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Research Process Steps: What they are + How To Follow

There are various approaches to conducting basic and applied research. This article explains the research process steps you should know. Whether you are doing basic research or applied research, there are many ways of doing it. In some ways, each research study is unique since it is conducted at a different time and place.

Conducting research might be difficult, but there are clear processes to follow. The research process starts with a broad idea for a topic. This article will assist you through the research process steps, helping you focus and develop your topic.

Research Process Steps

The research process consists of a series of systematic procedures that a researcher must go through in order to generate knowledge that will be considered valuable by the project and focus on the relevant topic.

To conduct effective research, you must understand the research process steps and follow them. Here are a few steps in the research process to make it easier for you:

Step 1: Identify the Problem

Finding an issue or formulating a research question is the first step. A well-defined research problem will guide the researcher through all stages of the research process, from setting objectives to choosing a technique. There are a number of approaches to get insight into a topic and gain a better understanding of it. Such as:

• A preliminary survey
• Case studies
• Interviews with a small group of people
• Observational survey

Step 2: Evaluate the Literature

A thorough examination of the relevant studies is essential to the research process . It enables the researcher to identify the precise aspects of the problem. Once a problem has been found, the investigator or researcher needs to find out more about it.

This stage gives problem-zone background. It teaches the investigator about previous research, how they were conducted, and its conclusions. The researcher can build consistency between his work and others through a literature review. Such a review exposes the researcher to a more significant body of knowledge and helps him follow the research process efficiently.

Step 3: Create Hypotheses

Formulating an original hypothesis is the next logical step after narrowing down the research topic and defining it. A belief solves logical relationships between variables. In order to establish a hypothesis, a researcher must have a certain amount of expertise in the field. 

It is important for researchers to keep in mind while formulating a hypothesis that it must be based on the research topic. Researchers are able to concentrate their efforts and stay committed to their objectives when they develop theories to guide their work.

Step 4: The Research Design

Research design is the plan for achieving objectives and answering research questions. It outlines how to get the relevant information. Its goal is to design research to test hypotheses, address the research questions, and provide decision-making insights.

The research design aims to minimize the time, money, and effort required to acquire meaningful evidence. This plan fits into four categories:

• Exploration and Surveys
• Data Analysis
• Observation

Step 5: Describe Population

Research projects usually look at a specific group of people, facilities, or how technology is used in the business. In research, the term population refers to this study group. The research topic and purpose help determine the study group.

Suppose a researcher wishes to investigate a certain group of people in the community. In that case, the research could target a specific age group, males or females, a geographic location, or an ethnic group. A final step in a study’s design is to specify its sample or population so that the results may be generalized.

Step 6: Data Collection

Data collection is important in obtaining the knowledge or information required to answer the research issue. Every research collected data, either from the literature or the people being studied. Data must be collected from the two categories of researchers. These sources may provide primary data.

• Questionnaire

Secondary data categories are:

• Literature survey
• Official, unofficial reports
• An approach based on library resources

Step 7: Data Analysis

During research design, the researcher plans data analysis. After collecting data, the researcher analyzes it. The data is examined based on the approach in this step. The research findings are reviewed and reported.

Data analysis involves a number of closely related stages, such as setting up categories, applying these categories to raw data through coding and tabulation, and then drawing statistical conclusions. The researcher can examine the acquired data using a variety of statistical methods.

Step 8: The Report-writing

After completing these steps, the researcher must prepare a report detailing his findings. The report must be carefully composed with the following in mind:

• The Layout: On the first page, the title, date, acknowledgments, and preface should be on the report. A table of contents should be followed by a list of tables, graphs, and charts if any.
• Introduction: It should state the research’s purpose and methods. This section should include the study’s scope and limits.
• Summary of Findings: A non-technical summary of findings and recommendations will follow the introduction. The findings should be summarized if they’re lengthy.
• Principal Report: The main body of the report should make sense and be broken up into sections that are easy to understand.
• Conclusion: The researcher should restate his findings at the end of the main text. It’s the final result.

LEARN ABOUT: 12 Best Tools for Researchers

The research process involves several steps that make it easy to complete the research successfully. The steps in the research process described above depend on each other, and the order must be kept. So, if we want to do a research project, we should follow the research process steps.

QuestionPro’s enterprise-grade research platform can collect survey and qualitative observation data. The tool’s nature allows for data processing and essential decisions. The platform lets you store and process data. Start immediately!

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 22 April 2024, from https://www.scribbr.co.uk/research-methods/research-design/

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Shona McCombes

Understanding Research Study Designs

Table of Contents

In order to find the best possible evidence, it helps to understand the basic designs of research studies. The following basic definitions and examples of clinical research designs follow the “ levels of evidence.”

Case Series and Case Reports

Case control studies, cohort studies, randomized controlled studies, double-blind method, meta-analyses, systematic reviews.

These consist either of collections of reports on the treatment of individual patients with the same condition, or of reports on a single patient.

• Case series/reports are used to illustrate an aspect of a condition, the treatment or the adverse reaction to treatment.
• Example : You have a patient that has a condition that you are unfamiliar with. You would search for case reports that could help you decide on a direction of treatment or to assist on a diagnosis.
• Case series/reports have no control group (one to compare outcomes), so they have no statistical validity.
• The benefits of case series/reports are that they are easy to understand and can be written up in a very short period of time.

Patients who already have a certain condition are compared with people who do not.

• Case control studies are generally designed to estimate the odds (using an odds ratio) of developing the studied condition/disease. They can determine if there is an associational relationship between condition and risk factor
• Example: A study in which colon cancer patients are asked what kinds of food they have eaten in the past and the answers are compared with a selected control group.
• Case control studies are less reliable than either randomized controlled trials or cohort studies.
• A major drawback to case control studies is that one cannot directly obtain absolute risk (i.e. incidence) of a bad outcome.
• The advantages of case control studies are they can be done quickly and are very efficient for conditions/diseases with rare outcomes.

Also called longitudinal studies, involve a case-defined population who presently have a certain exposure and/or receive a particular treatment that are followed over time and compared with another group who are not affected by the exposure under investigation.

• Cohort studies may be either prospective (i.e., exposure factors are identified at the beginning of a study and a defined population is followed into the future), or historical/retrospective (i.e., past medical records for the defined population are used to identify exposure factors).
• Cohort studies are used to establish causation of a disease or to evaluate the outcome/impact of treatment, when randomized controlled clinical trials are not possible.
• Example: One of the more well-know examples of a cohort study is the Framingham Heart Study, which followed generations of residents of Framingham, Massachusetts.
• Cohort studies are not as reliable as randomized controlled studies, since the two groups may differ in ways other than the variable under study.
• Other problems with cohort studies are that they require a large sample size, are inefficient for rare outcomes, and can take long periods of time. 

This is a study in which 1) There are two groups, one treatment group and one control group. The treatment group receives the treatment under investigation, and the control group receives either no treatment (placebo) or standard treatment. 2) Patients are randomly assigned to all groups. 

• Randomized controlled trials are considered the “gold standard” in medical research. They lend themselves best to answering questions about the effectiveness of different therapies or interventions.
• Randomization helps avoid the bias in choice of patients-to-treatment that a physician might be subject to. It also increases the probability that differences between the groups can be attributed to the treatment(s) under study.
• Having a  control group allows for a comparison of treatments – e.g., treatment A produced favorable results 56% of the time versus treatment B in which only 25% of patients had favorable results.
• There are certain types of questions on which randomized controlled studies cannot be done for ethical reasons, for instance, if patients were asked to undertake harmful experiences (like smoking) or denied any treatment beyond a placebo when there are known effective treatments.

A type of randomized controlled clinical trial/study in which neither medical staff/physician nor the patient knows which of several possible treatments/therapies the patient is receiving.

• Example : Studies of treatments that consist essentially of taking pills are very easy to do double blind – the patient takes one of two pills of identical size, shape, and color, and neither the patient nor the physician needs to know which is which.
• A double blind study is the most rigorous clinical research design because, in addition to the randomization of subjects, which reduces the risk of bias, it can eliminate or minimize the placebo effect which is a further challenge to the validity of a study.

Meta-analysis is a systematic, objective way to combine data from many studies, usually from randomized controlled clinical trials, and arrive at a pooled estimate of treatment effectiveness and statistical significance.

• Meta-analysis can also combine data from case/control and cohort studies. The advantage to merging these data is that it increases sample size and allows for analyses that would not otherwise be possible.
• They should not be confused with reviews of the literature or systematic reviews. 
• Two problems with meta-analysis are publication bias (studies showing no effect or little effect are often not published and just “filed” away) and the quality of the design of the studies from which data is pulled. This can lead to misleading results when all the data on the subject from “published” literature are summarized.

A systematic review is a comprehensive survey of a topic that takes great care to find all relevant studies of the highest level of evidence, published and unpublished, assess each study, synthesize the findings from individual studies in an unbiased, explicit and reproducible way and present a balanced and impartial summary of the findings with due consideration of any flaws in the evidence. In this way it can be used for the evaluation of either existing or new technologies and practices.   

A systematic review is more rigorous than a traditional literature review and attempts to reduce the influence of bias. In order to do this, a systematic review follows a formal process:

• Clearly formulated research question
• Published & unpublished (conferences, company reports, “file drawer reports”, etc.) literature is carefully searched for relevant research
• Identified research is assessed according to an explicit methodology
• Results of the critical assessment of the individual studies are combined
• Final results are placed in context, addressing such issues are quality of the included studies, impact of bias and the applicability of the findings
• The difference between a systematic review and a meta-analysis is that a systematic review looks at the whole picture (qualitative view), while a meta-analysis looks for the specific statistical picture (quantitative view). 

R esearch Process in the Health Sciences  (35:37 min): Overview of the scientific research process in the health sciences. Follows the seven steps: defining the problem, reviewing the literature, formulating a hypothesis, choosing a research design, collecting data, analyzing the data and interpretation and report writing. Includes a set of additional readings and library resources.

Research Study Designs in the Health Sciences  (29:36 min): An overview of research study designs used by health sciences researchers. Covers case reports/case series, case control studies, cohort studies, correlational studies, cross-sectional studies, experimental studies (including randomized control trials), systematic reviews and meta-analysis.  Additional readings and library resources are also provided.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Reading Research Effectively
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Reading a Scholarly Article or Research Paper

Identifying a research problem to investigate usually requires a preliminary search for and critical review of the literature in order to gain an understanding about how scholars have examined a topic. Scholars rarely structure research studies in a way that can be followed like a story; they are complex and detail-intensive and often written in a descriptive and conclusive narrative form. However, in the social and behavioral sciences, journal articles and stand-alone research reports are generally organized in a consistent format that makes it easier to compare and contrast studies and to interpret their contents.

General Reading Strategies

W hen you first read an article or research paper, focus on asking specific questions about each section. This strategy can help with overall comprehension and with understanding how the content relates [or does not relate] to the problem you want to investigate. As you review more and more studies, the process of understanding and critically evaluating the research will become easier because the content of what you review will begin to coalescence around common themes and patterns of analysis. Below are recommendations on how to read each section of a research paper effectively. Note that the sections to read are out of order from how you will find them organized in a journal article or research paper.

1.  Abstract

The abstract summarizes the background, methods, results, discussion, and conclusions of a scholarly article or research paper. Use the abstract to filter out sources that may have appeared useful when you began searching for information but, in reality, are not relevant. Questions to consider when reading the abstract are:

• Is this study related to my question or area of research?
• What is this study about and why is it being done ?
• What is the working hypothesis or underlying thesis?
• What is the primary finding of the study?
• Are there words or terminology that I can use to either narrow or broaden the parameters of my search for more information?

2.  Introduction

If, after reading the abstract, you believe the paper may be useful, focus on examining the research problem and identifying the questions the author is trying to address. This information is usually located within the first few paragraphs of the introduction or in the concluding paragraph. Look for information about how and in what way this relates to what you are investigating. In addition to the research problem, the introduction should provide the main argument and theoretical framework of the study and, in the last paragraphs of the introduction, describe what the author(s) intend to accomplish. Questions to consider when reading the introduction include:

• What is this study trying to prove or disprove?
• What is the author(s) trying to test or demonstrate?
• What do we already know about this topic and what gaps does this study try to fill or contribute a new understanding to the research problem?
• Why should I care about what is being investigated?
• Will this study tell me anything new related to the research problem I am investigating?

3.  Literature Review

The literature review describes and critically evaluates what is already known about a topic. Read the literature review to obtain a big picture perspective about how the topic has been studied and to begin the process of seeing where your potential study fits within the domain of prior research. Questions to consider when reading the literature review include:

• W hat other research has been conducted about this topic and what are the main themes that have emerged?
• What does prior research reveal about what is already known about the topic and what remains to be discovered?
• What have been the most important past findings about the research problem?
• How has prior research led the author(s) to conduct this particular study?
• Is there any prior research that is unique or groundbreaking?
• Are there any studies I could use as a model for designing and organizing my own study?

4.  Discussion/Conclusion

The discussion and conclusion are usually the last two sections of text in a scholarly article or research report. They reveal how the author(s) interpreted the findings of their research and presented recommendations or courses of action based on those findings. Often in the conclusion, the author(s) highlight recommendations for further research that can be used to develop your own study. Questions to consider when reading the discussion and conclusion sections include:

• What is the overall meaning of the study and why is this important? [i.e., how have the author(s) addressed the " So What? " question].
• What do you find to be the most important ways that the findings have been interpreted?
• What are the weaknesses in their argument?
• Do you believe conclusions about the significance of the study and its findings are valid?
• What limitations of the study do the author(s) describe and how might this help formulate my own research?
• Does the conclusion contain any recommendations for future research?

5.  Methods/Methodology

The methods section describes the materials, techniques, and procedures for gathering information used to examine the research problem. If what you have read so far closely supports your understanding of the topic, then move on to examining how the author(s) gathered information during the research process. Questions to consider when reading the methods section include:

• Did the study use qualitative [based on interviews, observations, content analysis], quantitative [based on statistical analysis], or a mixed-methods approach to examining the research problem?
• What was the type of information or data used?
• Could this method of analysis be repeated and can I adopt the same approach?
• Is enough information available to repeat the study or should new data be found to expand or improve understanding of the research problem?

6.  Results

After reading the above sections, you should have a clear understanding of the general findings of the study. Therefore, read the results section to identify how key findings were discussed in relation to the research problem. If any non-textual elements [e.g., graphs, charts, tables, etc.] are confusing, focus on the explanations about them in the text. Questions to consider when reading the results section include:

• W hat did the author(s) find and how did they find it?
• Does the author(s) highlight any findings as most significant?
• Are the results presented in a factual and unbiased way?
• Does the analysis of results in the discussion section agree with how the results are presented?
• Is all the data present and did the author(s) adequately address gaps?
• What conclusions do you formulate from this data and does it match with the author's conclusions?

7.  References

The references list the sources used by the author(s) to document what prior research and information was used when conducting the study. After reviewing the article or research paper, use the references to identify additional sources of information on the topic and to examine critically how these sources supported the overall research agenda. Questions to consider when reading the references include:

• Do the sources cited by the author(s) reflect a diversity of disciplinary viewpoints, i.e., are the sources all from a particular field of study or do the sources reflect multiple areas of study?
• Are there any unique or interesting sources that could be incorporated into my study?
• What other authors are respected in this field, i.e., who has multiple works cited or is cited most often by others?
• What other research should I review to clarify any remaining issues or that I need more information about?

NOTE :  A final strategy in reviewing research is to copy and paste the title of the source [journal article, book, research report] into Google Scholar . If it appears, look for a "cited by" followed by a hyperlinked number [e.g., Cited by 45]. This number indicates how many times the study has been subsequently cited in other, more recently published works. This strategy, known as citation tracking, can be an effective means of expanding your review of pertinent literature based on a study you have found useful and how scholars have cited it. The same strategies described above can be applied to reading articles you find in the list of cited by references.

Reading Tip

Specific Reading Strategies

Effectively reading scholarly research is an acquired skill that involves attention to detail and an ability to comprehend complex ideas, data, and theoretical concepts in a way that applies logically to the research problem you are investigating. Here are some specific reading strategies to consider.

As You are Reading

• Focus on information that is most relevant to the research problem; skim over the other parts.
• As noted above, read content out of order! This isn't a novel; you want to start with the spoiler to quickly assess the relevance of the study.
• Think critically about what you read and seek to build your own arguments; not everything may be entirely valid, examined effectively, or thoroughly investigated.
• Look up the definitions of unfamiliar words, concepts, or terminology. A good scholarly source is Credo Reference .

Taking notes as you read will save time when you go back to examine your sources. Here are some suggestions:

• Mark or highlight important text as you read [e.g., you can use the highlight text  feature in a PDF document]
• Take notes in the margins [e.g., Adobe Reader offers pop-up sticky notes].
• Highlight important quotations; consider using different colors to differentiate between quotes and other types of important text.
• Summarize key points about the study at the end of the paper. To save time, these can be in the form of a concise bulleted list of statements [e.g., intro has provides historical background; lit review has important sources; good conclusions].

Write down thoughts that come to mind that may help clarify your understanding of the research problem. Here are some examples of questions to ask yourself:

• Do I understand all of the terminology and key concepts?
• Do I understand the parts of this study most relevant to my topic?
• What specific problem does the research address and why is it important?
• Are there any issues or perspectives the author(s) did not consider?
• Do I have any reason to question the validity or reliability of this research?
• How do the findings relate to my research interests and to other works which I have read?

Adapted from text originally created by Holly Burt, Behavioral Sciences Librarian, USC Libraries, April 2018.

Another Reading Tip

When is it Important to Read the Entire Article or Research Paper

Laubepin argues, "Very few articles in a field are so important that every word needs to be read carefully." However, this implies that some studies are worth reading carefully. As painful and time-consuming as it may seem, there are valid reasons for reading a study in its entirety from beginning to end. Here are some examples:

• Studies Published Very Recently .  The author(s) of a recent, well written study will provide a survey of the most important or impactful prior research in the literature review section. This can establish an understanding of how scholars in the past addressed the research problem. In addition, the most recently published sources will highlight what is currently known and what gaps in understanding currently exist about a topic, usually in the form of the need for further research in the conclusion .
• Surveys of the Research Problem .  Some papers provide a comprehensive analytical overview of the research problem. Reading this type of study can help you understand underlying issues and discover why scholars have chosen to investigate the topic. This is particularly important if the study was published very recently because the author(s) should cite all or most of the key prior research on the topic. Note that, if it is a long-standing problem, there may be studies that specifically review the literature to identify gaps that remain. These studies often include the word review in their title [e.g., Hügel, Stephan, and Anna R. Davies. "Public Participation, Engagement, and Climate Change Adaptation: A Review of the Research Literature." Wiley Interdisciplinary Reviews: Climate Change 11 (July-August 2020): https://doi.org/10.1002/ wcc.645].
• Highly Cited .  If you keep coming across the same citation to a study while you are reviewing the literature, this implies it was foundational in establishing an understanding of the research problem or the study had a significant impact within the literature [positive or negative]. Carefully reading a highly cited source can help you understand how the topic emerged and motivated scholars to further investigate the problem. It also could be a study you need to cite as foundational in your own paper to demonstrate to the reader that you understand the roots of the problem.
• Historical Overview .  Knowing the historical background of a research problem may not be the focus of your analysis. Nevertheless, carefully reading a study that provides a thorough description and analysis of the history behind an event, issue, or phenomenon can add important context to understanding the topic and what aspect of the problem you may want to examine further.
• Innovative Methodological Design .  Some studies are significant and worth reading in their entirety because the author(s) designed a unique or innovative approach to researching the problem. This may justify reading the entire study because it can motivate you to think creatively about pursuing an alternative or non-traditional approach to examining your topic of interest. These types of studies are generally easy to identify because they are often cited in others works because of their unique approach to studying the research problem.
• Cross-disciplinary Approach .  R eviewing studies produced outside of your discipline is an essential component of investigating research problems in the social and behavioral sciences. Consider reading a study that was conducted by author(s) based in a different discipline [e.g., an anthropologist studying political cultures; a study of hiring practices in companies published in a sociology journal]. This approach can generate a new understanding or a unique perspective about the topic . If you are not sure how to search for studies published in a discipline outside of your major or of the course you are taking, contact a librarian for assistance.

Laubepin, Frederique. How to Read (and Understand) a Social Science Journal Article . Inter-University Consortium for Political and Social Research (ISPSR), 2013; Shon, Phillip Chong Ho. How to Read Journal Articles in the Social Sciences: A Very Practical Guide for Students . 2nd edition. Thousand Oaks, CA: Sage, 2015; Lockhart, Tara, and Mary Soliday. "The Critical Place of Reading in Writing Transfer (and Beyond): A Report of Student Experiences." Pedagogy 16 (2016): 23-37; Maguire, Moira, Ann Everitt Reynolds, and Brid Delahunt. "Reading to Be: The Role of Academic Reading in Emergent Academic and Professional Student Identities." Journal of University Teaching and Learning Practice 17 (2020): 5-12.

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Writing Research Papers

• Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines.  Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1  Many will also contain Figures and Tables and some will have an Appendix or Appendices.  These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

• Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
• Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

• Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
• Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
• Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

• For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos

APA Journal Article Reporting Guidelines

• Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

• Formatting APA Style Papers in Microsoft Word
• How to Write an APA Style Research Paper from Hamilton University
• WikiHow Guide to Writing APA Research Papers
• Sample APA Formatted Paper with Comments
• Sample APA Formatted Paper
• Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

2 geller, e. (2018).  how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

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• Formatting Research Papers
• Using Databases and Finding References
• What Types of References Are Appropriate?
• Evaluating References and Taking Notes
• Citing References
• Writing a Literature Review
• Writing Process and Revising
• Improving Scientific Writing
• Academic Integrity and Avoiding Plagiarism
• Writing Research Papers Videos

Research Process: 8 Steps in Research Process

The research process starts with identifying a research problem and conducting a literature review to understand the context. The researcher sets research questions, objectives, and hypotheses based on the research problem.

A research study design is formed to select a sample size and collect data after processing and analyzing the collected data and the research findings presented in a research report.

What is the Research Process?

There are a variety of approaches to research in any field of investigation, irrespective of whether it is applied research or basic research. Each research study will be unique in some ways because of the particular time, setting, environment, and place it is being undertaken.

Nevertheless, all research endeavors share a common goal of furthering our understanding of the problem, and thus, all traverse through certain primary stages, forming a process called the research process.

Understanding the research process is necessary to effectively carry out research and sequence the stages inherent in the process.

How Research Process Work?

Eight steps research process is, in essence, part and parcel of a research proposal. It is an outline of the commitment that you intend to follow in executing a research study.

A close examination of the above stages reveals that each of these stages, by and large, is dependent upon the others.

One cannot analyze data (step 7) unless he has collected data (step 6). One cannot write a report (step 8) unless he has collected and analyzed data (step 7).

Research then is a system of interdependent related stages. Violation of this sequence can cause irreparable harm to the study.

It is also true that several alternatives are available to the researcher during each stage stated above. A research process can be compared with a route map.

The map analogy is useful for the researcher because several alternatives exist at each stage of the research process.

Choosing the best alternative in terms of time constraints, money, and human resources in our research decision is our primary goal.

Before explaining the stages of the research process, we explain the term ‘iterative’ appearing within the oval-shaped diagram at the center of the schematic diagram.

The key to a successful research project ultimately lies in iteration: the process of returning again and again to the identification of the research problems, methodology, data collection, etc., which leads to new ideas, revisions, and improvements.

By discussing the research project with advisers and peers, one will often find that new research questions need to be added, variables to be omitted, added or redefined, and other changes to be made. As a proposed study is examined and reexamined from different perspectives, it may begin to transform and take a different shape.

This is expected and is an essential component of a good research study.

Besides, examining study methods and data collected from different viewpoints is important to ensure a comprehensive approach to the research question.

In conclusion, there is seldom any single strategy or formula for developing a successful research study, but it is essential to realize that the research process is cyclical and iterative.

What is the primary purpose of the research process?

The research process aims to identify a research problem, understand its context through a literature review, set research questions and objectives, design a research study, select a sample, collect data, analyze the data, and present the findings in a research report.

Why is the research design important in the research process?

The research design is the blueprint for fulfilling objectives and answering research questions. It specifies the methods and procedures for collecting, processing, and analyzing data, ensuring the study is structured and systematic.

8 Steps of Research Process

Identifying the research problem.

The first and foremost task in the entire process of scientific research is to identify a research problem .

A well-identified problem will lead the researcher to accomplish all-important phases of the research process, from setting objectives to selecting the research methodology .

But the core question is: whether all problems require research.

We have countless problems around us, but all we encounter do not qualify as research problems; thus, these do not need to be researched.

Keeping this point in mind, we must draw a line between research and non-research problems.

Intuitively, researchable problems are those that have a possibility of thorough verification investigation, which can be effected through the analysis and collection of data. In contrast, the non-research problems do not need to go through these processes.

Researchers need to identify both;

Non-Research Problems

Statement of the problem, justifying the problem, analyzing the problem.

A non-research problem does not require any research to arrive at a solution. Intuitively, a non-researchable problem consists of vague details and cannot be resolved through research.

It is a managerial or built-in problem that may be solved at the administrative or management level. The answer to any question raised in a non-research setting is almost always obvious.

The cholera outbreak, for example, following a severe flood, is a common phenomenon in many communities. The reason for this is known. It is thus not a research problem.

Similarly, the reasons for the sudden rise in prices of many essential commodities following the announcement of the budget by the Finance Minister need no investigation. Hence it is not a problem that needs research.

How is a research problem different from a non-research problem?

A research problem is a perceived difficulty that requires thorough verification and investigation through data analysis and collection. In contrast, a non-research problem does not require research for a solution, as the answer is often obvious or already known.

Non-Research Problems Examples

A recent survey in town- A found that 1000 women were continuous users of contraceptive pills.

But last month’s service statistics indicate that none of these women were using contraceptive pills (Fisher et al. 1991:4).

The discrepancy is that ‘all 1000 women should have been using a pill, but none is doing so. The question is: why the discrepancy exists?

Well, the fact is, a monsoon flood has prevented all new supplies of pills from reaching town- A, and all old supplies have been exhausted. Thus, although the problem situation exists, the reason for the problem is already known.

Therefore, assuming all the facts are correct, there is no reason to research the factors associated with pill discontinuation among women. This is, thus, a non-research problem.

A pilot survey by University students revealed that in Rural Town-A, the goiter prevalence among school children is as high as 80%, while in the neighboring Rural Town-A, it is only 30%. Why is a discrepancy?

Upon inquiry, it was seen that some three years back, UNICEF launched a lipiodol injection program in the neighboring Rural Town-A.

This attempt acted as a preventive measure against the goiter. The reason for the discrepancy is known; hence, we do not consider the problem a research problem.

A hospital treated a large number of cholera cases with penicillin, but the treatment with penicillin was not found to be effective. Do we need research to know the reason?

Here again, there is one single reason that Vibrio cholera is not sensitive to penicillin; therefore, this is not the drug of choice for this disease.

In this case, too, as the reasons are known, it is unwise to undertake any study to find out why penicillin does not improve the condition of cholera patients. This is also a non-research problem.

In the tea marketing system, buying and selling tea starts with bidders. Blenders purchase open tea from the bidders. Over the years, marketing cost has been the highest for bidders and the lowest for blenders. What makes this difference?

The bidders pay exorbitantly higher transport costs, which constitute about 30% of their total cost.

Blenders have significantly fewer marketing functions involving transportation, so their marketing cost remains minimal.

Hence no research is needed to identify the factors that make this difference.

Here are some of the problems we frequently encounter, which may well be considered non-research problems:

• Rises in the price of warm clothes during winter;
• Preferring admission to public universities over private universities;
• Crisis of accommodations in sea resorts during summer
• Traffic jams in the city street after office hours;
• High sales in department stores after an offer of a discount.

Research Problem

In contrast to a non-research problem, a research problem is of primary concern to a researcher.

A research problem is a perceived difficulty, a feeling of discomfort, or a discrepancy between a common belief and reality.

As noted by Fisher et al. (1993), a problem will qualify as a potential research problem when the following three conditions exist:

• There should be a perceived discrepancy between “what it is” and “what it should have been.” This implies that there should be a difference between “what exists” and the “ideal or planned situation”;
• A question about “why” the discrepancy exists. This implies that the reason(s) for this discrepancy is unclear to the researcher (so that it makes sense to develop a research question); and
• There should be at least two possible answers or solutions to the questions or problems.

The third point is important. If there is only one possible and plausible answer to the question about the discrepancy, then a research situation does not exist.

It is a non-research problem that can be tackled at the managerial or administrative level.

Research Problem Examples

Research problem – example #1.

While visiting a rural area, the UNICEF team observed that some villages have female school attendance rates as high as 75%, while some have as low as 10%, although all villages should have a nearly equal attendance rate. What factors are associated with this discrepancy?

We may enumerate several reasons for this:

• Villages differ in their socio-economic background.
• In some villages, the Muslim population constitutes a large proportion of the total population. Religion might play a vital role.
• Schools are far away from some villages. The distance thus may make this difference.

Because there is more than one answer to the problem, it is considered a research problem, and a study can be undertaken to find a solution.

Research Problem – Example #2

The Government has been making all-out efforts to ensure a regular flow of credit in rural areas at a concession rate through liberal lending policy and establishing many bank branches in rural areas.

Knowledgeable sources indicate that expected development in rural areas has not yet been achieved, mainly because of improper credit utilization.

More than one reason is suspected for such misuse or misdirection.

These include, among others:

• Diversion of credit money to some unproductive sectors
• Transfer of credit money to other people like money lenders, who exploit the rural people with this money
• Lack of knowledge of proper utilization of the credit.

Here too, reasons for misuse of loans are more than one. We thus consider this problem as a researchable problem.

Research Problem – Example #3

Let’s look at a new headline: Stock Exchange observes the steepest ever fall in stock prices: several injured as retail investors clash with police, vehicles ransacked .

Investors’ demonstration, protest and clash with police pause a problem. Still, it is certainly not a research problem since there is only one known reason for the problem: Stock Exchange experiences the steepest fall in stock prices. But what causes this unprecedented fall in the share market?

Experts felt that no single reason could be attributed to the problem. It is a mix of several factors and is a research problem. The following were assumed to be some of the possible reasons:

• The merchant banking system;
• Liquidity shortage because of the hike in the rate of cash reserve requirement (CRR);
• IMF’s warnings and prescriptions on the commercial banks’ exposure to the stock market;
• Increase in supply of new shares;
• Manipulation of share prices;
• Lack of knowledge of the investors on the company’s fundamentals.

The choice of a research problem is not as easy as it appears. The researchers generally guide it;

• own intellectual orientation,
• level of training,
• experience,
• knowledge on the subject matter, and
• intellectual curiosity.

Theoretical and practical considerations also play a vital role in choosing a research problem. Societal needs also guide in choosing a research problem.

Once we have chosen a research problem, a few more related steps must be followed before a decision is taken to undertake a research study.

These include, among others, the following:

• Statement of the problem.
• Justifying the problem.
• Analyzing the problem.

A detailed exposition of these issues is undertaken in chapter ten while discussing the proposal development.

A clear and well-defined problem statement is considered the foundation for developing the research proposal.

It enables the researcher to systematically point out why the proposed research on the problem should be undertaken and what he hopes to achieve with the study’s findings.

A well-defined statement of the problem will lead the researcher to formulate the research objectives, understand the background of the study, and choose a proper research methodology.

Once the problem situation has been identified and clearly stated, it is important to justify the importance of the problem.

In justifying the problems, we ask such questions as why the problem of the study is important, how large and widespread the problem is, and whether others can be convinced about the importance of the problem and the like.

Answers to the above questions should be reviewed and presented in one or two paragraphs that justify the importance of the problem.

As a first step in analyzing the problem, critical attention should be given to accommodate the viewpoints of the managers, users, and researchers to the problem through threadbare discussions.

The next step is identifying the factors that may have contributed to the perceived problems.

Issues of Research Problem Identification

There are several ways to identify, define, and analyze a problem, obtain insights, and get a clearer idea about these issues. Exploratory research is one of the ways of accomplishing this.

The purpose of the exploratory research process is to progressively narrow the scope of the topic and transform the undefined problems into defined ones, incorporating specific research objectives.

The exploratory study entails a few basic strategies for gaining insights into the problem. It is accomplished through such efforts as:

Pilot Survey

A pilot survey collects proxy data from the ultimate subjects of the study to serve as a guide for the large study. A pilot study generates primary data, usually for qualitative analysis.

This characteristic distinguishes a pilot survey from secondary data analysis, which gathers background information.

Case Studies

Case studies are quite helpful in diagnosing a problem and paving the way to defining the problem. It investigates one or a few situations identical to the researcher’s problem.

Focus Group Interviews

Focus group interviews, an unstructured free-flowing interview with a small group of people, may also be conducted to understand and define a research problem .

Experience Survey

Experience survey is another strategy to deal with the problem of identifying and defining the research problem.

It is an exploratory research endeavor in which individuals knowledgeable and experienced in a particular research problem are intimately consulted to understand the problem.

These persons are sometimes known as key informants, and an interview with them is popularly known as the Key Informant Interview (KII).

Reviewing of Literature

A review of relevant literature is an integral part of the research process. It enables the researcher to formulate his problem in terms of the specific aspects of the general area of his interest that has not been researched so far.

Such a review provides exposure to a larger body of knowledge and equips him with enhanced knowledge to efficiently follow the research process.

Through a proper review of the literature, the researcher may develop the coherence between the results of his study and those of the others.

A review of previous documents on similar or related phenomena is essential even for beginning researchers.

Ignoring the existing literature may lead to wasted effort on the part of the researchers.

Why spend time merely repeating what other investigators have already done?

Suppose the researcher is aware of earlier studies of his topic or related topics . In that case, he will be in a much better position to assess his work’s significance and convince others that it is important.

A confident and expert researcher is more crucial in questioning the others’ methodology, the choice of the data, and the quality of the inferences drawn from the study results.

In sum, we enumerate the following arguments in favor of reviewing the literature:

• It avoids duplication of the work that has been done in the recent past.
• It helps the researcher discover what others have learned and reported on the problem.
• It enables the researcher to become familiar with the methodology followed by others.
• It allows the researcher to understand what concepts and theories are relevant to his area of investigation.
• It helps the researcher to understand if there are any significant controversies, contradictions, and inconsistencies in the findings.
• It allows the researcher to understand if there are any unanswered research questions.
• It might help the researcher to develop an analytical framework.
• It will help the researcher consider including variables in his research that he might not have thought about.

Why is reviewing literature crucial in the research process?

Reviewing literature helps avoid duplicating previous work, discovers what others have learned about the problem, familiarizes the researcher with relevant concepts and theories, and ensures a comprehensive approach to the research question.

What is the significance of reviewing literature in the research process?

Reviewing relevant literature helps formulate the problem, understand the background of the study, choose a proper research methodology, and develop coherence between the study’s results and previous findings.

Setting Research Questions, Objectives, and Hypotheses

After discovering and defining the research problem, researchers should make a formal statement of the problem leading to research objectives .

An objective will precisely say what should be researched, delineate the type of information that should be collected, and provide a framework for the scope of the study. A well-formulated, testable research hypothesis is the best expression of a research objective.

A hypothesis is an unproven statement or proposition that can be refuted or supported by empirical data. Hypothetical statements assert a possible answer to a research question.

Step #4: Choosing the Study Design

The research design is the blueprint or framework for fulfilling objectives and answering research questions .

It is a master plan specifying the methods and procedures for collecting, processing, and analyzing the collected data. There are four basic research designs that a researcher can use to conduct their study;

• experiment,
• secondary data study, and
• observational study.

The type of research design to be chosen from among the above four methods depends primarily on four factors:

• The type of problem
• The objectives of the study,
• The existing state of knowledge about the problem that is being studied, and
• The resources are available for the study.

Deciding on the Sample Design

Sampling is an important and separate step in the research process. The basic idea of sampling is that it involves any procedure that uses a relatively small number of items or portions (called a sample) of a universe (called population) to conclude the whole population.

It contrasts with the process of complete enumeration, in which every member of the population is included.

Such a complete enumeration is referred to as a census.

A population is the total collection of elements we wish to make some inference or generalization.

A sample is a part of the population, carefully selected to represent that population. If certain statistical procedures are followed in selecting the sample, it should have the same characteristics as the population. These procedures are embedded in the sample design.

Sample design refers to the methods followed in selecting a sample from the population and the estimating technique vis-a-vis the formula for computing the sample statistics.

The fundamental question is, then, how to select a sample.

To answer this question, we must have acquaintance with the sampling methods.

These methods are basically of two types;

• probability sampling , and
• non-probability sampling .

Probability sampling ensures every unit has a known nonzero probability of selection within the target population.

If there is no feasible alternative, a non-probability sampling method may be employed.

The basis of such selection is entirely dependent on the researcher’s discretion. This approach is called judgment sampling, convenience sampling, accidental sampling, and purposive sampling.

The most widely used probability sampling methods are simple random sampling , stratified random sampling , cluster sampling , and systematic sampling . They have been classified by their representation basis and unit selection techniques.

Two other variations of the sampling methods that are in great use are multistage sampling and probability proportional to size (PPS) sampling .

Multistage sampling is most commonly used in drawing samples from very large and diverse populations.

The PPS sampling is a variation of multistage sampling in which the probability of selecting a cluster is proportional to its size, and an equal number of elements are sampled within each cluster.

Collecting Data From The Research Sample

Data gathering may range from simple observation to a large-scale survey in any defined population. There are many ways to collect data. The approach selected depends on the objectives of the study, the research design, and the availability of time, money, and personnel.

With the variation in the type of data (qualitative or quantitative) to be collected, the method of data collection also varies .

The most common means for collecting quantitative data is the structured interview .

Studies that obtain data by interviewing respondents are called surveys. Data can also be collected by using self-administered questionnaires . Telephone interviewing is another way in which data may be collected .

Other means of data collection include secondary sources, such as the census, vital registration records, official documents, previous surveys, etc.

Qualitative data are collected mainly through in-depth interviews, focus group discussions , Key Informant Interview ( KII), and observational studies.

Process and Analyze the Collected Research Data

Data processing generally begins with the editing and coding of data . Data are edited to ensure consistency across respondents and to locate omissions if any.

In survey data, editing reduces errors in the recording, improves legibility, and clarifies unclear and inappropriate responses. In addition to editing, the data also need coding.

Because it is impractical to place raw data into a report, alphanumeric codes are used to reduce the responses to a more manageable form for storage and future processing.

This coding process facilitates the processing of the data. The personal computer offers an excellent opportunity for data editing and coding processes.

Data analysis usually involves reducing accumulated data to a manageable size, developing summaries, searching for patterns, and applying statistical techniques for understanding and interpreting the findings in light of the research questions.

Further, based on his analysis, the researcher determines if his findings are consistent with the formulated hypotheses and theories.

The techniques used in analyzing data may range from simple graphical techniques to very complex multivariate analyses depending on the study’s objectives, the research design employed, and the nature of the data collected.

As in the case of data collection methods, an analytical technique appropriate in one situation may not be suitable for another.

Writing Research Report – Developing Research Proposal, Writing Report, Disseminating and Utilizing Results

The entire task of a research study is accumulated in a document called a proposal or research proposal.

A research proposal is a work plan, prospectus, outline, offer, and a statement of intent or commitment from an individual researcher or an organization to produce a product or render a service to a potential client or sponsor .

The proposal will be prepared to keep the sequence presented in the research process. The proposal tells us what, how, where, and to whom it will be done.

It must also show the benefit of doing it. It always includes an explanation of the purpose of the study (the research objectives) or a definition of the problem.

It systematically outlines the particular research methodology and details the procedures utilized at each stage of the research process.

The end goal of a scientific study is to interpret the results and draw conclusions.

To this end, it is necessary to prepare a report and transmit the findings and recommendations to administrators, policymakers, and program managers to make a decision.

There are various research reports: term papers, dissertations, journal articles , papers for presentation at professional conferences and seminars, books, thesis, and so on. The results of a research investigation prepared in any form are of little utility if they are not communicated to others.

The primary purpose of a dissemination strategy is to identify the most effective media channels to reach different audience groups with study findings most relevant to their needs.

The dissemination may be made through a conference, a seminar, a report, or an oral or poster presentation.

The style and organization of the report will differ according to the target audience, the occasion, and the purpose of the research. Reports should be developed from the client’s perspective.

A report is an excellent means that helps to establish the researcher’s credibility. At a bare minimum, a research report should contain sections on:

• An executive summary;
• Background of the problem;
• Literature review;
• Methodology;
• Discussion;
• Conclusions and
• Recommendations.

The study results can also be disseminated through peer-reviewed journals published by academic institutions and reputed publishers both at home and abroad. The report should be properly evaluated .

These journals have their format and editorial policies. The contributors can submit their manuscripts adhering to the policies and format for possible publication of their papers.

There are now ample opportunities for researchers to publish their work online.

The researchers have conducted many interesting studies without affecting actual settings. Ideally, the concluding step of a scientific study is to plan for its utilization in the real world.

Although researchers are often not in a position to implement a plan for utilizing research findings, they can contribute by including in their research reports a few recommendations regarding how the study results could be utilized for policy formulation and program intervention.

Why is the dissemination of research findings important?

Dissemination of research findings is crucial because the results of a research investigation have little utility if not communicated to others. Dissemination ensures that the findings reach relevant stakeholders, policymakers, and program managers to inform decisions.

How should a research report be structured?

A research report should contain sections on an executive summary, background of the problem, literature review, methodology, findings, discussion, conclusions, and recommendations.

Why is it essential to consider the target audience when preparing a research report?

The style and organization of a research report should differ based on the target audience, occasion, and research purpose. Tailoring the report to the audience ensures that the findings are communicated effectively and are relevant to their needs.

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Research Paper – Structure, Examples and Writing Guide

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Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

The Reporter

New Evidence on the Impacts of Birth Order

What determines a child's success? We know that family matters — children from higher socioeconomic status families do better in school, get more education, and earn more.

However, even beyond that, there is substantial variation in success across children within families. This has led researchers to study factors that relate to within-family differences in children's outcomes. One that has attracted much interest is the role played by birth order, which varies systematically within families and is exogenously determined.

While economists have been interested in understanding human capital development for many decades, compelling economic research on birth order is more recent and has largely resulted from improved availability of data. Early work on birth order was hindered by the stringent data requirements necessary to convincingly identify the effects of birth order. Most importantly, one needs information on both family size and birth order. As there is only a third-born child in a family with at least three children, comparing third-borns to firstborns across families of different sizes will conflate the birth order effect with a family size effect, so one needs to be able to control for family size. Additionally, it is beneficial to have information on multiple children from the same family so that birth order effects can be estimated from within-family differences in child outcomes; otherwise, birth order effects will be conflated with other effects that vary systematically with birth order, such as cohort effects. Large Scandinavian register datasets that became available to researchers beginning in the late 1990s have enabled birth order research, as they contain population data on both family structure and a variety of child outcomes. Here, I describe my research with a number of coauthors, using these data to explore the effects of birth order on outcomes including human capital accumulation, earnings, development of cognitive and non-cognitive skills, and health.

Birth Order and Economic Success

Almost a half-century ago, economists including Gary Becker, H. Gregg Lewis, and Nigel Tomes created models of quality-quantity trade-offs in child-rearing and used these models to explore the role of family in children's success. They sought to explain an observed negative correlation between family income and family size: if child quality is a normal good, as income rises the family demands higher-quality children at the cost of lower family size. 1

However, this was a difficult model to test, as characteristics other than family income and child quality vary with family size. The introduction of natural experiments, combined with newly available large administrative datasets from Scandinavia, made testing such a model possible.

In my earliest work on the topic, Paul Devereux, Kjell Salvanes, and I took advantage of the Norwegian administrative dataset and set out to better understand this theoretical quantity-quality tradeoff. 2 It became clear that child "quality" was not a constant within a family — children within families were quite different, despite the model assumptions to the contrary. Indeed, we found that birth order could explain a large fraction of the family size differential in children's educational outcomes. Average educational attainment was lower in larger families largely because later-born children had lower average education, rather than because firstborns had lower education in large families than in small families. We found that firstborns had higher educational attainment than second-borns who in turn did better than third-borns, and so on. These results were robust to a variety of specifications; most importantly, we could compare outcomes of children within the same families.

To give a sense of the magnitude of these effects: The difference in educational attainment between the first child and the fifth child in a five-child family is roughly equal to the difference between the educational attainment of blacks and whites calculated from the 2000 Census. We augmented the education results by examining earnings, whether full-time employed, and whether one had a child as a teenager as additional outcome variables, and found strong evidence for birth order effects, particularly for women. Later-born women have lower earnings (whether employed full-time or not), are less likely to work full-time, and are more likely to have their first child as teenagers. In contrast, while later-born men have lower full-time earnings, they are not less likely to work full-time [Figure 1].

Birth Order and Cognitive Skills

One possible explanation for these differences is that cognitive ability varies systematically by birth order. In subsequent work, Devereux, Salvanes, and I examined the effect of birth order on IQ scores. 3

The psychology literature has long debated the role of birth order in determining children's IQs; this debate was seemingly resolved when, in 2000, J. L. Rodgers et al. published a paper in American Psychologist entitled "Resolving the Debate Over Birth Order, Family Size, and Intelligence" that referred to the apparent relationship between birth order and IQ as a "methodological illusion." 4 However, this work was limited due to the absence of large representative datasets necessary to identify these effects. We again used population register data from Norway to estimate this relationship.

To measure IQ, we used the outcomes of standardized cognitive tests administered to Norwegian men between the age of 18 and 20 when they enlist in the military. Consistent with our earlier findings on educational attainment but in contrast to the previous work in the literature, we found strong birth order effects on IQ that are present when we look within families. Later-born children have lower IQs, on average, and these differences are quite large. For example, the difference between firstborn and second-born average IQ is on the order of one-fifth of a standard deviation, or about three IQ points. This translates into approximately a 2 percent difference in annual earnings in adulthood.

The Effect of Birth Order on Non-Cognitive Skills

Personality is another factor that is posited to vary by birth order, a proposition that has been particularly difficult to assess in a compelling way due to the paucity of large datasets containing information on individual personality. In recent work on the topic, Erik Gronqvist, Bjorn Ockert, and I use Swedish administrative datasets to examine this issue. 5

In the economics literature, personality traits are often referred to as non-cognitive abilities and denote traits that can be distinguished from intelligence. 6 To measure "personality" (or non-cognitive skills), we use the outcome of a standardized psychological evaluation, conducted by a certified psychologist, that is performed on all Swedish men between the ages of 18 and 20 when they enlist in the military, and which is strongly related to success in the labor market. An individual is given a higher score if he is considered to be emotionally stable, persistent, socially outgoing, willing to assume responsibility, and able to take initiative. Similar to the results for cognitive skills, we find evidence of consistently lower scores in this measure for later-born children. Third-born children have non-cognitive abilities that are 0.2 standard deviations below firstborn children. Interestingly, boys with older brothers suffer almost twice as much in terms of these personality characteristics as boys with older sisters.

Importantly, we also demonstrate that these personality differences translate into differences in occupation choice by birth order. Firstborn children are significantly more likely to be employed and to work as top managers, while later-born children are more likely to be self-employed. More generally, firstborn children are more likely to be in occupations requiring sociability, leadership ability, conscientiousness, agreeableness, emotional stability, extraversion, and openness.

The Effect of Birth Order on Health

Finally, how do these differences translate into later health? In more recent work, Devereux, Salvanes, and I analyze the effect of birth order on health. 7 There is a sizable body of literature about the relationship between birth order and adult health; individual studies have typically examined only one or a small number of health outcomes and, in many cases, have used relatively small samples. Again, we use large nationally representative data from Norway to identify the relationship between birth order and health when individuals are in their 40s, where health is measured along a number of dimensions, including medical indicators, health behaviors, and overall life satisfaction.

The effects of birth order on health are less straightforward than other outcomes we have examined, as firstborns do better on some dimensions and worse on others. We find that the probability of having high blood pressure declines with birth order, and the largest gap is between first- and second-borns. Second-borns are about 3 percent less likely to have high blood pressure than firstborns; fifth-borns are about 7 percent less likely to have high blood pressure than firstborns. Given that 24 percent of this population has high blood pressure, this is quite a large difference. Firstborns are also more likely to be overweight and obese. Compared with second-borns, firstborns are 4 percent more likely to be overweight and 2 percent more likely to be obese. The equivalent differences between fifth-borns and firstborns are 10 percent and 5 percent. For context, 47 percent of the population is overweight and 10 percent is obese. Once again, the magnitudes are quite large.

However, later-borns are less likely to consider themselves to be in good health, and measures of mental health generally decline with birth order. Later-born children also exhibit worse health behaviors. The number of cigarettes smoked daily increases monotonically with birth order, suggesting that the higher prevalence of smoking by later-borns found among U.S. adolescents by Laura M. Argys et al. 8 may persist throughout adulthood and, hence, have important effects on health outcomes.

Possible Mechanisms

Why are adult outcomes likely to be affected by birth order? A host of potential explanations has been proposed across several academic disciplines.

A number of biological factors may explain birth order effects. These relate to changes in the womb environment or maternal immune system that occur over successive births. Beyond biology, parents could have other influences. Childhood inputs, especially in the first years of life, are considered crucial for skill formation. 9 Firstborn children have the full attention of parents, but as families grow the family environment is diluted and parental resources become scarcer. 10 In contrast, parents are more experienced and tend to have higher incomes when raising later-born children. In addition, for a given amount of resources, parents may treat firstborn children differently than second- or later-born children. Parents may use more strict parenting practices toward the firstborn, so as to gain a reputation for "toughness" necessary to induce good behavior among later-borns. 11

There are also theories that suggest that interactions among siblings can shape birth order effects. For example, based on evolutionary psychology, Frank J. Sulloway suggests that firstborns have an advantage in following the status quo, while later-borns — by having incentives to engage in investments aimed at differentiating themselves — become more sociable and unconventional in order to attract parental resources. 12

In each of these papers, we attempted to identify potential mechanisms for the patterns we observed. However, it is here we see the limitations of these large administrative datasets, as for the most part, we lack necessary detailed information on biological factors and on household dynamics when the children are young. However, we do have some evidence on the role of biological factors. Later-born children tend to have better birth outcomes as measured by factors such as birth weight. In our Swedish data, we took advantage of the fact that some children's biological birth order is different from their environmental birth order, due to the death of an older sibling or because their parent gave up a child for adoption. When we examine this subsample, we find that the birth order effect on occupational choice is entirely driven by the environmental birth order, again suggesting that biological factors may not be central.

Also in our Swedish study, we found that firstborn teenagers are more likely to read books, spend more time on homework, and spend less time watching TV or playing video games. Parents spend less time discussing school work with later-born children, suggesting there may be differences in parental time investments. Using Norwegian data, we found that smoking early in pregnancy is more prevalent for first pregnancies than for later ones. However, women are more likely to quit smoking during their first pregnancy than during later ones, and firstborns are more likely to be breastfed. These findings suggest that early investments may systematically benefit firstborns and help explain their generally better outcomes.

In the past two decades, with the increased accessibility of administrative datasets on large swaths of the population, economists and other researchers have been better able to identify the role of birth order in the outcomes of children. There is strong evidence of substantial differences by birth order across a range of outcomes. While I have described several of my own papers on the topic, a number of other researchers have also taken advantage of newly available datasets in Florida and Denmark to examine the role of birth order on other important outcomes, specifically juvenile delinquency and later criminal behavior. 13 Consistent with the work discussed here, later-born children experience higher rates of delinquency and criminal behavior; this is at least partly attributable to time investments of parents.

Researchers

More from nber.

G. Becker, "An Economic Analysis of Fertility," in Demographic and Economic Change in Developed Countries , New York, Columbia University Press, 1960, pp. 209-40; G. Becker and H. Lewis, "Interaction Between Quantity and Quality of Children," in Economics of the Family: Marriage, Children, and Human Capital , 1974, pp. 81-90; G. Becker and N. Tomes, "Child Endowments, and the Quantity and Quality of Children," NBER Working Paper 123 , February 1976.  

S. Black, P. Devereux, and K. Salvanes, "The More the Merrier? The Effect of Family Composition on Children's Education" NBER Working Paper 10720 , September 2004, and Quarterly Journal of Economics , 120(2), 2005, pp. 669-700.  

S. Black, P. Devereux, and K. Salvanes, "Older and Wiser? Birth Order and the IQ of Young Men," NBER Working Paper 13237 , July 2007, and CESifo Economic Studies , Oxford University Press, vol. 57(1), pages 103-20, March 2011.  

J. Rodgers, H. Cleveland, E. van den Oord, and D. Rowe, "Resolving the Debate Over Birth Order, Family Size, and Intelligence," American Psychologist , 55(6), 2000, pp. 599-612.

S. Black, E. Gronqvist, and B. Ockert, "Born to Lead? The Effect of Birth Order on Non-Cognitive Abilities," NBER Working Paper 23393 , May 2017.  

L. Borghans, A. Duckworth, J. Heckman, and B. ter Weel, "The Economics and Psychology of Personality Traits," Journal of Human Resources , 43, 2008, pp. 972-1059.  

S. Black, P. Devereux, K. Salvanes, "Healthy (?), Wealthy, and Wise: Birth Order and Adult Health, NBER Working Paper 21337 , July 2015.  

L. Argys, D. Rees, S. Averett, and B. Witoonchart, "Birth Order and Risky Adolescent Behavior," Economic Inquiry , 44(2), 2006, pp. 215-33.  

F. Cunha and J. Heckman, "The Technology of Skill Formation," NBER Working Paper 12840 , January 2007.

R. Zajonc and G. Markus, "Birth Order and Intellectual Development," Psychological Review , 82(1), 1975, pp. 74-88; R. Zajonc, "Family Configuration and Intelligence," Science , 192(4236), 1976, pp. 227-36; J. Price, "Parent-Child Quality Time: Does Birth Order Matter?" in Journal of Human Resources , 43(1), 2008, pp. 240-65; J.Lehmann, A. Nuevo-Chiquero, and M. Vidal-Fernandez, "The Early Origins of Birth Order Differences in Children's Outcomes and Parental Behavior," forthcoming in Journal of Human Resources .  

V. Hotz and J. Pantano, "Strategic Parenting, Birth Order, and School Performance," NBER Working Paper 19542 , October 2013, and Journal of Population Economics , 28(4), 2015, pp. 911-936. ↩  

F. Sulloway, Born to Rebel: Birth Order, Family Dynamics, and Creative Lives , New York, Pantheon Books, 1996.

S. Breining, J. Doyle, D. Figlio, K. Karbownik, J. Roth, "Birth Order and Delinquency: Evidence from Denmark and Florida," NBER Working Paper 23038 , January 2017.

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Key findings about online dating in the U.S.

Online dating in the United States has evolved over the past several decades into a booming industry , transforming the way some people meet matches . A new report from Pew Research Center explores the upsides and downsides of online dating by highlighting Americans’ experiences and views about it. Here are 12 key takeaways.

Pew Research Center conducted this study to understand Americans’ experiences with dating sites and apps and their views of online dating generally. This analysis is based on a survey conducted among 6,034 U.S. adults from July 5-17, 2022. This included 4,996 respondents from the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. It also included an oversample of 1,038 respondents from Ipsos’ KnowledgePanel who indicated that they are lesbian, gay or bisexual (LGB), with oversampled groups weighted back to reflect proportions in the population. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis, along with responses, and its methodology .

Terminology

• Online dating users refers to the 30% of Americans who answered yes to the following question: “Have you ever used an online dating site or dating app?”
• Current or recent online dating users refers to the 9% of adults who had used a dating site or app in the past year as of the July survey.
• Partnered refers to the 69% of U.S. adults who describe themselves as married, living with a partner, or in a committed romantic relationship.
• LGB refers to those who are lesbian, gay or bisexual. These groups are combined because of small sample sizes. Additionally, since this research is focused on sexual orientation, not gender identity, and due to the fact that the transgender population in the U.S. is very small, transgender respondents are not identified separately. Read the report for more details.

A note about the Asian adult sample

This survey includes a total sample size of 234 Asian adults. The sample primarily includes English-speaking Asian adults and therefore may not be representative of the overall Asian adult population. Despite this limitation, it is important to report the views of Asian adults on the topics in this study. As always, Asian adults’ responses are incorporated into the general population figures throughout this report. Asian adults are shown as a separate group when the question was asked of the full sample. Because of the relatively small sample size and a reduction in precision due to weighting, results are not shown separately for Asian adults for questions that were only asked of online dating users or other filtered questions. We are also not able to analyze Asian adults by demographic categories, such as gender, age or education.

Three-in-ten U.S. adults say they have ever used a dating site or app, identical to the share who said this in 2019 . That includes 9% who report doing so in the past year, according to the Center’s survey of 6,034 adults conducted July 5-17, 2022.

Online dating is more common among younger adults than among older people. About half of those under 30 (53%) report having ever used a dating site or app, compared with 37% of those ages 30 to 49, 20% of those 50 to 64 and 13% of those 65 and older.

When looking at sexual orientation, lesbian, gay or bisexual (LGB) adults are more likely than their straight counterparts to say they have ever used a dating site or app (51% vs. 28%).

Men are somewhat more likely than women to have tried online dating (34% vs. 27%), as are those with at least some college education when compared with those with a high school education or less.

Adults who have never been married are much more likely than married adults to report having used online dating sites or apps (52% vs. 16%). Adults who are currently living with a partner (46%) or who are divorced, separated or widowed (36%) are also more likely to have tried online dating than married adults.

There are no statistically significant differences in the shares of adults who report ever using an online dating platform by race or ethnicity: Similar shares of White, Black, Hispanic and Asian adults report ever having done so.

Tinder tops the list of dating sites or apps the survey studied and is particularly popular among adults under 30. Some 46% of online dating users say they have ever used Tinder, followed by about three-in-ten who have used Match (31%) or Bumble (28%). OkCupid, eharmony and Hinge are each used by about a fifth of online dating users. Grindr and HER are used by very few online dating users overall (6% and 3%, respectively) but are more widely used by LGB adults than straight adults. Additionally, 31% of online dating users mention having tried some other online dating platform not asked about directly in this survey. (Read the topline  for a list of the most common other dating sites and apps users mentioned.)

Tinder use is far more common among younger adults than among older Americans: 79% of online dating users under 30 say they have used the platform, compared with 44% of users ages 30 to 49, 17% of users 50 to 64 and just 1% of those 65 and older. Tinder is the top online dating platform among users under 50. By contrast, users 50 and older are about five times more likely to use Match than Tinder (50% vs. 11%).

One-in-ten partnered adults – meaning those who are married, living with a partner or in a committed romantic relationship – met their current significant other through a dating site or app. Partnered adults who are under 30 or who are LGB stand out from other groups when looking at this measure of online dating “success”: One-in-five partnered adults under 30 say they met their current spouse or partner on a dating site or app, as do about a quarter of partnered LGB adults (24%).

Online dating users are somewhat divided over whether their experiences on these platforms have been positive or negative. Among those who have ever used a dating site or app, slightly more say their personal experiences have been very or somewhat positive than say they have been very or somewhat negative (53% vs. 46%).

Some demographic groups are more likely to report positive experiences. For example, 57% of men who have dated online say their experiences have been positive, while women users are roughly split down the middle (48% positive, 51% negative). In addition, LGB users of these platforms are more likely than straight users to report positive experiences (61% vs. 53%).

Roughly a third of online dating users (35%) say they have ever paid to use one of these platforms – including for extra features – but this varies by income, age and gender. Some 45% of online dating users with upper incomes report having paid to use a dating site or app, compared with 36% of users with middle incomes and 28% of those with lower incomes. Similarly, 41% of users 30 and older say they have paid to use these platforms, compared with 22% of those under 30. Men who have dated online are more likely than women to report having paid for these sites and apps (41% vs. 29%).

Those who have ever paid to use dating sites or apps report more positive experiences than those who have never paid. Around six-in-ten paid users (58%) say their personal experiences with dating sites or apps have been positive; half of users who have never paid say this.

Women who have used online dating platforms in the past year are more likely to feel overwhelmed by the number of messages they get, while men are more likely to feel insecure about a lack of messages. Among current or recent online dating users, 54% of women say they have felt overwhelmed by the number of messages they received on dating sites or apps in the past year, while just a quarter of men say the same. By contrast, 64% of men say they have felt insecure because of the lack of messages they received, while four-in-ten women say the same.

Overall, 55% of adults who have used a dating app or site in the past year say they often or sometimes felt insecure about the number of messages they received, while 36% say they often or sometimes felt overwhelmed.

Among recent online daters, large majorities of men and women say they have often or sometimes felt excited by the people they have seen while using these platforms, though large majorities also say they have often or sometimes felt disappointed.

When asked why they’ve turned to dating sites or apps in the past year, 44% of users say a major reason was to meet a long-term partner and 40% say a major reason was to date casually. Smaller shares say a major reason was to have casual sex (24%) or make new friends (22%).

Men who have used a dating platform in the past year are much more likely than women to say casual sex was a major reason (31% vs. 13%). There are no statistically significant gender differences on the other three reasons asked about in the survey.

About four-in-ten U.S. adults overall (42%) say online dating has made the search for a long-term partner easier. Far fewer (22%) say it has made the search for a long-term partner or spouse harder. About a third (32%) say it has made no difference.

Adults under 30 are less convinced than their older counterparts that online dating has made the search for a partner easier. These younger adults are about evenly divided in their views, with 35% of those ages 18 to 29 saying it has made the search easier and 33% saying it has made the search harder.

When it comes to the choices people have on dating sites and apps, 43% of adults overall say people have the right amount of options for dating on these platforms, while 37% think choices are too plentiful. Fewer (13%) say there are not enough options.

Most U.S. adults are skeptical or unsure that dating algorithms can predict love. About one-in-five adults (21%) think that the types of computer programs that dating sites and apps use could determine whether two people will eventually fall in love. But greater shares of Americans either say these programs could not do this (35%) or are unsure (43%).

Americans are split on whether online dating is a safe way to meet people, and a majority support requiring background checks before someone can create a profile. The share of U.S. adults who say online dating is generally a very or somewhat safe way to meet people has dipped slightly since 2019, from 53% to 48%. Women are more likely than men to say online dating is not too or not at all safe.

There are also differences by age: 62% of Americans ages 65 and older say online dating is not safe, compared with 53% of those 50 to 64 and 42% of adults younger than 50. Those who have never used a dating site or app are particularly likely to think it is unsafe: 57% say this, compared with 32% of those who have used an online dating site or app.

At the same time, six-in-ten Americans say companies should require background checks before someone creates a dating profile, while 15% say they should not and 24% are not sure. Women are more likely than men to say these checks should be required, as are adults 50 and older compared with younger adults.

These checks do not have majority support among online dating users themselves, however: 47% of users say companies should require background checks, versus 65% of those who have never used a dating site or app.

Younger women who have used dating sites or apps stand out for experiencing unwanted behaviors on these platforms. A majority of women under 50 who have used dating sites or apps (56%) say they have been sent a sexually explicit message or image they didn’t ask for, and about four-in-ten have had someone continue to contact them after they said they were not interested (43%) or have been called an offensive name (37%). Roughly one-in-ten of this group (11%) have received threats of physical harm. Each of these experiences is less common among women online dating users ages 50 and older, as well as among men of any age.

Among all online dating users, 38% have ever received unsolicited sexually explicit messages or images while using a dating site or app; 30% have experienced continued unwanted contact; 24% have been called an offensive name; and 6% have been threatened with physical harm.

About half of those who have used dating sites and apps (52%) say they have come across someone they think was trying to scam them. Men under 50 are particularly likely to say they have had this experience: 63% of men in this age group who have used dating sites or apps think they have encountered a scammer on them. Smaller shares of men ages 50 and older (47%) and women of any age (44%) say the same.

Note: Here are the questions used for this analysis, along with responses, and its methodology .

• Online Dating
• Romance & Dating

Emily A. Vogels is a former research associate focusing on internet and technology at Pew Research Center

Colleen McClain is a research associate focusing on internet and technology research at Pew Research Center

For Valentine’s Day, facts about marriage and dating in the U.S.

Dating at 50 and up: older americans’ experiences with online dating, about half of lesbian, gay and bisexual adults have used online dating, about half of never-married americans have used an online dating site or app, from looking for love to swiping the field: online dating in the u.s., most popular.

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Fox News' Tyrus instructs Donald Trump to ignore court imposed gag order

Tyrus: “Mr. President, forget the gag order”

Written by Media Matters Staff

Published 04/23/24 5:43 PM EDT

Citation From the April 23, 2024, edition of Fox News'  The Five

TYRUS (CO-HOST): I want to talk about the testimony for a little bit because, one, Mr. President, forget the gag order. We talked about this. Keep this in the street. People need to know what's going on. You know, it's a foregone conclusion, according to your legal team and yourself, so just go to town. I normally would never say -- this is the time for the 3:00 A.M. tweet. This is the time. This is the time to do all the things that people didn't want -- talk, talk. Because the way they're laying it out -- again, I didn't pass the bar, but I did grow up in the hood a little bit, we can kind of see when someone's laying it out to play it out. 

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Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

Affiliations

Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
• Criterion sampling-selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling-selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.  Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

• Internal validity--- Credibility
• External validity---Transferability
• Reliability---Dependability
• Objectivity---Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.  

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
• Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

• Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

• Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research.  Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc.  Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

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• Published: 19 April 2024

Asparagine reduces the risk of schizophrenia: a bidirectional two-sample mendelian randomization study of aspartate, asparagine and schizophrenia

• Huang-Hui Liu 1 , 2   na1 ,
• Yao Gao 1 , 2   na1 ,
• Dan Xu 1 , 2 ,
• Xin-Zhe Du 1 , 2 ,
• Si-Meng Wei 1 , 2 ,
• Jian-Zhen Hu 1 , 2 ,
• Yong Xu 1 , 2 &
• Liu Sha 1 , 2  

BMC Psychiatry volume  24 , Article number:  299 ( 2024 ) Cite this article

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Despite ongoing research, the underlying causes of schizophrenia remain unclear. Aspartate and asparagine, essential amino acids, have been linked to schizophrenia in recent studies, but their causal relationship is still unclear. This study used a bidirectional two-sample Mendelian randomization (MR) method to explore the causal relationship between aspartate and asparagine with schizophrenia.

This study employed summary data from genome-wide association studies (GWAS) conducted on European populations to examine the correlation between aspartate and asparagine with schizophrenia. In order to investigate the causal effects of aspartate and asparagine on schizophrenia, this study conducted a two-sample bidirectional MR analysis using genetic factors as instrumental variables.

No causal relationship was found between aspartate and schizophrenia, with an odds ratio (OR) of 1.221 (95%CI: 0.483–3.088, P -value = 0.674). Reverse MR analysis also indicated that no causal effects were found between schizophrenia and aspartate, with an OR of 0.999 (95%CI: 0.987–1.010, P -value = 0.841). There is a negative causal relationship between asparagine and schizophrenia, with an OR of 0.485 (95%CI: 0.262-0.900, P -value = 0.020). Reverse MR analysis indicates that there is no causal effect between schizophrenia and asparagine, with an OR of 1.005(95%CI: 0.999–1.011, P -value = 0.132).

This study suggests that there may be a potential risk reduction for schizophrenia with increased levels of asparagine, while also indicating the absence of a causal link between elevated or diminished levels of asparagine in individuals diagnosed with schizophrenia. There is no potential causal relationship between aspartate and schizophrenia, whether prospective or reverse MR. However, it is important to note that these associations necessitate additional research for further validation.

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Introduction

Schizophrenia is a serious psychiatric illness that affects 0.5 -1% of the global population [ 1 ]. The burden of mental illness was estimated to account for 7% of all diseases worldwide in 2016, and nearly 20% of all years lived with disability [ 2 ]. The characteristics of schizophrenia are positive symptoms, negative symptoms, and cognitive symptoms, which are often severe functional impairments and significant social maladaptations for patients suffering from schizophrenia [ 3 ]. It is still unclear what causes schizophrenia and what the pathogenesis is. There are a number of hypotheses based on neurochemical mechanisms, including dopaminergic and glutamatergic systems [ 4 ]. Although schizophrenia research has made significant advances in the past, further insight into its mechanisms and causes is still needed.

Association genetics research and genome-wide association studies have successfully identified more than 24 candidate genes that serve as molecular biomarkers for the susceptibility to treatment- refractory schizophrenia (TRS). It is worth noting that some proteins in these genes are related to glutamate transfer, especially the N-methyl-D-aspartate receptor (NMDAR) [ 5 ]. It is thought that NMDARs are important for neural plasticity, which is the ability of the brain itself to adapt to new environments. With age, NMDAR function usually declines, which may lead to decreased plasticity, leading to learning and memory problems. Consequently, the manifestation of cognitive deficits observed in diverse pathologies, including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, Parkinson’s disease, schizophrenia, and major depression, can be attributed to the dysfunction of NMDAR [ 4 ]. There are two enantiomers of aspartate (Asp): L and D [ 6 ]. In the brain, D-aspartate (D-Asp) stimulates glutamate receptors and dopaminergic neurons through its direct NMDAR agonist action [ 7 ]. According to the glutamate theory of Sch, glutamate NMDAR dysfunction is a primary contributor to the development of this psychiatric disorder and TRS [ 8 ]. It has been shown in two autopsy studies that D-Asp of prefrontal cortex neurons in patients with schizophrenia are significantly reduced, which is related to an increased expression of D-Asp oxidase [ 9 ] or an increased activity of D-Asp oxidase [ 10 ]. Several studies in animal models and humans have shown that D-amino acids, particularly D-Ser and D-Asp [ 11 ], are able to modulate several NMDAR-dependent processes, including synaptic plasticity, brain development, cognition and brain ageing [ 12 ]. In addition, D-Asp is synthesized in hippocampal and prefrontal cortex neurons, which play an important role in the development of schizophrenia [ 13 ]. It has been reported that the precursor substance of asparagine (Asn), aspartate, activates the N-methyl-D-aspartate receptor [ 14 ]. Asparagine is essential for the survival of all cells [ 15 ], and it was decreased in schizophrenia patients [ 16 ]. Asparagine can cause metabolic disorders of alanine, aspartate, and glutamic acid, leading to dysfunction of the glutamine-glutamate cycle and further affecting it Gamma-Aminobutyric Acid(GABA) level [ 17 ].It is widely understood that the imbalance of GABA levels and NMDAR plays a crucial role in the pathogenesis of schizophrenia, causing neurotoxic effects, synaptic dysfunction, and cognitive impairments [ 18 ].Schizophrenic patients exhibited significantly higher levels of serum aspartate, glutamate, isoleucine, histidine and tyrosine and significantly lower concentrations of serum asparagine, tryptophan and serine [ 19 ]. Other studies have also shown that schizophrenics have higher levels of asparagine, phenylalanine, and cystine, and lower ratios of tyrosine, tryptophan, and tryptophan to competing amino acids, compared to healthy individuals [ 20 ]. Aspartate and asparagine’s association with schizophrenia is not fully understood, and their causal relationship remains unclear.

The MR method is a method that uses Mendelian independence principle to infer causality, which uses genetic variation to study the impact of exposure on outcomes. By using this approach, confounding factors in general research are overcome, and causal reasoning is provided on a reasonable temporal basis [ 21 ]. The instrumental variables for genetic variation that are chosen must adhere to three primary hypotheses: the correlation hypothesis, which posits a robust correlation between single nucleotide polymorphisms (SNPs) and exposure factors; the independence hypothesis, which asserts that SNPs are not affected by various confounding factors; the exclusivity hypothesis, which maintains that SNPs solely influence outcomes through on exposure factors. In a recent study, Mendelian randomization was used to reveal a causal connection between thyroid function and schizophrenia [ 22 ]. According to another Mendelian randomization study, physical activity is causally related to schizophrenia [ 23 ]. Therefore, this study used Mendelian randomization method to explore the causal effects of aspartate on schizophrenia and asparagine on schizophrenia.

To elucidate the causal effects of aspartate and asparagine on schizophrenia. This study used bidirectional MR analysis. In the prospective analysis of MR, the exposure factors under consideration were aspartate and asparagine, while the outcome of interest was the risk of schizophrenia. On the contrary, in the reverse MR analysis, schizophrenia was utilized as the exposure factor, with aspartate and asparagine being chosen as the outcomes.

Materials and methods

Obtaining data sources, select genetic tools closely related to aspartate or asparagine.

In this research, publicly accessible GWAS summary statistical datasets from the MR basic platform were utilized. These datasets consisted of 7721 individuals of European ancestry [ 24 ] for the exposure phenotype instrumental variable of aspartate, and 7761 individuals of European ancestry [ 24 ] for the exposure phenotype instrumental variable of asparagine.

Select genetic tools closely related to schizophrenia

Data from the MR basic platform was used in this study for GWAS summary statistics, which included 77,096 individuals of European ancestry [ 5 ], as instrumental variables related to schizophrenia exposure phenotype.

Obtaining result data

The publicly accessible GWAS summary statistical dataset for schizophrenia was utilized on the MR basic platform, with a sample size of 77096. Additionally, the summary level data for aspartate and asparagine were obtained from the publicly available GWAS summary dataset on the MR basic platform, with sample sizes of 7721 and 7761, respectively, serving as outcome variables.

Instrumental variable filtering

Eliminating linkage disequilibrium.

The selection criteria for identifying exposure related SNPs from the aggregated data of GWAS include: (1) Reaching a significance level that meets the threshold for whole genome research, expressed as P -value < 5 * 10 − 6 [ 25 ]; (2) Ensure the independence of the selected SNPs and eliminate linkage disequilibrium SNPs ( r 2  < 0.001, window size of 10000KB) [ 26 ]; (3) There are corresponding data related to the research results in the GWAS summary data.

Eliminating weak instruments

To evaluate whether the instrumental variables selected for this MR study have weak values, we calculated the F-statistic. If the F-value is greater than 10, it indicates that there are no weak instruments in this study, indicating the reliability of the study. Using the formula F =[(N-K-1)/K] × [R 2 /(1-R 2 )], where N denotes the sample size pertaining to the exposure factor, K signifies the count of instrumental variables, and R 2 denotes the proportion of variations in the exposure factor that can be elucidated by the instrumental variables.

The final instrumental variable obtained

As a result of removing linkage disequilibrium and weak instrumental variables, finally, 3 SNPs related to aspartate and 24 SNPs related to asparagine were selected for MR analysis.

Bidirectional MR analysis

Research design.

Figure  1 presents a comprehensive depiction of the overarching design employed in the MR analysis undertaken in this study. We ascertained SNPs exhibiting robust correlation with the target exposure through analysis of publicly available published data, subsequently investigating the existence of a causal association between these SNPs and the corresponding outcomes. This study conducted two bidirectional MR analyses, one prospective and reverse MR on the causal relationship between aspartate and schizophrenia, and the other prospective and reverse MR on the causal relationship between asparagine and schizophrenia.

A MR analysis of aspartate and schizophrenia (located in the upper left corner). B  MR analysis of schizophrenia and aspartate (located in the upper right corner). C  MR analysis of asparagine and schizophrenia (located in the lower left corner). D  MR analysis of schizophrenia and asparagine (located in the lower right corner)

Statistical analysis

Weighted median, weighted mode, MR Egger, and inverse variance weighting (IVW) were used to conduct a MR study. The primary research findings were derived from the results obtained through IVW, the results of sensitivity analysis using other methods to estimate causal effects are considered. Statistical significance was determined if the P -value was less than 0.05. To enhance the interpretation of the findings, this study converted the beta values obtained in to OR, accompanied by the calculation of a 95% confidence interval (CI).

Test for directional horizontal pleiotropy

This study used MR Egger intercept to test horizontal pleiotropy. If the P -value is greater than 0.05, it indicates that there is no horizontal pleiotropy in this study, meaning that instrumental variables can only regulate outcome variables through exposure factors.

Results of bidirectional MR analysis of aspartate and schizophrenia

Analysis results of aspartate and schizophrenia.

In prospective MR analysis, this study set aspartate as the exposure factor and schizophrenia as the outcome. We used 3 SNPs significantly associated with aspartate screened across the entire genome. The instrumental variables exhibited F-values exceeding 10, signifying the absence of weak instruments and thereby affirming the robustness of our findings. Through MR analysis (Fig.  2 A), we assessed the individual influence of each SNP locus on schizophrenia. The results of the IVW method indicate that no causal effect was found between aspartate and schizophrenia, with an OR of 1.221 (95%CI: 0.483–3.088, P -value = 0.674).

In addition, the analyses conducted using the weighted mode and weighted median methods yielded similar results, indicating the absence of a causal association between aspartate and schizophrenia. Furthermore, the MR Egger analysis demonstrated no statistically significant disparity in effectiveness between aspartate and schizophrenia, as evidenced by a P -value greater than 0.05 (Table  1 ; Fig.  2 B).

In order to test the reliability of the research results, this study used MR Egger intercept analysis to examine horizontal pleiotropy, and the result was P -value = 0.579 > 0.05, indicating the absence of level pleiotropy. Furthermore, a leave-one-out test was conducted to demonstrate that no single SNP had a substantial impact on the stability of the results, indicating that this study has considerable stability (Fig.  2 C). Accordingly, the MR analysis results demonstrate the conclusion that aspartate and schizophrenia do not exhibit a causal relationship.

Analysis results of schizophrenia and aspartate

Different from prospective MR studies, in reverse MR studies, schizophrenia was set as an exposure factor and aspartate was set as the outcome. Through MR analysis (Fig.  2 D), we assessed the individual influence of each SNP locus on aspartate .The results of the IVW method indicate that there is no causal effect between schizophrenia and aspartate, with an OR of 0.999(95%CI: 0.987–1.010, P -value = 0.841). Similarly, the weighted mode, weighted median methods also failed to demonstrate a causal link between schizophrenia and aspartate. Additionally, the MR Egger analysis did not reveal any statistically significant difference in effectiveness between schizophrenia and aspartate ( P -value > 0.05) (Table  1 and Fig . 2 E).

The MR Egger intercept was used to test horizontal pleiotropy, and the result was P -value = 0.226 > 0.05, proving that this study is not affected by horizontal pleiotropy. Furthermore, a leave-one-out test revealed that no individual SNP significantly influenced the robustness of the findings (Fig.  2 F).

Depicts the causal association between aspartate and schizophrenia through diverse statistical analyses, as well as the causal association between schizophrenia and aspartate through diverse statistical analyses. A The forest plot of aspartate related SNPs and schizophrenia analysis results, with the red line showing the MR Egger test and IVW method. B  Scatter plot of the analysis results of aspartate and schizophrenia, with the slope indicating the strength of the causal relationship. C  Leave-one-out test of research results on aspartate and schizophrenia. D The forest plot of schizophrenia related SNPs and aspartate analysis results, with the red line showing the MR Egger test and IVW method. E  Scatter plot of the analysis results of schizophrenia and aspartate, with the slope indicating the strength of the causal relationship. F  Leave-one-out test of research results on schizophrenia and aspartate

Results of bidirectional MR analysis of asparagine and schizophrenia

Analysis results of asparagine and schizophrenia.

In prospective MR studies, we used asparagine as an exposure factor and schizophrenia as a result to investigate the potential causal relationship between them. Through a rigorous screening process, we identified 24 genome-wide significant SNPs associated with asparagine. In addition, the instrumental variable F values all exceeded 10, indicating that this study was not affected by weak instruments, thus proving the stability of the results. This study conducted MR analysis to evaluate the impact of all SNP loci on schizophrenia. (Fig.  3 A). According to the results of IVW, a causal relationship was found between asparagine and schizophrenia, and the relationship is negatively correlated, with an OR of 0.485 (95%CI: 0.262-0.900, P -value = 0.020).

The weighted median results also showed a causal relationship between asparagine and schizophrenia, and it was negatively correlated. In the weighted mode method, asparagine and schizophrenia did not have a causal relationship, while in the MR Egger method, there was no statistically significant difference in efficacy between them ( P -value > 0.05) (Table  1 ; Fig.  3 B).

In order to examine the horizontal pleiotropy, the MR Egger intercept was applied, and P -value = 0.768 > 0.05 result proves that this study is not affected by horizontal pleiotropy Furthermore, a leave-one-out test was conducted to demonstrate that no individual SNP had a substantial impact on the stability of the results, indicating that this study has good stability. (Fig.  3 C). Therefore, MR analysis shows that asparagine is inversely proportional to schizophrenia.

Analysis results of schizophrenia and asparagine

In reverse MR analysis, schizophrenia is considered an exposure factor, and asparagine is considered the result, studying the causal effects of schizophrenia and asparagine. Through MR analysis (Fig.  3 D), we assessed the individual influence of each SNP locus on s asparagine. The IVW method results indicated no potential causal relationship between schizophrenia and asparagine, with an OR of 1.005(95%CI: 0.999–1.011, P -value = 0.132). The research results of weighted mode method and weighted median method did not find a causal effects of schizophrenia and asparagine. Additionally, the MR Egger analysis did not reveal any statistically significant difference in effectiveness between schizophrenia and asparagine ( P -value > 0.05) (Table  1 ; Fig.  3 E).

In order to examine the horizontal pleiotropy, the MR Egger intercept was applied, and the result was P -value = 0.474 > 0.05, proving that this study is not affected by horizontal pleiotropy. Furthermore, a leave-one-out test was conducted to demonstrate that no individual SNP had a substantial impact on the stability of the results, indicating that this study has good stability (Fig.  3 F).

Depicts the causal association between asparagine and schizophrenia through diverse statistical analyses, as well as the causal association between schizophrenia and asparagine through diverse statistical analyses. A  The forest plot of asparagine related SNPs and schizophrenia analysis results, with the red line showing the MR Egger test and IVW method. B  Scatter plot of the analysis results of asparagine and schizophrenia, with the slope indicating the strength of the causal relationship. C Leave-one-out test of research results on asparagine and schizophrenia. D  The forest plot of schizophrenia related SNPs and asparagine analysis results, with the red line showing the MR Egger test and IVW method. E  Scatter plot of the analysis results of schizophrenia and asparagine, with the slope indicating the strength of the causal relationship. F  Leave-one-out test of research results on schizophrenia and asparagine

In this study, the MR analysis results after sensitivity analysis suggested a causal relationship between asparagine and schizophrenia, which was negatively correlated. However, the reverse MR analysis did not reveal any potential relationship between schizophrenia and asparagine, no potential causal relationship between aspartate and schizophrenia was found in both prospective and reverse MR analyses (Fig.  4 ).

Summary of results from bidirectional two-sample MR study

The levels of asparagine in schizophrenia patients decrease, according to studies [ 16 ]. Based on the findings of the Madis Parksepp research team, a continuous five-year administration of antipsychotic drugs (AP) has been observed to induce significant metabolic changes in individuals diagnosed with schizophrenia. Significantly, the concentrations of asparagine, glutamine (Gln), methionine, ornithine, and taurine have experienced a substantial rise, whereas aspartate, glutamate (Glu), and alpha-aminoadipic acid(α-AAA) levels have demonstrated a notable decline. Olanzapine (OLZ) treatment resulted in significantly lower levels of Asn compared to control mice [ 27 ]. Asn and Asp play significant roles in various biological processes within the human body, such as participating in glycoprotein synthesis and contributing to brain functionality. It is worth noting that the ammonia produced in brain tissue needs to have a rapid excretion pathway in the brain. Asn plays a crucial role in regulating cellular function within neural tissues through metabolic control. This amino acid is synthesized by the combination of Asp and ammonia, facilitated by the enzyme asparagine synthase. Additionally, the brain effectively manages ammonia elimination by producing glutamine Gln and Asn. This may be an explanation for the significant increase in Asn and Gln levels (as well as a decrease in Asp and Glu levels) during 5 years of illness and after receiving AP treatment [ 28 ]. The study by Marie Luise Rao’s team compared unmedicated schizophrenic patients, healthy individuals and patients receiving antipsychotic treatment. Unmedicated schizophrenics had higher levels of asparagine, citrulline, phenylalanine, and cysteine, while the ratios of tyrosine, tryptophan, and tryptophan to competing amino acids were significantly lower than in healthy individuals [ 29 ].

The findings of our study demonstrate an inverse association between asparagine levels and the susceptibility to schizophrenia, suggesting that asparagine may serve as a protective factor against the development of this psychiatric disorder. However, we did not find a causal relationship between schizophrenia and asparagine. Consequently, additional investigation and scholarly discourse are warranted to gain a comprehensive understanding of this complex association.

Two different autopsy studies measured D-ASP levels in two different brain samples from patients with schizophrenia and a control group [ 14 ]. The first study, which utilized a limited sample size (7–10 subjects per diagnosis), demonstrated a reduction in D-ASP levels within the prefrontal cortex (PFC) postmortem among individuals diagnosed with schizophrenia, amounting to approximately 101%. This decrease was found to be correlated with a notable elevation in D-aspartate oxidase (DDO) mRNA levels within the same cerebral region [ 30 ]. In addition, the second study was conducted on a large sample size (20 subjects/diagnosis/brain regions). The findings of this study indicated a noteworthy decrease of approximately 30% in D-ASP selectivity within the dorsal lateral PFC (DLPFC) of individuals diagnosed with schizophrenia, when compared to corresponding brain regions of individuals without schizophrenia. However, no significant reduction in D-ASP was observed in the hippocampus of patients with schizophrenia. The decrease in D-Asp content was associated with a significant increase (about 25%) in DDO enzyme activity in the DLPFC of schizophrenia patients. This observation highlights the existence of a dysfunctional metabolic process in DDO activity levels in the brains of schizophrenia patients [ 31 ].

Numerous preclinical investigations have demonstrated the influence of D-Asp on various phenotypes reliant on NMDAR, which are linked to schizophrenia. After administering D-Asp to D-Asp oxidase gene knockout mice, the abnormal neuronal pre-pulse inhibition induced by psychoactive drugs such as MK-801 and amphetamine was significantly reduced by the sustained increase in D-Asp [ 32 ]. According to a review, free amino acids, specifically D-Asp and D-Ser (D-serine), have been identified as highly effective and safe nutrients for promoting mental well-being. These amino acids not only serve as integral components of the central nervous system’s structural proteins, but also play a vital role in maintaining optimal functioning of the central nervous system. This is due to their essential role in regulating neurotransmitter levels, including dopamine, norepinephrine, serotonin, and others. For many patients with schizophrenia, a most persistent and effective improvement therapy may be supplementing amino acids, which can improve the expected therapeutic effect of AP and alleviate positive and negative symptoms of schizophrenia [ 33 ].

Numerous studies have demonstrated a plausible correlation between aspartate and schizophrenia; however, our prospective and reverse MR investigations have failed to establish a causal link between aspartate and schizophrenia. This discrepancy may be attributed to the indirect influence of aspartate on the central nervous system through the stimulation of NMDAR, necessitating further investigation to elucidate the direct relationship between aspartate and schizophrenia.

This study used a bidirectional two-sample MR analysis method to explore the causal relationship between aspartate and asparagine with schizophrenia, as well as its inverse relationship [ 34 ]. The utilization of MR analysis presents numerous benefits in the determination of causality [ 35 ]. Notably, the random allocation of alleles to gametes within this method permits the assumption of no correlation between instrumental variables and confounding factors. Consequently, this approach effectively alleviates bias stemming from confounding factors during the inference of causality. Furthermore, the study’s utilization of a substantial sample size in the GWAS summary data engenders a heightened level of confidence in the obtained results [ 36 ]. Consequently, this investigation not only advances the existing body of research on the relationship between aspartate and asparagine with schizophrenia, but also contributed to clinical treatment decisions for patients with schizophrenia.

Nevertheless, this study possesses certain limitations, as it solely relies on populations of European ancestry for both exposure and results. Consequently, it remains uncertain whether these findings can be replicated among non-European races, necessitating further investigation. In addition, in this study, whether the effects of aspartate and asparagine on schizophrenia vary by gender or age cannot be evaluated, and stratified MR analysis should be performed. Additional experimental research is imperative for a comprehensive understanding of the underlying biological mechanisms connecting aspartate and asparagine with schizophrenia.

In summary, our MR analysis found a negative correlation between asparagine and schizophrenia, indicating that asparagine reduces the risk of schizophrenia. However, there is no potential causal relationship between schizophrenia and asparagine. This study provides new ideas for the early detection of schizophrenia in the clinical setting and offers new insights into the etiology and pathogenesis of schizophrenia. Nonetheless, additional research is required to elucidate the potential mechanisms that underlie the association between aspartate and asparagine with schizophrenia.

Availability of data and materials

The datasets generated and analysed during the current study are available in the GWAS repository. https://gwas.mrcieu.ac.uk/datasets/met-a-388/ , https://gwas.mrcieu.ac.uk/datasets/met-a-638/ , https://gwas.mrcieu.ac.uk/datasets/ieu-b-42/ .

Charlson FJ, Ferrari AJ, Santomauro DF, Diminic S, Stockings E, Scott JG, McGrath JJ, Whiteford HA. Global epidemiology and burden of schizophrenia: findings from the global burden of disease study 2016. Schizophr Bull. 2018;44(6):1195–203.

Article   PubMed   PubMed Central   Google Scholar  

Rehm J, Shield KD. Global burden of disease and the impact of mental and addictive disorders. Curr Psychiatry Rep. 2019;21(2):10.

Article   PubMed   Google Scholar  

Vita A, Minelli A, Barlati S, Deste G, Giacopuzzi E, Valsecchi P, Turrina C, Gennarelli M. Treatment-resistant Schizophrenia: genetic and neuroimaging correlates. Front Pharmacol. 2019;10:402.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Adell A. Brain NMDA receptors in schizophrenia and depression. Biomolecules. 2020;10(6):947.

Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–7.

Abdulbagi M, Wang L, Siddig O, Di B, Li B. D-Amino acids and D-amino acid-containing peptides: potential disease biomarkers and therapeutic targets? Biomolecules. 2021;11(11):1716.

Krashia P, Ledonne A, Nobili A, Cordella A, Errico F, Usiello A, D’Amelio M, Mercuri NB, Guatteo E, Carunchio I. Persistent elevation of D-Aspartate enhances NMDA receptor-mediated responses in mouse substantia Nigra pars compacta dopamine neurons. Neuropharmacology. 2016;103:69–78.

Article   CAS   PubMed   Google Scholar  

Kantrowitz JT, Epstein ML, Lee M, Lehrfeld N, Nolan KA, Shope C, Petkova E, Silipo G, Javitt DC. Improvement in mismatch negativity generation during d-serine treatment in schizophrenia: correlation with symptoms. Schizophr Res. 2018;191:70–9.

Elkis H, Buckley PF. Treatment-resistant Schizophrenia. Psychiatr Clin North Am. 2016;39(2):239–65.

Dunlop DS, Neidle A, McHale D, Dunlop DM, Lajtha A. The presence of free D-aspartic acid in rodents and man. Biochem Biophys Res Commun. 1986;141(1):27–32.

de Bartolomeis A, Vellucci L, Austin MC, De Simone G, Barone A. Rational and translational implications of D-Amino acids for treatment-resistant Schizophrenia: from neurobiology to the clinics. Biomolecules. 2022;12(7):909.

Taniguchi K, Sawamura H, Ikeda Y, Tsuji A, Kitagishi Y, Matsuda S. D-amino acids as a biomarker in schizophrenia. Diseases. 2022;10(1):9.

Singh SP, Singh V. Meta-analysis of the efficacy of adjunctive NMDA receptor modulators in chronic schizophrenia. CNS Drugs. 2011;25(10):859–85.

Errico F, Napolitano F, Squillace M, Vitucci D, Blasi G, de Bartolomeis A, Bertolino A, D’Aniello A, Usiello A. Decreased levels of D-aspartate and NMDA in the prefrontal cortex and striatum of patients with schizophrenia. J Psychiatr Res. 2013;47(10):1432–7.

Rousseau J, Gagné V, Labuda M, Beaubois C, Sinnett D, Laverdière C, Moghrabi A, Sallan SE, Silverman LB, Neuberg D, et al. ATF5 polymorphisms influence ATF function and response to treatment in children with childhood acute lymphoblastic leukemia. Blood. 2011;118(22):5883–90.

Liu L, Zhao J, Chen Y, Feng R. Metabolomics strategy assisted by transcriptomics analysis to identify biomarkers associated with schizophrenia. Anal Chim Acta. 2020;1140:18–29.

Cao B, Wang D, Brietzke E, McIntyre RS, Pan Z, Cha D, Rosenblat JD, Zuckerman H, Liu Y, Xie Q, et al. Characterizing amino-acid biosignatures amongst individuals with schizophrenia: a case-control study. Amino Acids. 2018;50(8):1013–23.

Olthof BMJ, Gartside SE, Rees A. Puncta of neuronal nitric oxide synthase (nNOS) mediate NMDA receptor signaling in the Auditory Midbrain. J Neuroscience: Official J Soc Neurosci. 2019;39(5):876–87.

Article   CAS   Google Scholar  

Tortorella A, Monteleone P, Fabrazzo M, Viggiano A, De Luca L, Maj M. Plasma concentrations of amino acids in chronic schizophrenics treated with clozapine. Neuropsychobiology. 2001;44(4):167–71.

Rao ML, Strebel B, Gross G, Huber G. Serum amino acid profiles and dopamine in schizophrenic patients and healthy subjects: window to the brain? Amino Acids. 1992;2(1–2):111–8.

Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ (Clinical Res ed). 2005;330(7499):1076–9.

Article   Google Scholar  

Freuer D, Meisinger C. Causal link between thyroid function and schizophrenia: a two-sample mendelian randomization study. Eur J Epidemiol. 2023;38(10):1081–8.

Papiol S, Schmitt A, Maurus I, Rossner MJ, Schulze TG, Falkai P. Association between Physical Activity and Schizophrenia: results of a 2-Sample mendelian randomization analysis. JAMA Psychiat. 2021;78(4):441–4.

Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50.

Zhao JV, Kwok MK, Schooling CM. Effect of glutamate and aspartate on ischemic heart disease, blood pressure, and diabetes: a mendelian randomization study. Am J Clin Nutr. 2019;109(4):1197–206.

Zhou K, Zhu L, Chen N, Huang G, Feng G, Wu Q, Wei X, Gou X. Causal associations between schizophrenia and cancers risk: a mendelian randomization study. Front Oncol. 2023;13:1258015.

Zapata RC, Rosenthal SB, Fisch K, Dao K, Jain M, Osborn O. Metabolomic profiles associated with a mouse model of antipsychotic-induced food intake and weight gain. Sci Rep. 2020;10(1):18581.

Parksepp M, Leppik L, Koch K, Uppin K, Kangro R, Haring L, Vasar E, Zilmer M. Metabolomics approach revealed robust changes in amino acid and biogenic amine signatures in patients with schizophrenia in the early course of the disease. Sci Rep. 2020;10(1):13983.

Rao ML, Gross G, Strebel B, Bräunig P, Huber G, Klosterkötter J. Serum amino acids, central monoamines, and hormones in drug-naive, drug-free, and neuroleptic-treated schizophrenic patients and healthy subjects. Psychiatry Res. 1990;34(3):243–57.

Errico F, D’Argenio V, Sforazzini F, Iasevoli F, Squillace M, Guerri G, Napolitano F, Angrisano T, Di Maio A, Keller S, et al. A role for D-aspartate oxidase in schizophrenia and in schizophrenia-related symptoms induced by phencyclidine in mice. Transl Psychiatry. 2015;5(2):e512.

Nuzzo T, Sacchi S, Errico F, Keller S, Palumbo O, Florio E, Punzo D, Napolitano F, Copetti M, Carella M, et al. Decreased free d-aspartate levels are linked to enhanced d-aspartate oxidase activity in the dorsolateral prefrontal cortex of schizophrenia patients. NPJ Schizophr. 2017;3:16.

Errico F, Rossi S, Napolitano F, Catuogno V, Topo E, Fisone G, D’Aniello A, Centonze D, Usiello A. D-aspartate prevents corticostriatal long-term depression and attenuates schizophrenia-like symptoms induced by amphetamine and MK-801. J Neurosci. 2008;28(41):10404–14.

Nasyrova RF, Khasanova AK, Altynbekov KS, Asadullin AR, Markina EA, Gayduk AJ, Shipulin GA, Petrova MM, Shnayder NA. The role of D-Serine and D-aspartate in the pathogenesis and therapy of treatment-resistant schizophrenia. Nutrients. 2022;14(23):5142.

Hao D, Liu C. Deepening insights into food and medicine continuum within the context of pharmacophylogeny. Chin Herb Med. 2023;15(1):1–2.

PubMed   Google Scholar  

Zhang Y. Awareness and ability of paradigm shift are needed for research on dominant diseases of TCM. Chin Herb Med. 2023;15(4):475.

CAS   PubMed   PubMed Central   Google Scholar  

Chen J. Essential role of medicine and food homology in health and wellness. Chin Herb Med. 2023;15(3):347–8.

PubMed   PubMed Central   Google Scholar  

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This work was supported by the National Natural Science Foundation of China (82271546, 82301725, 81971601); National Key Research and Development Program of China (2023YFC2506201); Key Project of Science and Technology Innovation 2030 of China (2021ZD0201800, 2021ZD0201805); China Postdoctoral Science Foundation (2023M732155); Fundamental Research Program of Shanxi Province (202203021211018, 202203021212028, 202203021212038). Research Project Supported by Shanxi Scholarship Council of China (2022 − 190); Scientific Research Plan of Shanxi Health Commission (2020081, 2020SYS03,2021RC24); Shanxi Provincial Administration of Traditional Chinese Medicine (2023ZYYC2034), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (2022L132); Shanxi Medical University School-level Doctoral Initiation Fund Project (XD2102); Youth Project of First Hospital of Shanxi Medical University (YQ2203); Doctor Fund Project of Shanxi Medical University in Shanxi Province (SD2216); Shanxi Science and Technology Innovation Talent Team (202304051001049); 136 Medical Rejuvenation Project of Shanxi Province, China; STI2030-Major Projects-2021ZD0200700. Key laboratory of Health Commission of Shanxi Province (2020SYS03);

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Huang-Hui Liu and Yao Gao contributed equally to this work.

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Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, NO.85 Jiefang Nan Road, Taiyuan, China

Huang-Hui Liu, Yao Gao, Dan Xu, Xin-Zhe Du, Si-Meng Wei, Jian-Zhen Hu, Yong Xu & Liu Sha

Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China

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Huang-Hui Liu and Yao Gao provided the concept and designed the study. Huang-Hui Liu and Yao Gao conducted the analyses and wrote the manuscript. Dan Xu, Huang-Hui Liu and Yao Gao participated in data collection. Xin-Zhe Du, Si-Meng Wei and Jian-Zhen Hu participated in the analysis of the data. Liu Sha, Yong Xu and Yao Gao revised and proof-read the manuscript. All authors contributed to the article and approved the submitted version.

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Liu, HH., Gao, Y., Xu, D. et al. Asparagine reduces the risk of schizophrenia: a bidirectional two-sample mendelian randomization study of aspartate, asparagine and schizophrenia. BMC Psychiatry 24 , 299 (2024). https://doi.org/10.1186/s12888-024-05765-5

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NEWS... BUT NOT AS YOU KNOW IT

Your surname could be ruining your career, study finds

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Teachers marking work in alphabetical order could be inadvertently impacting their student’s career prospects – and not in a good way.

New research from the University of Michigan found that students who had surnames that came later in the alphabet received lower grades than those at the top of roll call. 

They also found that these students tend to receive ruder comments on their work and have lower grading quality, measured by post-grade complaints from students. 

Associate professor and first author Dr Jun Li said: ‘We spend a lot of time thinking about how to make the grading fair and accurate. But even for me it was really surprising.

‘It didn’t occur to us until we looked at the data and realised that sequence makes a difference.’

The study looked at more than 30 million grading scores through the popular grading software, Canvas, which gives student papers to teachers for grading in alphabetical order by default. 

They found a clear pattern of decline in grading quality as teachers marked more assignments. Students whose surnames began with A, B, C, D, or E received a 0.3-point higher grade out of 100 possible points, compared to when they were graded randomly.

However, students with a surname lower down in the alphabet, from U-Z, received a 0.3-point lower grade, creating a 0.6-point gap.

And for a small group of graders when the alphabet was flipped, A-E students were worse off, while W-Z students received higher grades compared to what they would receive when graded randomly.

The 0.6-point gap may seem small, but it did affect students’ averages – and might even impact their future career paths.

The findings echo similar results from a 1970 study where researchers found that higher achievement was associated with students whose last names began with the letters toward the beginning of the alphabet – but not all of the results were statistically significant.

Study co-author Helen Wang said: ‘Our conclusion is this may be something that happened unconsciously by the graders that’s actually creating a real social impact.’

Can surnames influence your grades?

Research from the University of Cambridge reveals the mean GCSE scores of candidates with occupational surnames such as Baker or Cook were slightly lower than those with other surnames.

The average mean GCSE score for candidates with an occupational (non-status) surname was 5.16, but candidates whose surnames had neither occupational nor status origin had an average score of 5.25.

The findings were similar to the difference expected between candidates half a year apart in age, but smaller than the well-known ‘gap’ between male and female GCSE candidates.

The reason for the findings is unclear, but researchers suggest that teachers might get tired and irritable as they pass through the alphabet.

Jiaxin Pei, another co-author of the study, said: ‘We kind of suspect that fatigue is one of the major factors that is driving this effect.

‘When you’re working on something for a long period of time, you get tired and then you start to lose your attention and your cognitive abilities are dropping.’

Researchers say the option to grade the assignments in a random order does exist, but alphabetical mode is the default in Canvas and other online learning management systems.

They also suggest academic institutions could hire more graders for larger classes. 

Dr Li added: ‘A college student emailed us afterward asking us to share the paper with him. He mentioned that his last name started with W. He’s going to tell his parents it’s not because of him it’s because of his last name.’

The study is under review by the journal  Management Science .

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Evaluation of the potential of recovering various valuable elements from a vanadiferous titanomagnetite tailing based on chemical and process mineralogical characterization

• Research Article
• Published: 23 June 2023
• Volume 30 , pages 83991–84001, ( 2023 )

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• Jinsheng Liu   ORCID: orcid.org/0000-0001-6221-212X 1 , 2 ,
• Zhenxing Xing 1 , 2 ,
• Jianxing Liu 1 , 2 ,
• Xueyong Ding 1 , 2 &
• Xiangxin Xue 1 , 2  

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In order to evaluate the potential of recovering various valuable elements from vanadiferous titanomagnetite tailing (VTMT), the chemical and process mineralogical characterization of VTMT were investigated in this study by various analytical techniques such as XRF, XRD, optical microscopy, SEM, EDS, and AMICS. It was found that VTMT is a coarser powder in general; about 50% of the particle size is greater than 54.30 μm. The total iron content of the VTMT was 22.40 wt.%, and its TiO 2 grade is 14.45 wt.%, even higher than those found in natural ilmenite ores. The majority of iron and titanium were located in ilmenite and hematite; 62.84% of hematite and 90.27% of ilmenite were present in monomeric form. However, there is still a portion of ilmenite and hematite embedded in gangue such as anorthite, diopside, and serpentite. For the recovery of valuable fractions such as Fe and TiO 2 from VTMT, a treatment process including ball milling–high-intensity magnetic separation–one roughing and three refining flotation was proposed. Finally, a concentrate with TiO 2 grade of 47.31% and total Fe (TFe) grade of 35.44% was produced; TiO 2 and TFe had recovery rates of 57.71% and 28.23%, respectively. The recovered product is adequate as a raw material for the production of rutile. This study provides a reference and a new research direction for the recycling and comprehensive utilization of VTMT.

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Process Mineralogical Analysis of a Typical Vanadium Titano-magnetite Concentrate

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Rina Kim, Yuik Eom, … Ho-Seok Jeon

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The data is publically available, and all source of data used in this research is given in the manuscript.

Abdollahi M, Bahrami A, Mirmohammadi MS, Kazemi F, Danesh A, Ghorbani Y (2020) A process mineralogy approach to optimize molybdenite flotation in copper – molybdenum processing plants. Miner Eng 157:106557

Article   CAS   Google Scholar  

Alfonso P, Ruiz M, Zambrana RN, Sendrós M, Garcia-Valles M, Anticoi H, Sidki-Rius N, Salas A (2022) Process mineralogy of the tailings from Llallagua: towards a sustainable activity. Minerals 12:214–229

Barik K, Prusti P, Soren S, Meikap BC, Biswal SK (2022) Analysis of iron ore pellets properties concerning raw material mineralogy for effective utilization of mining waste. Powder Technol 400:117259

Baum W (2014) Ore characterization, process mineralogy and lab automation a roadmap for future mining. Miner Eng 60:69–73

Brough CP, Warrender R, Bowell RJ, Barnes A, Parbhakar-Fox A (2013) The process mineralogy of mine wastes. Miner Eng 52:125–135

Ciarapica F, Bevilacqua M, Antomarioni S (2019) An approach based on association rules and social network analysis for managing environmental risk: a case study from a process industry. Process Saf Environ Prot 128:50–64

Gan CD, Cui SF, Wu ZZ, Yang JY (2022) Multiple heavy metal distribution and microbial community characteristics of vanadium-titanium magnetite tailing profiles under different management modes. J Hazard Mater 429:128032

Gao ZX, Yang ST, Xue XX, Yang H, Cheng GJ (2020) Extraction method for valuable elements of low-grade vanadia–titania magnetite. J Cleaner Prod 250:119451

Guanira K, Valente TM, Ríos CA, Castellanos OM, Salazar L, Lattanzi D, Jaime P (2020) Methodological approach for mineralogical characterization of tailings from a Cu(Au, Ag) skarn type deposit using QEMSCAN (Quantitative Evaluation of Minerals by Scanning Electron Microscopy). J Geochem Explor 209:106439

Han Y, Kim S, Go B, Lee S, Park S, Jeon HS (2021) Optimized magnetic separation for efficient recovery of V and Ti enriched concentrates from vanadium-titanium magnetite ore: Effect of grinding and magnetic intensity. Powder Technol 391:282–291

Kang X, Cui Y, Shen T, Yan M, Tu W, Shoaib M, Xiang Q, Zhao K, Gu Y, Chen Q, Li S, Liang Y, Ma M, Zou L, Yu X (2020) Changes of root microbial populations of natively grown plants during natural attenuation of V-Ti magnetite tailings. Ecotoxicol Environ Saf 201:110816

Kelvin M, Whiteman E, Petrus J, Leybourne M, Nkuna V (2022) Application of LA-ICP-MS to process mineralogy: Gallium and germanium recovery at Kipushi copper-zinc deposit. Miner Eng 176:107322

Li L, Jiang T, Chen B, Zhou M, Chen C (2020) Overall utilization of vanadium–titanium magnetite tailings to prepare lightweight foam ceramics. Process Saf Environ Prot 139:305–314

Li L, Jiang T, Chen BJ, Wen J, Yang GD (2022a) Integrated utilization of vanadium-titanium magnetite tailings for synthesis of lightweight foamed ceramics: effect of chemical composition on the properties and phase evolution. J Sustainable Metall 8:517–525

Article   Google Scholar  

Li WB, Liu X, Liu DQ, Han YX (2022b) Mineralogical reconstruction of Titanium-Vanadium hematite and magnetic separation mechanism of titanium and iron minerals. Adv Powder Technol 33:103408

Liu J, Liu RZ, Zhang ZJ, Cai YP, Zhang LX (2019) A Bayesian Network-based risk dynamic simulation model for accidental water pollution discharge of mine tailings ponds at watershed-scale. J Environ Manage 246:821–831

Lotter NO (2011) Modern process mineralogy: an integrated multi-disciplined approach to flowsheeting. Miner Eng 24:1229–1237

Mahieux PY, Aubert JE, Cyr M, Coutand M, Husson B (2010) Quantitative mineralogical composition of complex mineral wastes – contribution of the rietveld method. Waste Manage 30:378–388

Ospina-Correa JD, Mejía-Restrepo E, Serna-Zuluaga CM, Posada-Montoya A, Osorio-Cachaya JG, Tamayo-Sepúlveda JA, Calderón-Gutiérrez JA (2018) Process mineralogy of refractory gold ore in thiosulfate solutions. Hydrometallurgy 182:104–113

Simonsen AMT, Solismaa S, Hansen HK, Jensen PE (2020) Evaluation of mine tailings’ potential as supplementary cementitious materials based on chemical, mineralogical and physical characteristics. Waste Manage 102:710–721

Sukmara S, Adi WA, Manaf A (2022) Mineral analysis and its extraction process of ilmenite rocks in titanium-rich cumulates from Pandeglang Banten Indonesia. J. Mater. Res. Technol. 17:3384–3393

Wang B, Xia DS, Yu Y, Chen H, Jia J (2018) Source apportionment of soil-contamination in Baotou City (North China) based on a combined magnetic and geochemical approach. Sci Total Environ 642:95–104

Xiao JH, Zhang YS (2019) Recovering Cobalt and Sulfur in Low Grade Cobalt-Bearing V-Ti Magnetite Tailings Using Flotation Process. Processes 7:536–555

Xiao JH, Zou K, Chen T, Peng Y, Ding W, Chen JH, Deng B, Li H, Wang Z (2021) Extraction of Sc from Sc-bearing V-Ti magnetite tailings. JOM 73:1836–1844

Xu CB, Zhang YM, Liu T, Huang J (2017) Characterization and pre-concentration of low-grade vanadium-titanium magnetite ore. Minerals 7:137–146

Xu CL, Zhong CB, Lyu RL, Ruan YY, Zhang ZY, Chi RA (2019) Process mineralogy of Weishan rare earth ore by MLA. J Rare Earths 37:334–338

Xu W, Shi B, Tian Y, Chen Y, Li SQ, Cheng Q, Mei GJ (2021) Process mineralogy characteristics and flotation application of a refractory collophanite from Guizhou, China. Minerals 11:1249–1268

Yu XM, Li YM, Zhang C, Liu HY, Liu J, Zheng WW, Kang X, Leng XJ, Zhao K, Gu YF, Zhang XP, Xiang QJ, Chen Q (2014) Culturable heavy metal-resistant and plant growth promoting bacteria in V-Ti magnetite mine tailing soil from Panzhihua. China Plos One 9:e106618

Yu JW, Hu N, Xiao HX, Gao P, Sun YS (2021) Reduction behaviors of vanadium-titanium magnetite with H 2 via a fluidized bed. Powder Technol 385:83–91

Zhai JH, Chen P, Sun W, Chen W, Wan S (2020) A review of mineral processing of ilmenite by flotation. Miner Eng 157:106558

Zhang T, He YQ, Wang FF, Ge LH, Zhu XN, Li H (2014) Chemical and process mineralogical characterizations of spent lithium-ion batteries: an approach by multi-analytical techniques. Waste Manage 34:1051–1058

Zhu XY, Sun N, Huang Y, Zhu YG, Wang WQ (2021) Preparation of full tailings-based foam ceramics and auxiliary foaming effect of vanadium-titanium magnetite tailings. J Non-Cryst Solids 571:121063

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This work was supported by the National Natural Science Foundation of China (grant number 51674084); the National Natural Science Foundation of China—Liaoning United Foundation (grant number U1908226); and the National Key R&D Program of China (No. 2017YFB0603801).

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Department of Resource and Environment, School of Metallurgy, Northeastern University, Shenyang, 110819, People’s Republic of China

Jinsheng Liu, Zhenxing Xing, Jianxing Liu, Xueyong Ding & Xiangxin Xue

Liaoning Key Laboratory of Recycling Science for Metallurgical Resources, Shenyang, 110819, People’s Republic of China

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Jinsheng Liu: investigation, writing (original draft), data collection and validation. Zhenxing Xing: data analysis. Jianxing Liu: resources and methodology. Xueyong Ding: project administration and supervision. Xiangxin Xue: conceptualization, project administration, and supervision. Both authors read and approved the final manuscript.

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Liu, J., Xing, Z., Liu, J. et al. Evaluation of the potential of recovering various valuable elements from a vanadiferous titanomagnetite tailing based on chemical and process mineralogical characterization. Environ Sci Pollut Res 30 , 83991–84001 (2023). https://doi.org/10.1007/s11356-023-27897-z

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Received : 23 September 2022

Accepted : 21 May 2023

Published : 23 June 2023

Issue Date : July 2023

DOI : https://doi.org/10.1007/s11356-023-27897-z

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