surveys. False responses may occasionally be given, which would undermine the reliability of the data gathered and, ultimately, the research’s conclusions.

Pros and Cons of Descriptive Research

Pros	Cons
Comprehensive	Unable to validate or test research question
Various Data Collection Techniques	Risk of Sampling Error
High External Validity	Absence of Dependability
Quick and Inexpensive	Possibility of False Responses

Why to use Descriptive Research?

Comparing variables, validate the current conditions, analysis of data trends.

The descriptive research approach can be used to track changes in variables over time, enabling the discovery and analysis of trends.

Describe the Features of the Subjects

When deciding whether or not to use Descriptive Research, researchers should consider the pros and cons, as well as the specific research question they are trying to answer.

Advantages and disadvantages of descriptive research

Descriptive research

Descriptive research is a type of research that is responsible for describing the population situation or phenomenon around which his study focuses. It seeks to provide information about the what, how, when, and where of the research problem, without giving priority to answering the “why” of the problem. As its name says, this way of investigating “describes”, it does not explain. Advantages and disadvantages of descriptive research

In addition, it obtains information on the phenomenon or situation to be studied, using techniques such as observation and survey, among others. For example, research studying the morphology and mechanism of action of SARS-CoV-2 is descriptive. Answer the “what”, not the “why”.

This type of research is very useful when conducting studies, for example, when you want to know which brand of soda is most consumed in a supermarket, where you only want to know which is the most consumed, and not why it is the most consumed. consumed.

Descriptive investigations, unlike other types of investigations, carry out their study without altering or manipulating any of the variables of the phenomenon, limiting themselves only to their measurement and description. Additionally, it is possible to make future forecasts, although they are considered premature or basic.

Descriptive research characteristics

Here are some of the most important characteristics of descriptive research :

Has no control over variables

In descriptive research, the researcher has no control over any of the variables that affect the event or problem under investigation. Advantages and disadvantages of descriptive research

Existence of variables

To carry out a descriptive research , it is necessary to know in advance the variables that will be analyzed, since this type of research is not dedicated to the search for variables, but to their study.

Although, when obtaining data on the variables , it is possible to make forecasts, these are not entirely reliable, since they are considered premature.

Quantitative information

In most cases, descriptive research gets data on quantities, not qualities . It is for this reason that it can be said that a descriptive research is quantitative. Advantages and disadvantages of descriptive research

Even so, there is also the possibility of obtaining qualitative data.

As in all types of research , the data provided by descriptive research must be both accurate and reliable.

Information classification

Descriptive research can be used to classify the data collected in the study that is being carried out, separating them into different categories of description.

Usually, the cross-sectional or transectional design is the most used to carry out this type of research , although it is also possible to use the pre-experimental design. Advantages and disadvantages of descriptive research

Descriptive research design

The research design is used to draw up the work plan to follow in the research. It is where the conceptual phase of the research, such as the statement of the problem , meets the operational phase, such as the method and instruments of the investigation.

For the case of the design of a descriptive investigation, most of the time it is necessary to obtain data that refers to the quantity. To achieve this task, the researcher can choose between two different types of research designs, which have specific characteristics that differentiate them from each other.

The two types of designs used in descriptive research are described below:

Cross-sectional or transectional design

In cross-sectional designs, the variables are not affected by any type of process, which is why they only dedicate themselves to observing the event as it happens, limiting themselves only to analyzing them. Advantages and disadvantages of descriptive research

They basically consist of making a description of the variables to be measured in a phenomenon, and analyzing the incidence at the time that event occurs.

Pre-experimental design

There are occasions where the pre- experimental design is used as a test to get a first contact with the research problem in a real way, being used, on some occasions, as a test of experiments with a greater degree of control.

This type of design does not allow to establish causal relationships, since they do not have the possibility of controlling variables , and their internal validity is not very reliable. Furthermore, it is applied only to a group, over which it has no control whatsoever.

There are two ways to carry out a pre- experimental design, which are as follows:

Case study with a single measurement : in this type of design, a stimulus is applied to a group and then the data obtained from the variable or variables to be measured are taken. The simplicity of the design makes it unreliable, since there is no reference to the level of the variable (s) before the stimulus is applied, as well as no control over them.
Test and post-test design with a single group : for this type of design, a test is carried out before and after applying the stimulus to the group, thus allowing the visualization of the differences that may exist between the measurements of the studied variable (s) . Although, using this design it is possible to differentiate the levels of the variables , before and after the stimulus is applied, it does not allow to visualize causality, since there is no comparison group, nor is there the possibility of manipulating the variables. Advantages and disadvantages of descriptive research

Techniques used in descriptive research

In the case of descriptive research , there are three techniques to carry it out:

Observation

Observation is one of the most used information, of the quantitative or qualitative type:

To obtain quantitative information , statistical and numerical study methodologies are used, where information about values such as weight, scale and years, among others, is obtained. So it can be said that fundamentally numerical values are obtained.
On the other hand, to obtain qualitative information, the type of data obtained does not have to do with numbers or statistics , but with the dynamics that occur in the group on which the research is being developed. Advantages and disadvantages of descriptive research

Using the case study it is possible to carry out a slightly more detailed analysis of the event, as well as to study in detail groups or subjects separately.

In addition, it is possible to present a hypothesis and to expand the degree of knowledge about the event under investigation. However, due to its low precision in forecasting, it is not possible to specify the causes and effects of the phenomenon studied.

Research survey

The research survey is one of the most widely used instruments when conducting descriptive research, where the number of samples to be taken is large. Advantages and disadvantages of descriptive research

The selection of questions should include both open and closed questions, thus guaranteeing a balance between them and making it possible to collect good quality information.

Like all different types of research , descriptive research has both advantages and disadvantages. Some of the most important are listed below.

The brevity by which descriptive investigations are carried out means that their costs are not high, compared to other types of investigations.
It enables both the collection of quantitative data and qualitative data.
They allow to formulate hypotheses, as well as provide a large amount of valuable data for the development of future investigations. Advantages and disadvantages of descriptive research
By using descriptive research , the data is collected in the place where it occurs, without any type of alteration, ensuring the quality and integrity of the same.

Disadvantages

If the questions are not well formulated, the answers obtained may not be entirely reliable, which makes it difficult to carry out a credible investigation.
The types of variables that allow the study of descriptive investigations make it impossible to visualize the causes and effects of the event.
The data obtained by conducting a descriptive research , being collected randomly, make it impossible to obtain valid data that represent the entire population.

Descriptive Research Examples

Some examples of descriptive investigations may be the following:

Penguin census

Studying the penguin population that exists in the South Georgia Islands is a descriptive investigation that answers the what and where. Advantages and disadvantages of descriptive research

National census

The research carried out in a national census is descriptive, since it is only interested in data such as the number of population, the salary they receive, or what class the household is, without making any kind of analogy between these. .

Carrying out a descriptive investigation that collects data about the political party that people will choose in the next elections, it is possible to predict, with a margin of error , the result that will be obtained in them.

Supermarket

Using observation, qualitative data can be collected on the habits of supermarket customers regarding the purchases they make in it. Advantages and disadvantages of descriptive research

Kids playtime

Through the resource of the survey , it is possible to carry out a descriptive investigation that yields information about the number of hours per day that children in a particular population play. Being able to make a forecast of the weather that a particular child of that city plays.

What is Narrative analysis Uses Case Study Analysis of the Results

What is Observation in research Benefits Disadvantages Types

Medical Statistics Examples Hypothesis formulation tests

What is observation Tools importance qualities of a good observer examples

16 Advantages and Disadvantages of Experimental Research

How do you make sure that a new product, theory, or idea has validity? There are multiple ways to test them, with one of the most common being the use of experimental research. When there is complete control over one variable, the other variables can be manipulated to determine the value or validity that has been proposed.

Then, through a process of monitoring and administration, the true effects of what is being studied can be determined. This creates an accurate outcome so conclusions about the final value potential. It is an efficient process, but one that can also be easily manipulated to meet specific metrics if oversight is not properly performed.

Here are the advantages and disadvantages of experimental research to consider.

What Are the Advantages of Experimental Research?

1. It provides researchers with a high level of control. By being able to isolate specific variables, it becomes possible to determine if a potential outcome is viable. Each variable can be controlled on its own or in different combinations to study what possible outcomes are available for a product, theory, or idea as well. This provides a tremendous advantage in an ability to find accurate results.

2. There is no limit to the subject matter or industry involved. Experimental research is not limited to a specific industry or type of idea. It can be used in a wide variety of situations. Teachers might use experimental research to determine if a new method of teaching or a new curriculum is better than an older system. Pharmaceutical companies use experimental research to determine the viability of a new product.

3. Experimental research provides conclusions that are specific. Because experimental research provides such a high level of control, it can produce results that are specific and relevant with consistency. It is possible to determine success or failure, making it possible to understand the validity of a product, theory, or idea in a much shorter amount of time compared to other verification methods. You know the outcome of the research because you bring the variable to its conclusion.

4. The results of experimental research can be duplicated. Experimental research is straightforward, basic form of research that allows for its duplication when the same variables are controlled by others. This helps to promote the validity of a concept for products, ideas, and theories. This allows anyone to be able to check and verify published results, which often allows for better results to be achieved, because the exact steps can produce the exact results.

5. Natural settings can be replicated with faster speeds. When conducting research within a laboratory environment, it becomes possible to replicate conditions that could take a long time so that the variables can be tested appropriately. This allows researchers to have a greater control of the extraneous variables which may exist as well, limiting the unpredictability of nature as each variable is being carefully studied.

6. Experimental research allows cause and effect to be determined. The manipulation of variables allows for researchers to be able to look at various cause-and-effect relationships that a product, theory, or idea can produce. It is a process which allows researchers to dig deeper into what is possible, showing how the various variable relationships can provide specific benefits. In return, a greater understanding of the specifics within the research can be understood, even if an understanding of why that relationship is present isn’t presented to the researcher.

7. It can be combined with other research methods. This allows experimental research to be able to provide the scientific rigor that may be needed for the results to stand on their own. It provides the possibility of determining what may be best for a specific demographic or population while also offering a better transference than anecdotal research can typically provide.

What Are the Disadvantages of Experimental Research?

1. Results are highly subjective due to the possibility of human error. Because experimental research requires specific levels of variable control, it is at a high risk of experiencing human error at some point during the research. Any error, whether it is systemic or random, can reveal information about the other variables and that would eliminate the validity of the experiment and research being conducted.

2. Experimental research can create situations that are not realistic. The variables of a product, theory, or idea are under such tight controls that the data being produced can be corrupted or inaccurate, but still seem like it is authentic. This can work in two negative ways for the researcher. First, the variables can be controlled in such a way that it skews the data toward a favorable or desired result. Secondly, the data can be corrupted to seem like it is positive, but because the real-life environment is so different from the controlled environment, the positive results could never be achieved outside of the experimental research.

3. It is a time-consuming process. For it to be done properly, experimental research must isolate each variable and conduct testing on it. Then combinations of variables must also be considered. This process can be lengthy and require a large amount of financial and personnel resources. Those costs may never be offset by consumer sales if the product or idea never makes it to market. If what is being tested is a theory, it can lead to a false sense of validity that may change how others approach their own research.

4. There may be ethical or practical problems with variable control. It might seem like a good idea to test new pharmaceuticals on animals before humans to see if they will work, but what happens if the animal dies because of the experimental research? Or what about human trials that fail and cause injury or death? Experimental research might be effective, but sometimes the approach has ethical or practical complications that cannot be ignored. Sometimes there are variables that cannot be manipulated as it should be so that results can be obtained.

5. Experimental research does not provide an actual explanation. Experimental research is an opportunity to answer a Yes or No question. It will either show you that it will work or it will not work as intended. One could argue that partial results could be achieved, but that would still fit into the “No” category because the desired results were not fully achieved. The answer is nice to have, but there is no explanation as to how you got to that answer. Experimental research is unable to answer the question of “Why” when looking at outcomes.

6. Extraneous variables cannot always be controlled. Although laboratory settings can control extraneous variables, natural environments provide certain challenges. Some studies need to be completed in a natural setting to be accurate. It may not always be possible to control the extraneous variables because of the unpredictability of Mother Nature. Even if the variables are controlled, the outcome may ensure internal validity, but do so at the expense of external validity. Either way, applying the results to the general population can be quite challenging in either scenario.

7. Participants can be influenced by their current situation. Human error isn’t just confined to the researchers. Participants in an experimental research study can also be influenced by extraneous variables. There could be something in the environment, such an allergy, that creates a distraction. In a conversation with a researcher, there may be a physical attraction that changes the responses of the participant. Even internal triggers, such as a fear of enclosed spaces, could influence the results that are obtained. It is also very common for participants to “go along” with what they think a researcher wants to see instead of providing an honest response.

8. Manipulating variables isn’t necessarily an objective standpoint. For research to be effective, it must be objective. Being able to manipulate variables reduces that objectivity. Although there are benefits to observing the consequences of such manipulation, those benefits may not provide realistic results that can be used in the future. Taking a sample is reflective of that sample and the results may not translate over to the general population.

9. Human responses in experimental research can be difficult to measure. There are many pressures that can be placed on people, from political to personal, and everything in-between. Different life experiences can cause people to react to the same situation in different ways. Not only does this mean that groups may not be comparable in experimental research, but it also makes it difficult to measure the human responses that are obtained or observed.

The advantages and disadvantages of experimental research show that it is a useful system to use, but it must be tightly controlled in order to be beneficial. It produces results that can be replicated, but it can also be easily influenced by internal or external influences that may alter the outcomes being achieved. By taking these key points into account, it will become possible to see if this research process is appropriate for your next product, theory, or idea.

Descriptive Research

Oct 22, 2012

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Descriptive Research. Marketing Research – MKTG 446. Angela D’Auria Stanton, Ph.D. Descriptive Research. Descriptive research (often referred to as survey research) is aimed at characterizing phenomena and identifying association among selected variables. Descriptive research can be used to:

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Descriptive Research Marketing Research – MKTG 446 Angela D’Auria Stanton, Ph.D.

Descriptive Research • Descriptive research (often referred to as survey research) is aimed at characterizing phenomena and identifying association among selected variables. Descriptive research can be used to: • describe the characteristics of certain groups • estimate the proportion of people in a specified population who behave in a certain way • make specific predictions • Some specific examples of descriptive studies include: • The proportion of companies that have an electronic storefront • Target customer's evaluation of key product attributes • Measuring employee satisfaction

Classification of Descriptive Studies • Longitudinal Surveys • Provides a series of pictures that, when pierced together, provide a movie of the situation the changes that are occurring. • The sample remains relatively constant through time and the sample members are measured repeatedly. • Cross-Sectional Studies • Provides a snapshot of the variables of interest at a single point in time. • The sample is typically selected to be representative of some known universe.

Longitudinal Data – Advantages

An Example of a Panel Design Number of Households in Panel Purchasing Each Brand of Detergent

Another Way of Looking at It Loyalty Analysis

Survey Research Methods • Personal Interview • Telephone • Self-Administered • Online (computer administered)

Survey Research Methods • Personal Interviews (or Person Administered Surveys) – requires the presence of a trained human interviewer who asks questions and records the subject’s answers • In-home or In-office • Executive interview • Mall intercept • Purchase intercept

By physically being there, the interviewer may persuade the person to supply answers (probably the highest response rate). Information on the situation may be observed, without asking. Best for getting response from specific, identified person. Versatility of questioning methods (and flexibility in sequencing) and use of visual materials. Allows for probing of open-ended questions and clarification of ambiguous questions. Long questionnaires may be used successfully under interviewer's urging. If the respondent is having trouble understanding, interviewer may notice and remedy this. Selection of sample members can be more precise. Expensive and time intensive. Generally narrow distribution. People may be reluctant to talk with strangers. Often difficult to identify individuals to include in the sampling frame. Interviewer's presence, mannerisms and inflections may bias responses. Respondents know that they can be identified, which may inhibit their willingness to give information. Great difficulty in trying to supervise and control field interviewers. Staffing with capable interviewers, especially when conducted in distant places. May inaccurately record respondent’s answers Personal Interviews Advantages Disadvantages

Survey Research Methods • Telephone Interviews – personal interviews conducted via telephone technology typically from a central location Advantages Disadvantages • Relatively low cost • Wide distribution eliminates distance obstacle • Callbacks • Avoids personal travel to interview • Rapid coverage of even widely scattered sample. • Interviewers can be closely supervised • Less interview bias due to anonymity • Allows easy use of computer support (CATI/CAI) • Representative sampling frame difficult to establish due to unlisted numbers and increased use of cell phones • Inability to observe a respondent • Limitation to audio materials • Difficulty of conducting long interviews; no way to prevent hang-ups • More difficult to establish rapport over the telephone than in-person • Difficult to determine that appropriate respondent is being interviewed • Restrictions on types of data collected • Misperceptions and “sugging”

What to do about lack of listed numbers & cell phones? Plus-one dialing Random digit dialing Systematic random digit dialing Telephone Interviews Results of First Dialing Attempts * Probability of occurrence given eligible individual is at home

Survey Research Methods • Self-Administered – the respondent reads the survey questions and record his/her own answers without the presence of an interviewer. Typically done via mail, fax or drop-off. Advantages Disadvantages • Sampling frame easily developed when mailing lists are available • Respondent reads and answers questions without interviewer influence • May respond whenever convenient and without pressure • Any visual materials can be used • Relatively less expensive • More confidential information may be divulged • Anonymity of respondents easier to achieve • Eliminates the need for an interviewer • A majority may not respond, and those who do may not be typical • Significant time lag between the time the survey is mailed and when returned • Nothing can be learned except what is written on the questionnaire • The apparent low cost becomes relatively high when response is poor • Questions may be misunderstood or skipped • A person may read the entire form before answering any questions, so later questions can influence answers to earlier ones. • Significant problems in "pass along" effect.

Survey Research Methods • Online – typically conducted via e-mail or the Internet Advantages Disadvantages • Survey setup and execution can be done very quickly • Visual materials can be used • Respondent responds without being influenced by the interviewer • May respond whenever convenient and without pressure • Typically the least expensive • Can permit the respondent to be interrupted and later resume where he/she left off • Eliminates the cost of the interviewer. • Permits real-time data cleaning • Response rates are becoming an issue • May be confused with spam. • Will not be able to reach people without access or desire to use the Internet • Nothing can be learned except what is written on the questionnaire • No one present to stimulate replies or offer instructions. • Potential for "pass along" effect. • Respondent frustration if questionnaire forces response. • Issues with projectability of the sample

Selecting a Survey Method: Factors to Consider

Errors (or Bias) Affecting Survey Research Total Error = Sampling Error (difference between the actual sample results and the true population results) + Non-Sampling (Systematic) Error

Non-Sampling Errors • Respondent Errors • Non-response error • Response Bias • Deliberate falsification (social desirability error, auspices error, hostility, yea and nay-saying) • Unconscious misrepresentation (faulty recollection, fatigue, acquiescence error, extremity bias)

Non-Sampling Errors • Measurement/Research Design Errors • Construct development error • Scale measurement error (inappropriate questions, scale attributes or scale point descriptors) • Survey instrument error (improper sequence, length, poor or no instructions, etc.) • Data analysis error (use of wrong analytical technique, etc.) • Misinterpretation error (making the wrong inference, using only a selected portion of the study results)

Non-Sampling Errors • Problem Definition Errors • Misinterpreting the true nature of the problem situation • Administrative Errors • Data processing errors (coding, data entry or editing) • Interviewer error (cheating, recording error, misinterpretation, carelessness • Sample design error (sample selection error, sampling frame error, specifying the wrong population, etc.)

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10 Advantages & Disadvantages of Quantitative Research

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis.

Quantitative Research

When researchers look at gathering data, there are two types of testing methods they can use: quantitative research, or qualitative research. Quantitative research looks to capture real, measurable data in the form of numbers and figures; whereas qualitative research is concerned with recording opinion data, customer characteristics, and other non-numerical information.

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis. An integral component of quantitative research - and truly, all research - is the careful and considered analysis of the resulting data points.

There are several key advantages and disadvantages to conducting quantitative research that should be considered when deciding which type of testing best fits the occasion.

5 Advantages of Quantitative Research

Quantitative research is concerned with facts & verifiable information.

Quantitative research is primarily designed to capture numerical data - often for the purpose of studying a fact or phenomenon in their population. This kind of research activity is very helpful for producing data points when looking at a particular group - like a customer demographic. All of this helps us to better identify the key roots of certain customer behaviors.

Businesses who research their customers intimately often outperform their competitors. Knowing the reasons why a customer makes a particular purchasing decision makes it easier for companies to address issues in their audiences. Data analysis of this kind can be used for a wide range of applications, even outside the world of commerce.

Quantitative research can be done anonymously.

Unlike qualitative research questions - which often ask participants to divulge personal and sometimes sensitive information - quantitative research does not require participants to be named or identified. As long as those conducting the testing are able to independently verify that the participants fit the necessary profile for the test, then more identifying information is unnecessary.

Quantitative research processes don't need to be directly observed.

Whereas qualitative research demands close attention be paid to the process of data collection, quantitative research data can be collected passively. Surveys, polls, and other forms of asynchronous data collection generate data points over a defined period of time, freeing up researchers to focus on more important activities.

Quantitative research is faster than other methods.

Quantitative research can capture vast amounts of data far quicker than other research activities. The ability to work in real-time allows analysts to immediately begin incorporating new insights and changes into their work - dramatically reducing the turn-around time of their projects. Less delays and a larger sample size ensures you will have a far easier go of managing your data collection process.

Quantitative research is verifiable and can be used to duplicate results.

The careful and exact way in which quantitative tests must be designed enables other researchers to duplicate the methodology. In order to verify the integrity of any experimental conclusion, others must be able to replicate the study on their own. Independently verifying data is how the scientific community creates precedent and establishes trust in their findings.

5 Disadvantages of Quantitative Research

Limited to numbers and figures.

Quantitative research is an incredibly precise tool in the way that it only gathers cold hard figures. This double edged sword leaves the quantitative method unable to deal with questions that require specific feedback, and often lacks a human element. For questions like, “What sorts of emotions does our advertisement evoke in our test audiences?” or “Why do customers prefer our product over the competing brand?”, using the quantitative research method will not derive a meaningful answer.

Testing models are more difficult to create.

Creating a quantitative research model requires careful attention to be paid to your design. From the hypothesis to the testing methods and the analysis that comes after, there are several moving parts that must be brought into alignment in order for your test to succeed. Even one unintentional error can invalidate your results, and send your team back to the drawing board to start all over again.

Tests can be intentionally manipulative.

Bad actors looking to push an agenda can sometimes create qualitative tests that are faulty, and designed to support a particular end result. Apolitical facts and figures can be turned political when given a limited context. You can imagine an example in which a politician devises a poll with answers that are designed to give him a favorable outcome - no matter what respondents pick.

Results are open to subjective interpretation.

Whether due to researchers' bias or simple accident, research data can be manipulated in order to give a subjective result. When numbers are not given their full context, or were gathered in an incorrect or misleading way, the results that follow can not be correctly interpreted. Bias, opinion, and simple mistakes all work to inhibit the experimental process - and must be taken into account when designing your tests.

More expensive than other forms of testing.

Quantitative research often seeks to gather large quantities of data points. While this is beneficial for the purposes of testing, the research does not come free. The grander the scope of your test and the more thorough you are in it’s methodology, the more likely it is that you will be spending a sizable portion of your marketing expenses on research alone. Polling and surveying, while affordable means of gathering quantitative data, can not always generate the kind of quality results a research project necessitates.

Key Takeaways

Numerical data is a vital component of almost any research project. Quantitative data can provide meaningful insight into qualitative concerns. Focusing on the facts and figures enables researchers to duplicate tests later on, and create their own data sets.

To streamline your quantitative research process:

Have a plan. Tackling your research project with a clear and focused strategy will allow you to better address any errors or hiccups that might otherwise inhibit your testing.

Define your audience. Create a clear picture of your target audience before you design your test. Understanding who you want to test beforehand gives you the ability to choose which methodology is going to be the right fit for them.

Test, test, and test again. Verifying your results through repeated and thorough testing builds confidence in your decision making. It’s not only smart research practice - it’s good business.

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17 Advantages And Disadvantages Of PowerPoint

PowerPoint is a versatile and user-friendly multimedia presentation program compatible with most devices. It lets you make and share limitless presentations with ease. However, it comes with a fair share of disadvantages, like the complex features and tools, issues with performance on less powerful computers, and its price.

1. Available for All Major Operating Systems

2. abundant features, 3. widely accepted, 4. lots of themes and templates, 5. versatile interface, 6. relatively easy to use, 7. support various formats, 8. smooth integration with other office programs, 9. support add-in, 10. compare documents, 11. relatively easy to collaborate, 12. available mobile version, 13. password protection, 14. lack of innovation, 15. a bit complex to learn, 16. some performance issues on weak systems, 17. it’s relatively expensive, advantages and disadvantages of powerpoint – at a glance.

PowerPoint is available on Windows, macOS, iOS, Android , and the web.
PowerPoint has a rich set of features , including templates and themes.
Even for beginners, PowerPoint is relatively easy to use .
PowerPoint enables customization through a wide range of add-ins .
PowerPoint simplifies collaboration with others by allowing easy sharing and editing of presentations.
PowerPoint has limited innovation over its three-decade history, potentially making presentations feel dated.
Learning to use PowerPoint’s features and tools can be complex for some users.
PowerPoint may have performance issues on less powerful computers.
Compared to alternatives, PowerPoint can be relatively pricey if purchased outright.

Advantages Of PowerPoint

Microsoft PowerPoint is an excellent tool for presentations and more. Here are some of its key advantages:

PowerPoint is available for both Windows and macOS , as well as for mobile devices running iOS and Android. This makes it a convenient tool for creating presentations, regardless of what type of device you are using. You can also use PowerPoint for the Web in a web browser, making it even more accessible. Not a lot of presentation software offers such flexibility.

PowerPoint is the most feature-rich presentation software out there. It has everything you need to create a professional-looking presentation, including built-in templates, themes, and much more. Other presentation software simply cannot compete with PowerPoint in this regard.

PowerPoint is the most widely used presentation software, and it’s the industry standard tool for preparing presentations. People are generally familiar with how PowerPoint works, which makes it easy to use when giving presentations. It is also the most compatible presentation software , meaning that it can be opened and viewed on just about any device.

PowerPoint comes with a variety of built-in themes and templates that you can use to make your presentation look more professional. If you’re not a design expert, these templates can be a lifesaver. With just a few clicks, you can make your presentation look great without spending hours on design.

The interface of PowerPoint is also quite versatile. You can easily access all the needed features by using the toolbar options. Its interface is also customizable , so you can change it to suit your needs better.

PowerPoint is relatively easy to use , even if you’ve never used it before. Of course, it takes some time to learn all the features and how to use them effectively. However, you should be able to start creating basic presentations without much trouble.

You can open and edit presentations saved in various formats with PowerPoint. Some of the supported formats include pptx, ppt, gif, mp4, jpeg , and more. This is a convenient feature if you need to import or export presentations in variable programs. Other presentation software supports only a limited number of formats.

PowerPoint also integrates smoothly with other Microsoft Office programs, such as Word and Excel. This makes it easy to create presentations that include data from other Office programs. Moreover, PowerPoint files are supported by most online storage services, such as Google Drive and Dropbox, for seamless sharing.

PowerPoint also supports add-ins , which are small programs that add additional features to the software. There are a large number of add-ins available for PowerPoint that you can use to customize your presentations further.

The Review feature in PowerPoint allows you to compare two presentations side-by-side . This is a handy feature if you need to spot the differences between two versions of a presentation. It’s especially useful when you want to review the changes to your presentation made by someone else.

PowerPoint makes it relatively easy to collaborate with others on a presentation. You can easily share your presentation with others and allow them to view it or make changes by sharing a link. This is a convenient feature if you are working on a team project.

PowerPoint is also available in a mobile version , which allows you to create and edit presentations on the go. You can download the PowerPoint app for free from the App Store or Google Play to use on iOS or Android devices. This is a handy feature if you need to make last-minute changes to your presentation.

One of the features of the PowerPoint software that most users find useful is the password protection feature. This allows you to set a password for your presentation so that only those who know the password can open and view it. Most other presentation software does not include this component.

Disadvantages of PowerPoint

Now that we’ve looked at the advantages of PowerPoint, let’s take a look at some of its disadvantages:

It’s been around three decades since PowerPoint was first released, and in that time, it hasn’t seen a whole lot of innovation. This lack of innovation can make it feel dated compared to some of the newer presentation software options on the market. Some users find PowerPoint slides boring, as there is not much scope to create creative or interactive presentations.

The features and tools of PowerPoint can be a bit complex to learn , especially if you’ve never used the software before. It can take some time to get a grasp on how to use all the features effectively. And if you want to create more complex presentations, it may take even longer.

PowerPoint can also have some performance issues, especially on weak systems. The software can be a bit resource-intensive, so it may run slowly on older computers . Additionally, large or complex presentations may take longer to load and may not run as smoothly as you’d like.

If you want to purchase PowerPoint outright, it’s relatively expensive compared to some of the other presentation software options on the market. Google Slides offers many of the same features as PowerPoint, but it’s free to use.

PowerPoint is a widely used presentation software that is available for all major operating systems. It offers a large number of features and is widely accepted. However, it can be a bit complex to learn and is relatively expensive. Despite these disadvantages, PowerPoint is still a popular choice for creating presentations.

Advantages And Disadvantages Of Mobile Phones

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Open Access

Study Protocol

Evaluating variable selection methods for multivariable regression models: A simulation study protocol

Roles Conceptualization, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria

Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

Roles Writing – review & editing

Affiliation Institute of Biometry and Clinical Epidemiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

Roles Software, Writing – review & editing

Affiliations Institute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria, Austrian Agency for Health and Food Safety (AGES), Vienna, Austria

* E-mail: [email protected]

¶ Membership list can be found in the Acknowledgments section.

Theresa Ullmann,
Georg Heinze,
Lorena Hafermann,
Christine Schilhart-Wallisch,
Daniela Dunkler,
for TG2 of the STRATOS initiative

Published: August 9, 2024
https://doi.org/10.1371/journal.pone.0308543
Peer Review
Reader Comments

Researchers often perform data-driven variable selection when modeling the associations between an outcome and multiple independent variables in regression analysis. Variable selection may improve the interpretability, parsimony and/or predictive accuracy of a model. Yet variable selection can also have negative consequences, such as false exclusion of important variables or inclusion of noise variables, biased estimation of regression coefficients, underestimated standard errors and invalid confidence intervals, as well as model instability. While the potential advantages and disadvantages of variable selection have been discussed in the literature for decades, few large-scale simulation studies have neutrally compared data-driven variable selection methods with respect to their consequences for the resulting models. We present the protocol for a simulation study that will evaluate different variable selection methods: forward selection, stepwise forward selection, backward elimination, augmented backward elimination, univariable selection, univariable selection followed by backward elimination, and penalized likelihood approaches (Lasso, relaxed Lasso, adaptive Lasso). These methods will be compared with respect to false inclusion and/or exclusion of variables, consequences on bias and variance of the estimated regression coefficients, the validity of the confidence intervals for the coefficients, the accuracy of the estimated variable importance ranking, and the predictive performance of the selected models. We consider both linear and logistic regression in a low-dimensional setting (20 independent variables with 10 true predictors and 10 noise variables). The simulation will be based on real-world data from the National Health and Nutrition Examination Survey (NHANES). Publishing this study protocol ahead of performing the simulation increases transparency and allows integrating the perspective of other experts into the study design.

Citation: Ullmann T, Heinze G, Hafermann L, Schilhart-Wallisch C, Dunkler D, for TG2 of the STRATOS initiative (2024) Evaluating variable selection methods for multivariable regression models: A simulation study protocol. PLoS ONE 19(8): e0308543. https://doi.org/10.1371/journal.pone.0308543

Editor: Suyan Tian, The First Hospital of Jilin University, CHINA

Received: February 7, 2024; Accepted: July 25, 2024; Published: August 9, 2024

Copyright: © 2024 Ullmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: This manuscript is a protocol of a simulation study. We intend to share the software code after the study has been conducted and published. This will allow recreating our data and reproducing our simulation study.

Funding: This research was funded in part by the Austrian Science Fund (FWF, https://www.fwf.ac.at/en/ ) [I-4739-B] (for T.U. and C.W.) and by the German Research Foundation (DFG, https://www.dfg.de/en ) [RA 2347/8-1] (for L. H.). For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. The funders did not and will not have any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

Data-driven variable selection is frequently performed when modeling the associations between an outcome and multiple independent variables (sometimes also referred to as explanatory variables, covariates or predictors). Variable selection may help to generate parsimonious and interpretable models, and may also yield models with increased predictive accuracy. Despite these potential advantages, data-driven variable selection can also have unintended negative consequences that many researchers are not fully aware of. Variable selection induces additional uncertainty in the estimation process and may cause biased estimation of regression coefficients, model instability (i.e., models that are not robust with respect to small perturbations of the data set), and issues with post-selection inference such as underestimated standard errors and invalid confidence intervals [ 1 – 5 ].

A recent review [ 1 ] provided guidance about variable selection and gave an overview of possible consequences of variable selection. However, there are few systematic simulation studies that compare different variable selection methods with respect to their consequences for the resulting models (for some exceptions, see [ 6 – 10 ]). While many articles proposing new variable selection methods include a comparison with existing methods (based on simulated or real data), these comparisons are typically somewhat limited, often comparing the new method to only one to three competitors, even though there are many more existing methods. Moreover, these articles are inherently biased towards demonstrating superiority of the new methods. In particular, such studies cannot be considered as neutral . A neutral comparison study is a study whose authors do not have a vested interest in one of the competing methods, and are (as a group) approximately equally familiar with all considered methods [ 11 , 12 ]. More neutral comparison studies about existing variable selection methods are needed to better understand their properties, a viewpoint that aligns with the goals of the STRATOS initiative (STRengthening Analytical Thinking for Observational Studies [ 13 ]). The STRATOS initiative is an international consortium of biostatistical experts, and aims to provide guidance in the design and analysis of observational studies for specialist and non-specialist audiences. This perspective motivates our comprehensive simulation study.

We will focus on descriptive modeling (i.e., describing the relationship between the outcome and the independent variables in a parsimonious manner) and predictive modeling (i.e., predicting the outcome as accurately as possible) [ 14 ]. Our setting is multivariable regression analysis with one outcome variable. The outcome is either continuous (linear regression) or binary (logistic regression). We simulate data in a low-dimensional scenario (20 variables consisting of 10 true predictors and 10 noise variables). Different variable selection methods with multiple parameter settings are compared: forward selection, stepwise forward selection, backward elimination, augmented backward elimination [ 15 ], univariable selection, univariable selection followed by backward elimination, the Lasso [ 16 ], the relaxed Lasso [ 9 , 17 ], and the adaptive Lasso [ 18 ]. We compare the performances of these methods with respect to false inclusion and/or exclusion of variables, consequences on bias and variance of the estimated regression coefficients, the validity of the confidence intervals for the coefficients, the accuracy of the estimated variable importance ranking, and finally the predictive performance of the selected models.

Using simulated instead of real data allows us to a) know the true data generating process and b) systematically vary several data characteristics [ 19 , 20 ]. For example, we will include varying sample sizes and R 2 , as the consequences of variable selection depend on these parameters. To ensure that the simulation results are practically relevant, we use real data as the starting point for our simulation. The distributions and correlation structure of the variables are based on data from the National Health and Nutrition Examination Survey (NHANES) [ 21 ]. The choice of variables and true regression coefficients is inspired by an applied study about predicting the difference between ambulatory/home and clinic blood pressure readings [ 22 ]. Our simulated data thus mimics real cardiovascular data.

Our focus is on low-dimensional data, which is reflected in our simulation setting with twenty independent variables. Data of this type frequently appears in medicine and other application fields, and researchers often apply variable selection in this context. For example, a systematic review of models for COVID-19 prognosis [ 23 , 24 ] identified 236 newly developed regression models for prediction. Data-driven variable selection was applied (and reported) for 196 models. In 165 models both the number of candidate predictors (i.e., the predictors considered at the start of data-driven selection) and the number of predictors in the final model were reported; the median numbers were 28 (range 4–130), and 6 (range 1–38), respectively. This demonstrates that low- to medium-dimensional data played an important role in COVID-19 prediction research. Of course, data-driven variable selection is also relevant for high-dimensional data. Comparing variable selection methods for high-dimensional data would require a different study design and is not the purpose of this planned simulation study.

As mentioned above, neutrality is an important goal when conducting systematic comparison studies. “Perfect” neutrality may be the ultimate goal, but this ideal can be difficult to achieve in practice. While we aim to be as neutral as possible, we disclose (for the purpose of full transparency) that one of the methods for variable selection included in our comparison, namely augmented backwards elimination, was originally proposed by two authors of the present study protocol [ 15 ]. Our goal was to not let this fact influence our choice of study design, though unconscious biases can never be fully excluded. Striving for as much neutrality as possible motivated us to publish this study protocol. This will allow us to integrate the comments of reviewers before performing the simulation. For the design of our study, results from previous smaller simulation studies and pilot studies were taken into account [ 1 ]; however, the study outlined in this protocol has not yet been run and analyzed. Preregistration of study protocols for simulation studies/methodological studies is still very rare (for an exception, see [ 25 ]). However, this practice could offer similar advantages to those discussed for preregistration in applied research, such as increased transparency and prevention of “hindsight bias” [ 26 ]. Potential advantages of preregistering protocols for simulation studies, but also possible limitations and challenges, are discussed more extensively elsewhere [ 27 ].

A specific goal of our simulation study is to evaluate previously published recommendations about variable selection [ 1 ], which we discuss in Section 2. We then describe our simulation design in Section 3, explain the planned code review in Section 4, and conclude the protocol with some final remarks in Section 5.

2 Previous variable selection recommendations

Varied viewpoints exist in the literature as to whether researchers should apply data-driven variable selection, and, if so, which methods and parameters are deemed preferable. Some authors generally caution against data-driven variable selection, stressing potential negative consequences [ 5 ]. Other authors put more focus on potential advantages of variable selection and are more optimistic about using selection methods, at least if the sample size is large enough and if selection is accompanied by a stability analysis [ 28 ]. In a review conducted by three co-authors of the present study protocol, Heinze et al. [ 1 ] summarized different perspectives from the literature. Drawing upon existing recommendations, but also taking their own experience and a small simulation study into account, they derived recommendations for the usage of variable selection methods. These recommendations consider both benefits and drawbacks of variable selection, thereby reconciling different viewpoints on the matter. The recommendations depend on the “events-per-variable” (EPV) in the data. The EPV is the ratio between sample size (in linear regression) or the number of the less frequent outcome (in logistic regression) and the number of independent variables. Data-driven variable selection is applied on a carefully designed “global” model which includes all independent variables relevant for the research question. The denominator of EPV refers to the number of design variables (including possible dummy variables and other constructed variables) in this global model. The following bullet points list the recommendations, and how we plan to evaluate them.

EPV > 25: While variable selection may generally work well for a large EPV value, the selection of independent variables with small effect size can still be unstable. If backward elimination is used, a stringent threshold of α = 0.05 or selection with the BIC may lead to a more accurate selection of variables than milder thresholds. In our study : We will check whether selection rates of variables with small standardized regression coefficients (e.g., ±0.25) are notably different from either 0 or 1 (which indicates instability). For backward elimination, we will evaluate whether the selection of variables is more accurate when using the threshold α = 0.05 or the BIC (which corresponds to even stricter thresholds for our considered sample sizes [ 1 ]), compared to using larger α values.
10 < EPV ≤ 25: In general, the selection of variables might be unstable with such an EPV. When variables with unclear effect size are selected, their effects might be over-estimated. Penalized estimation (Lasso) or postestimation shrinkage is thus recommended. If backward elimination is used, a threshold corresponding to selection with the AIC (approximately α = 0.157) is recommended, but not smaller α values. In our study : Again, we will evaluate stability by checking whether selection rates of variables, particularly those with small standardized regression coefficients, are notably different from either 0 or 1. We will also calculate the conditional bias (i.e., bias conditioned on selection) of the variables and analyze whether variables with small standardized regression coefficients have large conditional bias away from zero. For backward elimination, we will evaluate to which extent a threshold of α = 0.157 (or an even milder threshold of α = 0.5) selects the true predictors more frequently than smaller thresholds (i.e., a fixed threshold of α = 0.05 or selection with the BIC) [ 3 ].
EPV ≤ 10: Data-driven variable selection is generally not recommended. In our study : We will analyze whether variable selection has negative consequences with respect to the different performance criteria.

The results of variable selection are not only influenced by EPV, but also by other aspects such as the R 2 of the model. We will thus consider different R 2 values in our simulation study. The recommendations above do not take R 2 into account, as the R 2 of the model is typically not known prior to the data analysis.

3 Simulation design

Morris et al. [ 19 ] proposed to describe the following components when reporting a simulation study: the aims of the study (A), the data-generating mechanisms (D), the estimands (i.e., the population quantities which are estimated) and other targets of interest (E), the methods to be compared (M), and the performance measures used for evaluating the methods (P). The ADEMP components of our study are briefly summarized in Tables 1 and 2 . We now describe the components in more detail.

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https://doi.org/10.1371/journal.pone.0308543.t001

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3.1 Aims (A)

We aim to compare different variable selection methods for multivariable linear or logistic regression, with respect to their consequences for the resulting models. We consider consequences on bias and variance of the estimated regression coefficients, validity of confidence intervals for the coefficients, false inclusion or exclusion of variables, and predictive performance. We analyze the behavior of variable selection methods…

… depending on sample size/EPV, with particular focus on evaluating the recommendations of Heinze et al. [ 1 ],
… depending on the R 2 of the population model,
… depending on the modeling goal (description or prediction),
… when functional forms are misspecified (i.e., when fitting models assuming linear functional forms of continuous predictors even though the true functional forms are nonlinear),
… when switching from our realistic scenario that mimics cardiovascular data to simplified scenarios (i.e., all variables are normally distributed and/or uncorrelated).

3.2 Data-generating mechanisms (D)

3.2.1 simulation of independent variables (predictors and noise variables)..

We simulate 20 independent variables: 10 true predictors (from now on just called “predictors”) and 10 noise variables. The correlation structure and distributions are based on real-world data from the 2013–14 and 2015–2016 cycles of the National Health and Nutrition Examination Survey (NHANES) [ 21 ]. To choose suitable variables in the NHANES data, we drew inspiration from a regression model reported by Sheppard et al. [ 22 ] for predicting the difference between diastolic blood pressure readings as measured ambulatory/at home versus in the clinic. The variables are described in detail in S1 Appendix . The correlation matrix Σ for the simulation is based on the empirical correlation matrix of the variables. For better interpretability, we set correlations below 0.15 to zero and round all values to the closest multiple of 0.05 (see S1 Fig and S1 Table for the resulting correlation matrix).

To obtain distributions from the NHANES data, we fit Bernoulli distributions for the binary variables, and normal distributions, log-normal distributions, or approximations of the empirical cumulative distribution function (CDF) for the continuous variables. For each continuous variable, we truncate its distribution with the minimum of the variable in the NHANES data as the lower bound and the maximum as the upper bound. The resulting distributions are as follows (see also Fig 1 ):

predictors: X 1 (log-normal), X 2 (continuous with approximated CDF), X 3 (log-normal), X 4 (binary, p = 0.50), X 5 (normal), X 6 (binary, p = 0.29), X 7 (log-normal), X 8 (log-normal), X 9 (normal), X 10 (binary, p = 0.11)
noise variables: X 11 (log-normal), X 12 (normal), X 13 (log-normal), X 14 (binary, p = 0.61), X 15 (normal), X 16 (binary, p = 0.20), X 17 (log-normal), X 18 (normal), X 19 (normal), X 20 (binary, p = 0.20)

Predictors are ordered by absolute values of standardized regression coefficients. Histograms are based on a large simulated dataset ( n = 100, 000).

https://doi.org/10.1371/journal.pone.0308543.g001

The distributions, together with the correlation matrix Σ, are then used as input for the normal-to-anything (NORTA) method for simulation [ 29 , 30 ].

3.2.2 Choice of regression coefficients.

This choice reflects a mixture of stronger and weaker effects, a situation typical for many applications in biology and medicine. We would expect different behaviors of the predictors during variable selection depending on their effects.

The regression coefficients for the noise variables X 11 , …, X 20 are set to zero.

3.2.3 Simulation of outcome Y .

The outcome Y is simulated as follows:

3.2.4 Nonlinear functional forms.

So far, we assumed that the functional forms of the effects of continuous predictors on Y are linear. In applied studies in biology and medicine, the actual functional forms of such variables might often be nonlinear, but researchers nonetheless fit a model with linear functional forms, e.g., because they are not aware that some functional forms might be nonlinear, or because they prefer a simpler model. To analyze the behavior of variable selection methods in this scenario, we include settings 1b-7b (corresponding to settings 1–7) where all predictors have nonlinear functional forms. The models that we consider for analysing the simulated data (linear/logistic regression) will not take the nonlinear functional forms into account and will thus be misspecified.

For each continuous predictor X j , we define a function g j ( x ) that describes the nonlinear functional form of the effect of the predictor on Y . We choose various functional forms: quadratic, log-quadratic, exponential and sigmoid. The functions are depicted in S3 Fig ; exact definitions are given in S1 Appendix .

After determining β ( g ) , the outcome Y is simulated as previously described in Section 3.2.3, with xβ replaced by the nonlinear composite predictor.

https://doi.org/10.1371/journal.pone.0308543.t003

For the global model in the settings with nonlinear effects, we will not only calculate the usual standard errors of the regression coefficients, but also robust standard errors [ 31 ], to check whether robust SEs improve the coverage of the confidence intervals. If robust SEs improve the coverage for the global model, it would be interesting to analyze whether this is also the case for models obtained by variable selection; however, combining robust standard errors with variable selection requires some further work and would go beyond the scope of the proposed study. For now, we will restrict the investigation of robust SEs to the global model for linear regression.

3.2.5 Simplified settings.

While our main focus is on simulating variables of various distribution types (e.g., Bernoulli, normal, and log-normal) and with correlation matrix Σ based on the empirical correlation matrix from the NHANES data ( S1 Table ), we are also interested in the behavior of the variable selection methods for data with simpler distribution-correlation structures. We thus consider the three following simplified scenarios:

The variables have the same individual distributions as described in Section 3.2.1 ( Fig 1 ), but are not correlated.

Depending on the results for settings 1b-2b and 4b-7b with nonlinear effects, we might additionally consider nonlinear effects for the simplified scenario 3 (variables not multivariate normal and not correlated).

3.2.6 Sample sizes.

For linear regression, we consider eight different sample sizes: 100, 200, 400, 500, 800, 1600, 3200, and 6400. These sample sizes result when doubling sample size six times from 100. Additionally, the sample size 500 is included because it corresponds to EPV = 25, and this EPV value was specifically mentioned in the recommendations of Heinze et al. [ 1 ].

Because this procedure is unstable for small event rates, we do not use the alignment based on standard errors for event rate 0.05. Instead, we choose sample sizes corresponding to the EPV values in linear regression.

The resulting sample sizes are displayed in Table 4 . The numbers below the sample sizes indicate the corresponding EPV values. For event rate 0.05, we will first include sample sizes only up to 10,000 (EPV = 25) to save computation time. We expect the variable selection methods to behave similarly for both event rates (0.3 and 0.05). If we observe different behaviors for event rate 0.05, we will include the additional sample sizes.

https://doi.org/10.1371/journal.pone.0308543.t004

In S1 Appendix , we additionally report expected shrinkage factors for each setting, based on sample size and R 2 [ 32 , 33 ].

3.3 Estimands and other targets (E)

As estimands, we consider the true regression coefficients of the data generating models. As further targets, we are interested in model selection (e.g., whether the true model is selected) and predictive performance of the selected models.

For the settings with linear functional forms, the regression coefficient estimands are the coefficients β (respectively c β for logistic regression) as described in Sections 3.2.2 and 3.2.3. For the settings with nonlinear effects, we cannot take the coefficients β ( g ) as defined in 3.2.4 as estimands, because our linear/logistic regression models will not take nonlinear functional forms into account and will thus be misspecified.

3.4 Methods (M)

3.4.1 overview of variable selection methods..

We include the following methods:

Forward selection with AIC: starting from the model containing only the intercept, variables are iteratively added to the model based on their capability to decrease the AIC when included.
Stepwise forward selection with AIC (i.e., forward selection with backward elimination steps): like simple forward selection, this method starts from the intercept model and adds variables based on the AIC. However, in each step, re-exclusion of already selected variables is allowed, based on the capability to decrease the AIC when removed.
Backward elimination with α = 0.05, with BIC, with AIC, and with α = 0.5: starting from the global model, variables are iteratively removed, either based on their capability to decrease the BIC/AIC when removed, or based on the p -values of their coefficients. We do not consider a stepwise variant of backward elimination with forward selection steps, following the recommendations of Royston and Sauerbrei [ 28 , p. 32] who argue that allowing re-inclusion of removed variables in backward elimination is rarely relevant, while allowing re-exclusion of included variables may cause a notable difference for forward selection.
Augmented backward elimination (ABE) with AIC [ 15 ]: backward elimination is combined with the change-in-estimate criterion [ 34 , 35 ]. A variable that would be removed in backward elimination based on AIC may stay in the model if its removal would induce a large change in the estimated regression coefficients of the other variables that are currently in the model. As threshold for the standardized change-in-estimate, we choose τ = 0.05. We will use the R package abe [ 36 ].
Univariable selection with α = 0.05 and α = 0.20: a variable is selected if its regression coefficient in a univariable model is significant at level α . While many authors have advised against using univariable selection [ 5 , 37 , 38 ], the method is still often used in practice, which is why we include it in our simulation study.
Univariable selection with α = 0.20, followed by backward elimination with α = 0.05: frequently, researchers use this combination instead of using only univariable selection or only backward elimination [ 39 , 40 ] However, the warnings against univariable selection still apply to the combination method.
Lasso [ 16 ]: a penalty on the coefficients is added to the OLS criterion (linear regression) or the negative log-likelihood (logistic regression), causing shrinkage of the coefficients toward zero and setting some of them to exactly zero.
Relaxed Lasso [ 9 , 17 ]: variables are selected with the Lasso, but the shrinkage of the coefficients of the selected variables is relaxed by refitting the model with the selected variables without penalty.
Adaptive Lasso [ 18 ]: first, the global linear/logistic model is fit, then a Lasso with variable-specific weights for the penalty is estimated. The estimates from the first step serve to get the variable-specific weights for the second step: the weights are calculated such that a variable with larger regression coefficient in the first step is penalized less than a variable with smaller regression coefficient. For all variants of the Lasso, we will use the R package glmnet [ 41 ]. The complexity parameter λ will be tuned with 10-fold cross-validation (CV). As performance criterion for the prediction on test sets during CV, we use the mean squared error for linear regression and deviance for logistic regression. For the relaxed Lasso, we additionally consider tuning λ with the BIC.

We also consider the global model with all variables.

3.4.2 Firth correction in logistic regression.

In the models for logistic regression, separation may occur (i.e., perfect separation of events and non-events by a linear combination of covariates), particularly for small to medium sample sizes and low event rates [ 42 ]. In this case, at least one parameter estimate is infinite. While separation can be detected by linear programming [ 43 ], we found that in practice, a simple and robust check can be performed by inspecting the model standard errors of the regression coefficients. If at least one standard error is extremely large, this indicates separation. A possible solution to the problem of separation is to apply the Firth correction to obtain finite parameter estimates [ 42 , 44 ].

In the simulation settings for logistic regression, we check for each individual simulated dataset whether separation occurs. In the case of separation, we apply the Firth correction (with the FLIC intercept correction [ 45 ] to obtain unbiased predictions), otherwise we use the standard logistic regression. When Firth correction is applied, confidence intervals for the regression coefficients are calculated based on the profile penalized likelihood, otherwise based on the profile likelihood.

We describe our procedure to check for separation based on the model standard errors of the coefficients in S1 Appendix .

3.5 Performance measures (P)

We organize the performance measures into three categories, based on which estimands/targets they pertain to. Formulas for all performance measures are given in S1 Appendix .

Performance measures for model selection as target include the selection rate of the true model consisting exactly of the ten predictors, the selection rate of an “over-selection” model which we define as a model including all predictors as well as at least one noise variable (previously called an “inflated” model [ 15 ]), and the selection rate of any “under-selection” model defined as a model not containing all predictors but possibly including noise variables (previously called a “biased” model [ 15 ]).

The performance measures for the regression coefficients and for model selection are primarily relevant for descriptive models, while performance measures for predictive performance are mainly relevant for prediction models. However, a descriptive model may also be suitable for prediction; therefore, performance measures for prediction could also be relevant for descriptive modeling. Vice versa, in prediction models, aspects such as interpretability, fairness etc. often play an important role; researchers might thus consider performance measures such as bias of coefficients also for prediction models.

3.6 Monte Carlo errors and number of simulation runs

4 Code review

To ensure reproducibility, as well as readability, the code will be checked by another researcher (a “code reviewer”) who works at the same institute as the first, second and last author of this protocol, but was not involved in planning the study. After writing the code, the first author (T.U.) will hand over the code to the code reviewer, together with instructions for running the code as well as some partial results (using less than the full n sim = 2000 repetitions). The code reviewer will then check the plausibility of the partial results and provide feedback on the simulation code, focusing on a) data generation, b) the implementation of the compared models, and c) the implementation of the performance measures applied to these models. Once T.U. and the code reviewer have agreed upon a final version of the code, T.U. will re-run the partial results, and the code reviewer will check the complete computational reproducibility by re-running the code on another machine. This check for reproducibility is done on the partial results as the generation of the final results is expected to require large amounts of computational resources. Once the reproducibility check has successfully concluded, T.U. will perform the full n sim = 2000 repetitions to generate the final results.

5 Final remarks

Our simulation study will enable researchers to better understand the consequences of variable selection, and will clarify differences in the performance of different selection methods depending on the considered scenarios. To make the results of the study more accessible and interpretable, we plan to display all results in an interactive web app (Shiny app) that will be published alongside the main paper. We will also make our code available on a Git repository, and will specify random seeds to ensure reproducibility of the results.

The performance measures for our study (Section 3.5) are defined as expected values and probabilities. Their estimation by simulation thus always involves taking the mean over (a part of) the simulation repetitions. However, if one only calculates the mean over the repetitions, one might miss relevant properties of the distribution of the values over the simulation repetitions. We will thus use distribution plots and correlation analyses to evaluate the simulation results in more detail [ 19 ]. Moreover, we will analyze how many variables were selected by each variable selection method. We did not include model size as a performance measure in Section 3.5 because there is no clear target value and smaller/larger values are not automatically better/worse (a smaller model size is preferable in some applications, but might be less relevant in others). A specific focus on model size (e.g., comparing different variable selection methods under constraints w.r.t. the number of chosen variables) would require a different study design.

In future work, it would be interesting to consider various extensions of our simulation. For example, while we focus on linear and logistic regression in the present protocol, data-driven variable selection is also often used in the context of survival analysis. We plan to conduct a further simulation study comparing different data-driven variable selection methods for Cox regression and the accelerated failure time model.

In the present study, we include several settings where all predictors have true nonlinear functional forms, but we nevertheless fit all models with linear functional forms; this mimics the frequent misspecification of models in practice. Generally, when fitting a regression model with linear effects, it is advisable to check for misspecification by analyzing the residuals. If misspecification is only mild, then a model with linear effects might still be justifiable. If misspecification is too severe, functional form selection can be performed to account for nonlinear effects, e.g., with spline-based approaches. In future work, our study could be extended by considering the combination of variable selection and functional form selection, which is a complex issue [ 39 ].

We focus on low-dimensional data in our study. Future studies could compare variable selection methods for high-dimensional data. Finally, our study considers variable selection in a frequentist framework. Future simulation studies could also evaluate Bayesian methods for variable selection.

Supporting information

S1 fig. correlation network graph..

https://doi.org/10.1371/journal.pone.0308543.s001

S2 Fig. Absolute standardized regression coefficients plotted against coefficients of determination for each independent variable.

https://doi.org/10.1371/journal.pone.0308543.s002

S3 Fig. Nonlinear effects.

https://doi.org/10.1371/journal.pone.0308543.s003

S1 Appendix. Details of the simulation design.

https://doi.org/10.1371/journal.pone.0308543.s004

S1 Table. Correlation table.

https://doi.org/10.1371/journal.pone.0308543.s005

Acknowledgments

We would like to thank the members of Topic Group (TG) 2 and the Publications Panel of the STRengthening Analytical Thinking for Observational Studies (STRATOS) initiative for helpful comments. In particular, we thank Willi Sauerbrei, Frank Harrell, Nadja Klein and Harald Binder.

At the time of submission, STRATOS TG2 consisted of the following members (in alphabetical order): Michal Abrahamowicz, Harald Binder, Daniela Dunkler, Frank Harrell, Georg Heinze, Marc Henrion, Michael Kammer, Aris Perperoglou, Willi Sauerbrei, and Matthias Schmid. The group is co-chaired by Georg Heinze ( [email protected] ), Aris Perperoglou, and Willi Sauerbrei.

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Descriptive Studies- Types, Applications, Advantages, Limitations

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Study designs: Part 2 – Descriptive studies

Priya Ranganathan

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2. Participants and Sampling

3. Data Collection Techniques

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6. Non-Experimental

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Presentation on theme: "Descriptive Research Design: Survey and Observation"— Presentation transcript:

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Types of Descriptive Research: Methods and Examples

What is Descriptive Research?