literature review vs empirical study

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• February 19, 2022
• Posted by: Chimnecherem Eke
• Category: Academic Writing Guide

Differences between Empirical Review and Literature Review

As a researcher, you might wonder about the difference between an Empirical Review and Literature Review. If you are going to write an essay or article, you must first figure out what your topic is. The subject determines the flow of the writing, the material provided, and every other element.

Content Outline

Get your seat closer; let’s get to what you need to know about Empirical Review and Literature Review and their notable differences.

Students often accost empirical studies and literature reviews while preparing a research paper . It’s critical to know the difference between the two in order to craft a solid piece of writing. Both articles are responsible for presenting the facts, but their strategies differ.

Students are often perplexed when asked what empirical research is. The following is an explanation of the differences between a systematic review and a literature review:

Definition of Empirical review -Empirical Review and Literature Review

An empirical literature review, also known as a systematic literature review, analyzes previous empirical studies in order to provide an answer to a specific research topic.

Rather than drawing information from theories or beliefs, empirical research relies on observations and measurements to arrive at conclusions. To address specific research inquiries, it could involve making a list of people, behaviours, or events that are being researched.

In some cases, reviews of studies that involve experiments are used in empirical reviews to generate findings based on experience that may be seen directly or indirectly. Most of the time, the analysis entails quantifying the data and drawing conclusions.

The goal is to provide data that can be quantified using established scientific methods . Research reviewers thoroughly examine all findings of other authors before drawing any conclusions in an essay or paper.

As a result of carefully planned and monitored observations, the experiment is carried out, and the resulting conclusion is rigorously monitored. In contrast to other types of literature reviews, the focus here is on the most recent results of the experimental studies as it is now being conducted. A hypothesis may also be a forecast of a previously presented theory based on prior material.

Empirical evidence refers to data gathered via testing or observation in this context. These data are collected and analyzed by scientists.

For example, an empirical review could involve reviewing the study of another researcher on a group of listeners exposed to upbeat music or a work on learning and improvisation that examines other studies on work that theorizes about the educational value of improvisational principles and practices, such as Viola Spolin and Keith Johnstone ‘s writings in which they present their beliefs, impressions, ideas, and theories about those.

Defining Literature Review -Empirical Review and Literature Review

The literature review , instead of an empirical review, necessitates reading several related studies. Other theoretical sources are used to compile the facts and information included in this piece. The accumulation of all literary works may lead to new deductions. Information and hypotheses, on the other hand, have already been developed.

To generate cohesive findings, a literature review compiles all necessary data. No new theories can be developed since no experimental work can be done.

There is an important function for the literature review in uncovering defining, and clarifying key ideas that will be utilized throughout the empirical parts of the paper argues.

A well-written review article may shed light on the current state of knowledge, explain apparent inconsistencies, pinpoint areas in need of more study, and even help to forge a consensus where none previously existed. A well-written review may also assist you in your professional life. Reviews aid in recognition and advancement due to their high citation frequency.

Selecting the type of review to conduct -Empirical Review and Literature Review

College students are often required to write several papers as part of their studies. When a student does a literature review, he or she attempts to use the written word to support or refute an idea or hypothesis. He or she may test a theory or try to find an answer to a specific issue based on already-known information.

An empirical review is a piece of writing based on a study that was done purely for the purpose of publishing it. Calibrated instruments are used to conduct the experiment in a scientifically controlled way.

In this age of AI, research has become more interesting as you can use AI for your research study to make reviewing literature easier (Thesis, Dissertation, Research paper, among others). AI tools like SciSpace literature review can help you compare, contrast, and analyze research papers more efficiently.

It would be best to start writing as soon as you finish the experiment. During the study, observations should be recorded methodically. This aids in developing coherence, which is more easily understood even in the future. Additionally, starting the writing process early allows for more time for revision and results in higher-quality work.

Because experiments may take some time to produce the desired results, this is especially important for empirical studies. Leaving the writing to the last minute and beginning it when the deadline is nearing will just add to the stress and complexity of the process. This interferes with the job and lowers the quality. As a result, staying on top of your job helps your paper and your personal life grow.

Almost every research article includes a review of the literature. An empirical study must first be established inside an accepted theory before they can publish their findings. In other words, before we get into our methodology and research questions, we’ll go through what’s been done previously and how the variables we want to investigate fit into the theories and frameworks of our research field.

An empirical literature review, also known as a systematic literature review, analyzes previous empirical studies in order to provide an answer to a specific research topic. Randomized controlled trials are the most common kind of empirical study.

Both of these tasks are similar in that they need to review previous work on the topic. The empirical literature review, on the other hand, seeks to address a particular empirical issue by analyzing data. The theoretical literature review serves primarily to place your research within a broader framework. A theoretical review will be included in systematic empirical reviews to help researchers understand why a specific research topic is worth investigating.

Thank you for your time, we hope you got value reading Differences between Empirical Review and Literature Review?

Frequently Asked Questions

What is an example of an empirical review.

An empirical review is an academic review that focuses on summarizing and analyzing the findings of empirical research studies. Empirical research involves collecting and analysing primary data through experiments, surveys, observations, or any other data collection method(s).

An example of an empirical review would be a critical examination of several recent scientific studies that have investigated the effectiveness of a specific drug in treating a particular medical condition. The empirical review would include the study objectives, a summary of the research methods, key findings, and a critical evaluation of the research design, data collection, and statistical analysis methods employed in those studies.

What is the purpose of a literature review in empirical research studies?

A literature review in empirical research studies provides a comprehensive and systematic overview of existing published research and scholarly works related to the topic under investigation. This serves several important purposes:

Contextualization : It helps researchers place their study within the context of existing knowledge and identify gaps or areas where further research is needed.

Identification of Theoretical Frameworks : A literature review aids in identifying relevant theories and concepts that can inform the research design and data analysis in empirical studies.

Methodological Guidance: It offers insights into prior studies’ methodologies and research methods, helping researchers make informed decisions about their own data collection and analysis techniques.

Hypothesis Development: It can assist in formulating research hypotheses and research questions based on the existing literature.

Validation of Research Question: By reviewing the literature, researchers can ensure that their research questions or hypotheses have not been adequately addressed in prior studies.

Identification of Variables: It helps identify key variables and factors examined in previous research, which can guide the selection of variables in the new study.

In summary, a literature review in empirical research serves as the foundation upon which a new empirical study is built, helping researchers gain a deep understanding of the existing knowledge in their field, refine their research questions, and make informed decisions about the research design and methods they will employ.

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Empirical Study vs. Literature Review

When writing an essay or an article, it is imperative to identify its theme. The flow of the written content, the information that is presented and all other aspects are based on the topic. When writing the research paper, students often accost empirical studies and literature review. To write a legit paper, it is essential to understand the difference between the two concepts. Both papers account for showing the facts, however, they vary in the approach. If there are difficulties with writing essays, a student can always turn to  write my essays services. They will help you write a paper on almost any subject and on any topic. A question what is empirical research often confuses those who learn. Systematic review vs literature review description is shown below:

1. Legit meaning of empirical research

Empirical research derives conclusions based on experience, which can be directly visualized or indirectly observed with the help of experiments. Normally, the analysis is performed by quantifying the results and the inference is drawn. The idea is to present the findings that are quantifiably observable through calibrated scientific applications. Paper review writers investigate every result before writing any conclusion in a paper or an essay. The experiment is carried out under a planned or controlled observation and the inference is strictly monitored. In contrast to the existing literature, emphasis is on the current findings of the experiment under operation. The previous literature is used to make a hypothesis, which can also be a prediction of a previously proposed theory.

2. How to work on the writing

The results are recorded, and the conclusions are drawn from the same. It is a frequent occurrence that science students are not adept for writing scientific papers. There are multiple services available online that help students with their assignments. The learners can easily avail the services online and get the paper written in accordance with their specific requirements. Services like Edusson or EssayServiceScanner are genuine ones. Students need to login and specify their requirements so that an expert assist with developing the paper. However, before paying a writer, it is better to go through the reviews about the platform. Ensure that it is not a scam. It is also recommended to review the article before you make any submissions.

3. Literature review

In contrast to the empirical study, it is essential to review different researches while working on the literature review. Such an article contains facts and information derived from other theoretical sources. New inferences may be drawn from the compilation of all literature pieces. However, the information and hypothesis are already existent. The literature review is a compilation of all the relevant data in order to produce coherent results. No experimental work may be conducted and the results come out of already existing theories in a systematic manner.

4. Deciding the type of article

College students often have to write different papers. A literature review is when a student attempts to prove or disprove a conceived point or an analysis through literature. By means of existing facts, he or she can challenge a hypothesis or attempt to answer a particular question. The article based on an experiment that was conducted solely to write the paper is an empirical review . The experiment is carried out by means of calibrated instruments in a controlled manner.

It is better to begin writing while conducting the experiment. Observations should be written down systematically during the research. This helps bring out coherence, which is easier to comprehend even later. Furthermore, beginning the writing process earlier provides one with enough time for the review and also produces better work. This is particularly necessary for the empirical papers as the experiments may take time to deliver the desired results. Leaving the writing behind and starting to write it when the deadline is approaching may make the process stressful enough and may lead to confusion. This also hampers with the work leading to decreased levels of quality. Hence, working in a timely way helps in developing the paper and your life on the whole.

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Identifying and Locating Empirical Articles

• Types of Scholarly Articles

Empirical Articles vs Review Articles

• Locating Empirical Articles

Empirical articles are written to share the results of original research. Their authors will share their findings, including results, data, and ideas for future research. This will allow other researchers to learn more and conduct further studies.

Review articles are written to compare and discuss the results of multiple articles. They may be structured similarly to original research articles, but they are synthesizing what others have written about, rather than reporting on their own research.

Watch the video below (~1:30 min) for more information.

• << Previous: Types of Scholarly Articles
• Next: Locating Empirical Articles >>
• Last Updated: Sep 29, 2023 2:51 PM
• URL: https://libguides.ucmerced.edu/identifying-locating-empirical-articles

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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

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

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A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy pp 1–15 Cite as

Methodological Approaches to Literature Review

• Dennis Thomas 2 ,
• Elida Zairina 3 &
• Johnson George 4  
• Living reference work entry
• First Online: 09 May 2023

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The literature review can serve various functions in the contexts of education and research. It aids in identifying knowledge gaps, informing research methodology, and developing a theoretical framework during the planning stages of a research study or project, as well as reporting of review findings in the context of the existing literature. This chapter discusses the methodological approaches to conducting a literature review and offers an overview of different types of reviews. There are various types of reviews, including narrative reviews, scoping reviews, and systematic reviews with reporting strategies such as meta-analysis and meta-synthesis. Review authors should consider the scope of the literature review when selecting a type and method. Being focused is essential for a successful review; however, this must be balanced against the relevance of the review to a broad audience.

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Thomas, D., Zairina, E., George, J. (2023). Methodological Approaches to Literature Review. In: Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. Springer, Cham. https://doi.org/10.1007/978-3-030-50247-8_57-1

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SWRK 330 - Social Work Research Methods

• Literature Reviews and Empirical Research
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What is a Literature Review?

Empirical research.

• Annotated Bibliographies

A literature review  summarizes and discusses previous publications  on a topic.

It should also:

explore past research and its strengths and weaknesses.

be used to validate the target and methods you have chosen for your proposed research.

consist of books and scholarly journals that provide research examples of populations or settings similar to your own, as well as community resources to document the need for your proposed research.

The literature review does not present new  primary  scholarship. 

be completed in the correct citation format requested by your professor  (see the  C itations Tab)

Access Purdue  OWL's Social Work Literature Review Guidelines here .  

Empirical Research  is  research  that is based on experimentation or observation, i.e. Evidence. Such  research  is often conducted to answer a specific question or to test a hypothesis (educated guess).

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

These are some key features to look for when identifying empirical research.

NOTE:  Not all of these features will be in every empirical research article, some may be excluded, use this only as a guide.

• Statement of methodology
• Research questions are clear and measurable
• Individuals, group, subjects which are being studied are identified/defined
• Data is presented regarding the findings
• Controls or instruments such as surveys or tests were conducted
• There is a literature review
• There is discussion of the results included
• Citations/references are included

See also Empirical Research Guide

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Know the difference between empirical and review articles.

Empirical article An empirical (research) article reports methods and findings of an original research study conducted by the authors of the article.  

Literature Review article A review article or "literature review" discusses past research studies on a given topic.

Definition of an empirical study:  An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research.

Parts of a standard empirical research article:  (articles will not necessary use the exact terms listed below.)

• Abstract  ... A paragraph length description of what the study includes.
• Introduction ...Includes a statement of the hypotheses for the research and a review of other research on the topic.
• Who are participants
• Design of the study
• What the participants did
• What measures were used
• Results ...Describes the outcomes of the measures of the study.
• Discussion ...Contains the interpretations and implications of the study.
• References ...Contains citation information on the material cited in the report. (also called bibliography or works cited)

Characteristics of an Empirical Article:

• Empirical articles will include charts, graphs, or statistical analysis.
• Empirical research articles are usually substantial, maybe from 8-30 pages long.
• There is always a bibliography found at the end of the article.

Type of publications that publish empirical studies:

• Empirical research articles are published in scholarly or academic journals
• These journals are also called “peer-reviewed,” or “refereed” publications.

Examples of such publications include:

• Computers in Human Behavior
• Journal of Educational Psychology

Examples of databases that contain empirical research:  (selected list only)

• Web of Science

This page is adapted from the Sociology Research Guide: Identify Empirical Articles page at Cal State Fullerton Pollak Library.

Know the difference between scholarly and non-scholarly articles.

"Scholarly" journal = "Peer-Reviewed" journal = "Refereed" journal

When researching your topic, you may come across many different types of sources and articles. When evaluating these sources, it is important to think about: 

• Who is the author? 
• Who is the audience or why was this written? 
• Where was this published? 
• Is this relevant to your research? 
• When was this written? Has it been updated? 
• Are there any citations? Who do they cite?  

Helpful Links and Guides

Here are helpful links and guides to check out for more information on scholarly sources: 

• This database contains data on different types of serials and can be used to determine whether a periodical is peer-reviewed or not:  Ulrich's Periodicals Directory  
• The UC Berkeley Library published this useful guide on evaluating resources, including the differences between scholarly and popular sources, as well as how to find primary sources:  UC Berkeley's Evaluating Resources LibGuide
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Empirical Journal Articles

Empirical Article - (Original Research) Based on experience and observation, rather than systematic logic. (according to MedicineNet.com)

The articles contain original research (such as scientific experiments, surveys and research studies) A list of references or sources is provided at the end of each article An editorial board, composed of experts in the field, reviews articles to decide whether they should be accepted; this is also known as "refereed," "peer-reviewed," "professional," "scholarly", or "academic". Uses a specialized vocabulary for that field.

Below are two websites that explain Empirical articles and research:

Empirical Research: How to Recognize and Locate (Penn State University) - Empirical Research PDF

Review Journal Articles

Review Article

An article that summarizes the progress or current state in some particular subject, area, or topic.

How to write a "Review Article?" (National Library of Medicine) - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4548566/

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Empirical Articles

Review Articles

What is a Peer Reviewed Article?

Peer review is a process that many, but not all, journals use. Article manuscripts submitted to peer-reviewed journals are not automatically accepted and published.

In peer review, a panel of experts in the given field review the manuscript to determine aspects such as the quality of research, appropriateness for the journal, and relevance to the field. One of three decisions is made: accept, reject, or revise based on commentary from reviewers.

The process of peer review is thought to help ensure that high quality articles appear in journals.

Another term for peer-reviewed is  refereed . Peer-reviewed journals may also be called  scholarly.

Remember that magazines, Internet sources, and books are not the same as peer-reviewed journals.

In psychology, articles that report on original/new research studies may be referred to as primary sources or empirical .

• Common sections in a research/empirical article include introduction, literature review, methods/process, data, results, discussion, conclusion / suggestions for further study, and references.
• If the article is not divided into sections, it does not automatically mean it is not an empirical article.
• You cannot assume that an article is empirical just because it is divided into sections.
• In the methods section [which may be called something similar], or otherwise usually toward the beginning or middle of the article [if it does not have sections]: The authors will describe how they actively conducted new or original research -- such as an experiment or survey. Examples of what would likely be explained: How they identified participants, that they received Institutional Review Board (IRB) approval, control vs. experimental groups, and so forth about their research.

Articles that either interpret or analyze empirical articles are considered  review articles.  Such articles are often referred to as  secondary  sources  or  secondary research.

• An entire article that is purely a literature review [usually a review of select other articles considered to be the best support for a research question/topic]
• Systematic review
• Meta-analysis / meta-analyses
• Meta-synthesis / meta-syntheses
• If an article is a review article, it is likely [but not always] to have the words literature review, systematic review, integrative review, meta-analysis, meta-synthesis, or other mention of review in the title.
• The main indicator of a review article is if authors are just interpreting, analyzing, and/or comparing the results of empirical articles. So, in comparison to an empirical article, the authors of a review article do not describe an experiment or survey they conducted.
• If there is a methods section, it will usually describe how the authors searched for other articles [which databases they searched, what search terms they used] and decided the criteria for articles to include and exclude as part of their review. Again, they will not be describing how they conducted new or original research, such as an experiment or survey.
• In a literature review, for example, the authors' might point out what they believe to be the most pertinent/applicable research articles.
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Conducting a Literature Review: Types of Literature

• Introduction
• 1. Choose Your Topic

Types of Literature

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• 4. Read & Analyze the Literature
• 5. Write the Review
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Different types of publications have different characteristics.

Primary Literature Primary sources means original studies, based on direct observation, use of statistical records, interviews, or experimental methods, of actual practices or the actual impact of practices or policies. They are authored by researchers, contains original research data, and are usually published in a peer-reviewed journal. Primary literature may also include conference papers, pre-prints, or preliminary reports. Also called empirical research .

Secondary Literature Secondary literature consists of interpretations and evaluations that are derived from or refer to the primary source literature. Examples include review articles (such as meta-analysis and systematic reviews) and reference works. Professionals within each discipline take the primary literature and synthesize, generalize, and integrate new research.

Tertiary Literature Tertiary literature consists of a distillation and collection of primary and secondary sources such as textbooks, encyclopedia articles, and guidebooks or handbooks. The purpose of tertiary literature is to provide an overview of key research findings and an introduction to principles and practices within the discipline.

Adapted from the Information Services Department of the Library of the Health Sciences-Chicago , University of Illinois at Chicago.

Types of Scientific Publications

These examples and descriptions of publication types will give you an idea of how to use various works and why you would want to write a particular kind of paper.

• Scholarly article aka empirical article
• Review article
• Conference paper

Scholarly (aka empirical) article -- example

Empirical studies use data derived from observation or experiment. Original research papers (also called primary research articles) that describe empirical studies and their results are published in academic journals.  Articles that report empirical research contain different sections which relate to the steps of the scientific method.

      Abstract - The abstract provides a very brief summary of the research.

     Introduction - The introduction sets the research in a context, which provides a review of related research and develops the hypotheses for the research.

     Method - The method section describes how the research was conducted.

     Results - The results section describes the outcomes of the study.

     Discussion - The discussion section contains the interpretations and implications of the study.

     References - A references section lists the articles, books, and other material cited in the report.

Review article -- example

A review article summarizes a particular field of study and places the recent research in context. It provides an overview and is an excellent introduction to a subject area. The references used in a review article are helpful as they lead to more in-depth research.

Many databases have limits or filters to search for review articles. You can also search by keywords like review article, survey, overview, summary, etc.

Conference proceedings, abstracts and reports -- example

Conference proceedings, abstracts and reports are not usually peer-reviewed.  A conference article is similar to a scholarly article insofar as it is academic. Conference articles are published much more quickly than scholarly articles. You can find conference papers in many of the same places as scholarly articles.

How Do You Identify Empirical Articles?

To identify an article based on empirical research, look for the following characteristics:

     The article is published in a peer-reviewed journal .

     The article includes charts, graphs, or statistical analysis .

     The article is substantial in size , likely to be more than 5 pages long.

     The article contains the following parts (the exact terms may vary): abstract, introduction, method, results, discussion, references .

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Module 2 Chapter 3: What is Empirical Literature & Where can it be Found?

In Module 1, you read about the problem of pseudoscience. Here, we revisit the issue in addressing how to locate and assess scientific or empirical literature . In this chapter you will read about:

• distinguishing between what IS and IS NOT empirical literature
• how and where to locate empirical literature for understanding diverse populations, social work problems, and social phenomena.

Probably the most important take-home lesson from this chapter is that one source is not sufficient to being well-informed on a topic. It is important to locate multiple sources of information and to critically appraise the points of convergence and divergence in the information acquired from different sources. This is especially true in emerging and poorly understood topics, as well as in answering complex questions.

What Is Empirical Literature

Social workers often need to locate valid, reliable information concerning the dimensions of a population group or subgroup, a social work problem, or social phenomenon. They might also seek information about the way specific problems or resources are distributed among the populations encountered in professional practice. Or, social workers might be interested in finding out about the way that certain people experience an event or phenomenon. Empirical literature resources may provide answers to many of these types of social work questions. In addition, resources containing data regarding social indicators may also prove helpful. Social indicators are the “facts and figures” statistics that describe the social, economic, and psychological factors that have an impact on the well-being of a community or other population group.The United Nations (UN) and the World Health Organization (WHO) are examples of organizations that monitor social indicators at a global level: dimensions of population trends (size, composition, growth/loss), health status (physical, mental, behavioral, life expectancy, maternal and infant mortality, fertility/child-bearing, and diseases like HIV/AIDS), housing and quality of sanitation (water supply, waste disposal), education and literacy, and work/income/unemployment/economics, for example.

Three characteristics stand out in empirical literature compared to other types of information available on a topic of interest: systematic observation and methodology, objectivity, and transparency/replicability/reproducibility. Let’s look a little more closely at these three features.

Systematic Observation and Methodology. The hallmark of empiricism is “repeated or reinforced observation of the facts or phenomena” (Holosko, 2006, p. 6). In empirical literature, established research methodologies and procedures are systematically applied to answer the questions of interest.

Objectivity. Gathering “facts,” whatever they may be, drives the search for empirical evidence (Holosko, 2006). Authors of empirical literature are expected to report the facts as observed, whether or not these facts support the investigators’ original hypotheses. Research integrity demands that the information be provided in an objective manner, reducing sources of investigator bias to the greatest possible extent.

Transparency and Replicability/Reproducibility.   Empirical literature is reported in such a manner that other investigators understand precisely what was done and what was found in a particular research study—to the extent that they could replicate the study to determine whether the findings are reproduced when repeated. The outcomes of an original and replication study may differ, but a reader could easily interpret the methods and procedures leading to each study’s findings.

What is NOT Empirical Literature

By now, it is probably obvious to you that literature based on “evidence” that is not developed in a systematic, objective, transparent manner is not empirical literature. On one hand, non-empirical types of professional literature may have great significance to social workers. For example, social work scholars may produce articles that are clearly identified as describing a new intervention or program without evaluative evidence, critiquing a policy or practice, or offering a tentative, untested theory about a phenomenon. These resources are useful in educating ourselves about possible issues or concerns. But, even if they are informed by evidence, they are not empirical literature. Here is a list of several sources of information that do not meet the standard of being called empirical literature:

• your course instructor’s lectures
• political statements
• advertisements
• newspapers & magazines (journalism)
• television news reports & analyses (journalism)
• many websites, Facebook postings, Twitter tweets, and blog postings
• the introductory literature review in an empirical article

You may be surprised to see the last two included in this list. Like the other sources of information listed, these sources also might lead you to look for evidence. But, they are not themselves sources of evidence. They may summarize existing evidence, but in the process of summarizing (like your instructor’s lectures), information is transformed, modified, reduced, condensed, and otherwise manipulated in such a manner that you may not see the entire, objective story. These are called secondary sources, as opposed to the original, primary source of evidence. In relying solely on secondary sources, you sacrifice your own critical appraisal and thinking about the original work—you are “buying” someone else’s interpretation and opinion about the original work, rather than developing your own interpretation and opinion. What if they got it wrong? How would you know if you did not examine the primary source for yourself? Consider the following as an example of “getting it wrong” being perpetuated.

Example: Bullying and School Shootings . One result of the heavily publicized April 1999 school shooting incident at Columbine High School (Colorado), was a heavy emphasis placed on bullying as a causal factor in these incidents (Mears, Moon, & Thielo, 2017), “creating a powerful master narrative about school shootings” (Raitanen, Sandberg, & Oksanen, 2017, p. 3). Naturally, with an identified cause, a great deal of effort was devoted to anti-bullying campaigns and interventions for enhancing resilience among youth who experience bullying.  However important these strategies might be for promoting positive mental health, preventing poor mental health, and possibly preventing suicide among school-aged children and youth, it is a mistaken belief that this can prevent school shootings (Mears, Moon, & Thielo, 2017). Many times the accounts of the perpetrators having been bullied come from potentially inaccurate third-party accounts, rather than the perpetrators themselves; bullying was not involved in all instances of school shooting; a perpetrator’s perception of being bullied/persecuted are not necessarily accurate; many who experience severe bullying do not perpetrate these incidents; bullies are the least targeted shooting victims; perpetrators of the shooting incidents were often bullying others; and, bullying is only one of many important factors associated with perpetrating such an incident (Ioannou, Hammond, & Simpson, 2015; Mears, Moon, & Thielo, 2017; Newman &Fox, 2009; Raitanen, Sandberg, & Oksanen, 2017). While mass media reports deliver bullying as a means of explaining the inexplicable, the reality is not so simple: “The connection between bullying and school shootings is elusive” (Langman, 2014), and “the relationship between bullying and school shooting is, at best, tenuous” (Mears, Moon, & Thielo, 2017, p. 940). The point is, when a narrative becomes this publicly accepted, it is difficult to sort out truth and reality without going back to original sources of information and evidence.

What May or May Not Be Empirical Literature: Literature Reviews

Investigators typically engage in a review of existing literature as they develop their own research studies. The review informs them about where knowledge gaps exist, methods previously employed by other scholars, limitations of prior work, and previous scholars’ recommendations for directing future research. These reviews may appear as a published article, without new study data being reported (see Fields, Anderson, & Dabelko-Schoeny, 2014 for example). Or, the literature review may appear in the introduction to their own empirical study report. These literature reviews are not considered to be empirical evidence sources themselves, although they may be based on empirical evidence sources. One reason is that the authors of a literature review may or may not have engaged in a systematic search process, identifying a full, rich, multi-sided pool of evidence reports.

There is, however, a type of review that applies systematic methods and is, therefore, considered to be more strongly rooted in evidence: the systematic review .

Systematic review of literature. A systematic reviewis a type of literature report where established methods have been systematically applied, objectively, in locating and synthesizing a body of literature. The systematic review report is characterized by a great deal of transparency about the methods used and the decisions made in the review process, and are replicable. Thus, it meets the criteria for empirical literature: systematic observation and methodology, objectivity, and transparency/reproducibility. We will work a great deal more with systematic reviews in the second course, SWK 3402, since they are important tools for understanding interventions. They are somewhat less common, but not unheard of, in helping us understand diverse populations, social work problems, and social phenomena.

Locating Empirical Evidence

Social workers have available a wide array of tools and resources for locating empirical evidence in the literature. These can be organized into four general categories.

Journal Articles. A number of professional journals publish articles where investigators report on the results of their empirical studies. However, it is important to know how to distinguish between empirical and non-empirical manuscripts in these journals. A key indicator, though not the only one, involves a peer review process . Many professional journals require that manuscripts undergo a process of peer review before they are accepted for publication. This means that the authors’ work is shared with scholars who provide feedback to the journal editor as to the quality of the submitted manuscript. The editor then makes a decision based on the reviewers’ feedback:

• Accept as is
• Accept with minor revisions
• Request that a revision be resubmitted (no assurance of acceptance)

When a “revise and resubmit” decision is made, the piece will go back through the review process to determine if it is now acceptable for publication and that all of the reviewers’ concerns have been adequately addressed. Editors may also reject a manuscript because it is a poor fit for the journal, based on its mission and audience, rather than sending it for review consideration.

Indicators of journal relevance. Various journals are not equally relevant to every type of question being asked of the literature. Journals may overlap to a great extent in terms of the topics they might cover; in other words, a topic might appear in multiple different journals, depending on how the topic was being addressed. For example, articles that might help answer a question about the relationship between community poverty and violence exposure might appear in several different journals, some with a focus on poverty, others with a focus on violence, and still others on community development or public health. Journal titles are sometimes a good starting point but may not give a broad enough picture of what they cover in their contents.

In focusing a literature search, it also helps to review a journal’s mission and target audience. For example, at least four different journals focus specifically on poverty:

• Journal of Children & Poverty
• Journal of Poverty
• Journal of Poverty and Social Justice
• Poverty & Public Policy

Let’s look at an example using the Journal of Poverty and Social Justice . Information about this journal is located on the journal’s webpage: http://policy.bristoluniversitypress.co.uk/journals/journal-of-poverty-and-social-justice . In the section headed “About the Journal” you can see that it is an internationally focused research journal, and that it addresses social justice issues in addition to poverty alone. The research articles are peer-reviewed (there appear to be non-empirical discussions published, as well). These descriptions about a journal are almost always available, sometimes listed as “scope” or “mission.” These descriptions also indicate the sponsorship of the journal—sponsorship may be institutional (a particular university or agency, such as Smith College Studies in Social Work ), a professional organization, such as the Council on Social Work Education (CSWE) or the National Association of Social Work (NASW), or a publishing company (e.g., Taylor & Frances, Wiley, or Sage).

Indicators of journal caliber.  Despite engaging in a peer review process, not all journals are equally rigorous. Some journals have very high rejection rates, meaning that many submitted manuscripts are rejected; others have fairly high acceptance rates, meaning that relatively few manuscripts are rejected. This is not necessarily the best indicator of quality, however, since newer journals may not be sufficiently familiar to authors with high quality manuscripts and some journals are very specific in terms of what they publish. Another index that is sometimes used is the journal’s impact factor . Impact factor is a quantitative number indicative of how often articles published in the journal are cited in the reference list of other journal articles—the statistic is calculated as the number of times on average each article published in a particular year were cited divided by the number of articles published (the number that could be cited). For example, the impact factor for the Journal of Poverty and Social Justice in our list above was 0.70 in 2017, and for the Journal of Poverty was 0.30. These are relatively low figures compared to a journal like the New England Journal of Medicine with an impact factor of 59.56! This means that articles published in that journal were, on average, cited more than 59 times in the next year or two.

Impact factors are not necessarily the best indicator of caliber, however, since many strong journals are geared toward practitioners rather than scholars, so they are less likely to be cited by other scholars but may have a large impact on a large readership. This may be the case for a journal like the one titled Social Work, the official journal of the National Association of Social Workers. It is distributed free to all members: over 120,000 practitioners, educators, and students of social work world-wide. The journal has a recent impact factor of.790. The journals with social work relevant content have impact factors in the range of 1.0 to 3.0 according to Scimago Journal & Country Rank (SJR), particularly when they are interdisciplinary journals (for example, Child Development , Journal of Marriage and Family , Child Abuse and Neglect , Child Maltreatmen t, Social Service Review , and British Journal of Social Work ). Once upon a time, a reader could locate different indexes comparing the “quality” of social work-related journals. However, the concept of “quality” is difficult to systematically define. These indexes have mostly been replaced by impact ratings, which are not necessarily the best, most robust indicators on which to rely in assessing journal quality. For example, new journals addressing cutting edge topics have not been around long enough to have been evaluated using this particular tool, and it takes a few years for articles to begin to be cited in other, later publications.

Beware of pseudo-, illegitimate, misleading, deceptive, and suspicious journals . Another side effect of living in the Age of Information is that almost anyone can circulate almost anything and call it whatever they wish. This goes for “journal” publications, as well. With the advent of open-access publishing in recent years (electronic resources available without subscription), we have seen an explosion of what are called predatory or junk journals . These are publications calling themselves journals, often with titles very similar to legitimate publications and often with fake editorial boards. These “publications” lack the integrity of legitimate journals. This caution is reminiscent of the discussions earlier in the course about pseudoscience and “snake oil” sales. The predatory nature of many apparent information dissemination outlets has to do with how scientists and scholars may be fooled into submitting their work, often paying to have their work peer-reviewed and published. There exists a “thriving black-market economy of publishing scams,” and at least two “journal blacklists” exist to help identify and avoid these scam journals (Anderson, 2017).

This issue is important to information consumers, because it creates a challenge in terms of identifying legitimate sources and publications. The challenge is particularly important to address when information from on-line, open-access journals is being considered. Open-access is not necessarily a poor choice—legitimate scientists may pay sizeable fees to legitimate publishers to make their work freely available and accessible as open-access resources. On-line access is also not necessarily a poor choice—legitimate publishers often make articles available on-line to provide timely access to the content, especially when publishing the article in hard copy will be delayed by months or even a year or more. On the other hand, stating that a journal engages in a peer-review process is no guarantee of quality—this claim may or may not be truthful. Pseudo- and junk journals may engage in some quality control practices, but may lack attention to important quality control processes, such as managing conflict of interest, reviewing content for objectivity or quality of the research conducted, or otherwise failing to adhere to industry standards (Laine & Winker, 2017).

One resource designed to assist with the process of deciphering legitimacy is the Directory of Open Access Journals (DOAJ). The DOAJ is not a comprehensive listing of all possible legitimate open-access journals, and does not guarantee quality, but it does help identify legitimate sources of information that are openly accessible and meet basic legitimacy criteria. It also is about open-access journals, not the many journals published in hard copy.

An additional caution: Search for article corrections. Despite all of the careful manuscript review and editing, sometimes an error appears in a published article. Most journals have a practice of publishing corrections in future issues. When you locate an article, it is helpful to also search for updates. Here is an example where data presented in an article’s original tables were erroneous, and a correction appeared in a later issue.

• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 12(8): e0181722. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558917/
• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2018).Correction—A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 13(3): e0193937.  http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193937

Search Tools. In this age of information, it is all too easy to find items—the problem lies in sifting, sorting, and managing the vast numbers of items that can be found. For example, a simple Google® search for the topic “community poverty and violence” resulted in about 15,600,000 results! As a means of simplifying the process of searching for journal articles on a specific topic, a variety of helpful tools have emerged. One type of search tool has previously applied a filtering process for you: abstracting and indexing databases . These resources provide the user with the results of a search to which records have already passed through one or more filters. For example, PsycINFO is managed by the American Psychological Association and is devoted to peer-reviewed literature in behavioral science. It contains almost 4.5 million records and is growing every month. However, it may not be available to users who are not affiliated with a university library. Conducting a basic search for our topic of “community poverty and violence” in PsychINFO returned 1,119 articles. Still a large number, but far more manageable. Additional filters can be applied, such as limiting the range in publication dates, selecting only peer reviewed items, limiting the language of the published piece (English only, for example), and specified types of documents (either chapters, dissertations, or journal articles only, for example). Adding the filters for English, peer-reviewed journal articles published between 2010 and 2017 resulted in 346 documents being identified.

Just as was the case with journals, not all abstracting and indexing databases are equivalent. There may be overlap between them, but none is guaranteed to identify all relevant pieces of literature. Here are some examples to consider, depending on the nature of the questions asked of the literature:

• Academic Search Complete—multidisciplinary index of 9,300 peer-reviewed journals
• AgeLine—multidisciplinary index of aging-related content for over 600 journals
• Campbell Collaboration—systematic reviews in education, crime and justice, social welfare, international development
• Google Scholar—broad search tool for scholarly literature across many disciplines
• MEDLINE/ PubMed—National Library of medicine, access to over 15 million citations
• Oxford Bibliographies—annotated bibliographies, each is discipline specific (e.g., psychology, childhood studies, criminology, social work, sociology)
• PsycINFO/PsycLIT—international literature on material relevant to psychology and related disciplines
• SocINDEX—publications in sociology
• Social Sciences Abstracts—multiple disciplines
• Social Work Abstracts—many areas of social work are covered
• Web of Science—a “meta” search tool that searches other search tools, multiple disciplines

Placing our search for information about “community violence and poverty” into the Social Work Abstracts tool with no additional filters resulted in a manageable 54-item list. Finally, abstracting and indexing databases are another way to determine journal legitimacy: if a journal is indexed in a one of these systems, it is likely a legitimate journal. However, the converse is not necessarily true: if a journal is not indexed does not mean it is an illegitimate or pseudo-journal.

Government Sources. A great deal of information is gathered, analyzed, and disseminated by various governmental branches at the international, national, state, regional, county, and city level. Searching websites that end in.gov is one way to identify this type of information, often presented in articles, news briefs, and statistical reports. These government sources gather information in two ways: they fund external investigations through grants and contracts and they conduct research internally, through their own investigators. Here are some examples to consider, depending on the nature of the topic for which information is sought:

• Agency for Healthcare Research and Quality (AHRQ) at https://www.ahrq.gov/
• Bureau of Justice Statistics (BJS) at https://www.bjs.gov/
• Census Bureau at https://www.census.gov
• Morbidity and Mortality Weekly Report of the CDC (MMWR-CDC) at https://www.cdc.gov/mmwr/index.html
• Child Welfare Information Gateway at https://www.childwelfare.gov
• Children’s Bureau/Administration for Children & Families at https://www.acf.hhs.gov
• Forum on Child and Family Statistics at https://www.childstats.gov
• National Institutes of Health (NIH) at https://www.nih.gov , including (not limited to):
• National Institute on Aging (NIA at https://www.nia.nih.gov
• National Institute on Alcohol Abuse and Alcoholism (NIAAA) at https://www.niaaa.nih.gov
• National Institute of Child Health and Human Development (NICHD) at https://www.nichd.nih.gov
• National Institute on Drug Abuse (NIDA) at https://www.nida.nih.gov
• National Institute of Environmental Health Sciences at https://www.niehs.nih.gov
• National Institute of Mental Health (NIMH) at https://www.nimh.nih.gov
• National Institute on Minority Health and Health Disparities at https://www.nimhd.nih.gov
• National Institute of Justice (NIJ) at https://www.nij.gov
• Substance Abuse and Mental Health Services Administration (SAMHSA) at https://www.samhsa.gov/
• United States Agency for International Development at https://usaid.gov

Each state and many counties or cities have similar data sources and analysis reports available, such as Ohio Department of Health at https://www.odh.ohio.gov/healthstats/dataandstats.aspx and Franklin County at https://statisticalatlas.com/county/Ohio/Franklin-County/Overview . Data are available from international/global resources (e.g., United Nations and World Health Organization), as well.

Other Sources. The Health and Medicine Division (HMD) of the National Academies—previously the Institute of Medicine (IOM)—is a nonprofit institution that aims to provide government and private sector policy and other decision makers with objective analysis and advice for making informed health decisions. For example, in 2018 they produced reports on topics in substance use and mental health concerning the intersection of opioid use disorder and infectious disease,  the legal implications of emerging neurotechnologies, and a global agenda concerning the identification and prevention of violence (see http://www.nationalacademies.org/hmd/Global/Topics/Substance-Abuse-Mental-Health.aspx ). The exciting aspect of this resource is that it addresses many topics that are current concerns because they are hoping to help inform emerging policy. The caution to consider with this resource is the evidence is often still emerging, as well.

Numerous “think tank” organizations exist, each with a specific mission. For example, the Rand Corporation is a nonprofit organization offering research and analysis to address global issues since 1948. The institution’s mission is to help improve policy and decision making “to help individuals, families, and communities throughout the world be safer and more secure, healthier and more prosperous,” addressing issues of energy, education, health care, justice, the environment, international affairs, and national security (https://www.rand.org/about/history.html). And, for example, the Robert Woods Johnson Foundation is a philanthropic organization supporting research and research dissemination concerning health issues facing the United States. The foundation works to build a culture of health across systems of care (not only medical care) and communities (https://www.rwjf.org).

While many of these have a great deal of helpful evidence to share, they also may have a strong political bias. Objectivity is often lacking in what information these organizations provide: they provide evidence to support certain points of view. That is their purpose—to provide ideas on specific problems, many of which have a political component. Think tanks “are constantly researching solutions to a variety of the world’s problems, and arguing, advocating, and lobbying for policy changes at local, state, and federal levels” (quoted from https://thebestschools.org/features/most-influential-think-tanks/ ). Helpful information about what this one source identified as the 50 most influential U.S. think tanks includes identifying each think tank’s political orientation. For example, The Heritage Foundation is identified as conservative, whereas Human Rights Watch is identified as liberal.

While not the same as think tanks, many mission-driven organizations also sponsor or report on research, as well. For example, the National Association for Children of Alcoholics (NACOA) in the United States is a registered nonprofit organization. Its mission, along with other partnering organizations, private-sector groups, and federal agencies, is to promote policy and program development in research, prevention and treatment to provide information to, for, and about children of alcoholics (of all ages). Based on this mission, the organization supports knowledge development and information gathering on the topic and disseminates information that serves the needs of this population. While this is a worthwhile mission, there is no guarantee that the information meets the criteria for evidence with which we have been working. Evidence reported by think tank and mission-driven sources must be utilized with a great deal of caution and critical analysis!

In many instances an empirical report has not appeared in the published literature, but in the form of a technical or final report to the agency or program providing the funding for the research that was conducted. One such example is presented by a team of investigators funded by the National Institute of Justice to evaluate a program for training professionals to collect strong forensic evidence in instances of sexual assault (Patterson, Resko, Pierce-Weeks, & Campbell, 2014): https://www.ncjrs.gov/pdffiles1/nij/grants/247081.pdf . Investigators may serve in the capacity of consultant to agencies, programs, or institutions, and provide empirical evidence to inform activities and planning. One such example is presented by Maguire-Jack (2014) as a report to a state’s child maltreatment prevention board: https://preventionboard.wi.gov/Documents/InvestmentInPreventionPrograming_Final.pdf .

When Direct Answers to Questions Cannot Be Found. Sometimes social workers are interested in finding answers to complex questions or questions related to an emerging, not-yet-understood topic. This does not mean giving up on empirical literature. Instead, it requires a bit of creativity in approaching the literature. A Venn diagram might help explain this process. Consider a scenario where a social worker wishes to locate literature to answer a question concerning issues of intersectionality. Intersectionality is a social justice term applied to situations where multiple categorizations or classifications come together to create overlapping, interconnected, or multiplied disadvantage. For example, women with a substance use disorder and who have been incarcerated face a triple threat in terms of successful treatment for a substance use disorder: intersectionality exists between being a woman, having a substance use disorder, and having been in jail or prison. After searching the literature, little or no empirical evidence might have been located on this specific triple-threat topic. Instead, the social worker will need to seek literature on each of the threats individually, and possibly will find literature on pairs of topics (see Figure 3-1). There exists some literature about women’s outcomes for treatment of a substance use disorder (a), some literature about women during and following incarceration (b), and some literature about substance use disorders and incarceration (c). Despite not having a direct line on the center of the intersecting spheres of literature (d), the social worker can develop at least a partial picture based on the overlapping literatures.

Figure 3-1. Venn diagram of intersecting literature sets.

Take a moment to complete the following activity. For each statement about empirical literature, decide if it is true or false.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

Want To Be Evidence-Based? Here’s a Literature Review Hack That Will Help You Get There

by Elspeth Slayter

Pile of Books

by Elspeth M. Slayter, PhD, MSW

    Our social work Code of Ethics calls on us to be evidence-based in our practice, so that we can demonstrate our familiarity with a practice area and its scholarly references, but there’s a lot of confusion out in the field about how to do this. I also see a lot of resistance to the task of reviewing the literature. Let’s face it—it sounds boring. This article is here to help and recognizes that practitioners exist in a productivity-driven, time-starved, and resource-strapped environment. So let’s strip this task down to the basics.

    To have this conversation, we need to start with two definitions. We need to know what an empirical study is (because that’s the literature we want to focus on) and we need to know what a social work intervention is (because that’s the type of article we want to find vs. opinion essays or sociological studies).

     Empirical study: A study that is either based on primary or secondary data collected from direct observations that are analyzed by study authors. It has a methods section that includes a sampling approach and a data analysis section. It is not an opinion article/essay that includes statistical data.

     Social work intervention: According to Sundell and Olsson , “In social work, interventions are intentionally implemented change strategies which aim to impede or eradicate risk factors, activate and/or mobilize protective factors, reduce or eradicate harm, or introduce betterment beyond harm eradication; thus social work intervention encompasses a range of psychotherapies, treatments, and programs.”

    It’s important to sift through all the commentary literature to find the empirical intervention study gems. It is true that reading through this stuff strikes fear into the hearts of many social workers who are phobic about reading statistics-heavy articles and reports. Schools of social work should support students and alumni in learning to demystify and translate the language of research and evaluation so that clinicians can make good on their commitment to be evidence-based.

    To start to consider the empirical literature base on any set of interventions that might be appropriate for a client, social workers should amass the relevant articles. As a student, you will have access to your university library database. You can watch my short screencast with a hack on how to search quickly and effectively . Lack of access to literature post-graduation is a common complaint that many raise, although this is less of an issue now that we have freely-accessible information from the Cochrane Library , the Society of Clinical Psychology’s list of empirically supported interventions , my own evidence-based practice page , and so on. Nowadays, many cities’ public libraries also have access to academic databases, so access to clinical journals is much more of an option for community-based clinicians.

    As you begin reading through your materials, you can begin the writing process. Start to take notes you can incorporate into a short write-up to share with colleagues. Part of approaching the task of literature review is knowing what to look for in the articles and reports that one finds. I argue that although a full academic literature review requires reading and understanding the full article as it relates to other articles in the stack, identifying themes, debates, and gaps as they go, social work clinicians can take a slightly different approach for field-based empirical literature review.

4 Areas of Focus

    In my opinion, a clinician with little time for reading, much less writing, should focus in on four things:

• what intervention/s was/were tested,
• who was sampled in the study,
• what measures were used in the study (if a quantitative study was conducted), and
• what the results of the study were).

    Each of these four areas should be thoroughly critiqued, as follows.

     Understanding the intervention/s studied: One of the main reasons to dig into the literature is to get a sense of the range of interventions that are used to treat a given condition or presenting problem. You want to get enough of a sense of what these interventions are, what is actually done with clients clinically, so that you can begin to replicate those actions.

    Examining the sample: One of the most important things you can do in reviewing an empirical intervention study as a clinician is to examine the methods section to see who was included in the study. In other words, what were the inclusion and exclusion criteria for the study’s sample, and specifically for the treatment and/or control group, if any? Once you identify those things, the key thing you want to think about is, how does that study’s sample generalize to your own clients, or does it? This is a question that is not often posed when we think about evidence-based practice. Too often, we just say, “Oh, that is an evidence-based practice because a study found it effective,” and we don’t think further about which population it was found effective on. We can do better than that!

     Considering the measures utilized: Another reason we review the empirical intervention literature as clinicians is to see how other evaluators have approached measuring process and outcome measures in their studies. We do this so we can use those same approaches, if possible, in our own evaluations, for the purpose of comparability. It is not only ethically mandated for us to evaluate our practice, but it is a best practice to relate our own evaluations back to the existing literature, which is best done when using similar measurement instruments.

     Critiquing the study’s results: Perhaps the most important aspect of the literature review is seeing how effective any given intervention was on particular client populations. Effectiveness may differ based on whether treatment was inpatient or outpatient, individual vs. group treatment, or on timeframe, for example. It is important to connect information about study results back to information about the study’s sample, as well.

    Noting whether statistical significance between groups or timeframes is present is important, understanding that sometimes the lack of statistical significance is a good thing, such as when a new treatment is compared to an existing, well-performing treatment. And, of course, considering not only statistical significance between groups or timeframes but also clinical significance or meaningfulness of the clinical scores (and effect size data, if available) is vital to a thorough literature review, as well. (See https://eslayter.pressbooks.com/chapter/chapter-10-critical-consumption-of-statistical-evaluation-data/.)

Synthesizing the Results, or Telling the Story of the Literature

    After these four elements of all relevant articles are considered, the clinician should synthesize the results by comparing and contrasting the studies read in order to pull it all together by “telling the story” of what the articles, taken together, say about the intervention/s in question. Above all else, do not write a list of the articles. To begin organizing what the literature says into a story, the write-up must demonstrate the connections and relationships between the articles and reports that have been considered.

    Some of the things to look for in your synthesis are: patterns or trends in study results or samples used, themes in use of measurements or outcome found, and debates about outcomes or gaps in study samples used. After identifying these items, a brief introduction should be written, before narrating the story, which can be done chronologically by publication date, thematically, methodologically, or theoretically.

    Telling the story of the literature chronologically is to follow the origins of the topic over time, but you want to do more than just present a list, which is really boring to read. Instead, think about presenting an analysis of patterns noted over time, perhaps commenting on turning points in the research, or key controversies or debates that have had an impact on the particular field of practice. If you are taking a thematic approach to telling the story of the literature, you can organize your writing into sections that talk about different parts of the story. Using a methodological approach to organize data may also be an option to consider, comparing qualitative vs. quantitative research and evaluation literature, for example. However, most of the empirical literature on clinical practice outcomes in social work will be quantitative.

    To start telling your story, you might mix several of these approaches. Your overall structure might be thematic, for example, but each theme could be talked about in a chronological order, for example. After telling the story of your literature, pull it all together with a brief introduction and conclusion to share with colleagues at your agency. This literature review grounds you in what the field has found helpful and not so helpful. It helps you to see who the literature has studied and not studied  and whether those populations are generalizable to your program’s population or not.

    Good luck with reviewing the empirical intervention literature and with sharing it with your colleagues to spread the evidence-based wealth and lift our profession to a better place!

Elspeth M. Slayter, PhD, MSW, is a professor of social work at Salem State University in Salem, Massachusetts. Her research focuses on supporting the disability community’s equitable treatment in child welfare and addiction treatment service systems.

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Conceptual Vs. Empirical Research: Which Is Better?

Scientific research is often divided into two classes: conceptual research and empirical research. There used to be distinct ways of doing research and a researcher would proudly claim to be one or the other, praising his method and scorning the alternative. Today the distinction is not so clear.

What is Conceptual Research?

Conceptual research focuses on the concept or theory that explains or describes the phenomenon being studied. What causes disease? How can we describe the motions of the planets? What are the building blocks of matter? The conceptual researcher sits at his desk with pen in hand and tries to solve these problems by thinking about them. He does no experiments but may make use of observations by others, since this is the mass of data that he is trying to make sense of. Until fairly recently, conceptual research methodology was considered the most honorable form of research—it required using the brain, not the hands. Researchers such as the alchemists who did experiments were considered little better than blacksmiths—“filthy empiricists.”

What is Empirical Research?

For all of their lofty status, conceptual researchers regularly produced theories that were wrong. Aristotle taught that large cannonballs fell to earth faster than small ones, and many generations of professors repeated his teachings until Galileo proved them wrong. Galileo was an empiricist of the best sort, one who performed original experiments not merely to destroy old theories but to provide the basis for new theories. A reaction against the ivory tower theoreticians culminated in those who claimed to have no use for theory, arguing that empirical acquisition of knowledge was the only way to the truth. A pure empiricist would simply graph data and see if he got a straight line relation between variables. If so, he had a good “empirical” relationship that would make useful predictions. The theory behind the correlation was irrelevant.

The Scientific Method: A Bit of Both

The modern scientific method is really a combination of empirical and conceptual research. Using known experimental data a scientist formulates a working hypothesis to explain some aspect of nature. He then performs new experiments designed to test predictions of the theory, to support it or disprove it. Einstein is often cited as an example of a conceptual researcher, but he based his theories on experimental observations and proposed experiments, real and thought, which would test his theories. On the other hand, Edison is often considered an empiricist, the “Edisonian method” being a by-word for trial and error. But Edison appreciated the work of theorists and hired some of the best. Random screening of myriad possibilities is still valuable: pharmaceutical companies looking for new drugs do this, sometimes with great success. Personally, I tend to be a semi-empiricist. In graduate school I used the Hammett linear free-energy relation (a semi-empirical equation) to gain insight into chemical transition states. So I don’t debate on “conceptual vs. empirical research.” There is a range of possibilities between both the forms, all of which have their uses.

Excellent explanations in a simple language.

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Thanks for this article,really helpful university of zambia

Albert Einstein did theoretical work–he had no laboratory, Put simply, through new conceptual models, he re-interpreted the findings of others and expressed them mathematically.

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Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

Difference between Theoretical and Empirical Research

The difference between theoretical and empirical research is fundamental to scientific, scholarly research, as it separates the development of ideas and models from their testing and validation.

These two approaches are used in many different fields of inquiry, including the natural sciences, social sciences, and humanities, and they serve different purposes and employ different methods.

Table of Contents

What is theoretical research.

Theoretical research involves the development of models, frameworks, and theories based on existing knowledge, logic, and intuition.

It aims to explain and predict phenomena, generate new ideas and insights, and provide a foundation for further research.

Theoretical research often takes place at the conceptual level and is typically based on existing knowledge, data, and assumptions.

What is Empirical Research?

In contrast, empirical research involves collecting and analysing data to test theories and models.

Empirical research is often conducted at the observational or experimental level and is based on direct or indirect observation of the world.

Empirical research involves testing theories and models, establishing cause-and-effect relationships, and refining or rejecting existing knowledge.

Theoretical vs Empirical Research

Theoretical research is often seen as the starting point for empirical research, providing the ideas and models that must be tested and validated.

Theoretical research can be qualitative or quantitative and involve mathematical models, simulations, and other computational methods.

Theoretical research is often conducted in isolation, without reference to primary data or observations.

On the other hand, empirical research is often seen as the final stage in the scientific process, as it provides evidence that supports or refutes theoretical models.

Empirical research can be qualitative or quantitative, involving surveys, experiments, observational studies, and other data collection methods.

Empirical research is often conducted in collaboration with others and is based on systematic data collection, analysis, and interpretation.

It is important to note that theoretical and empirical research are not mutually exclusive and can often complement each other.

For example, empirical data can inform the development of theories and models, and theoretical models can guide the design of empirical studies.

The most valuable research combines theoretical and empirical approaches in many fields, allowing for a comprehensive understanding of the studied phenomena.

It is important to note that this table is not meant to be exhaustive or prescriptive but rather to provide a general overview of the main difference between theoretical and empirical research.

The boundaries between these two approaches are not always clear, and in many cases, research may involve a combination of theoretical and empirical methods.

What are the Limitations of Theoretical Research?

Assumptions and simplifications may be made that do not accurately reflect the complexity of real-world phenomena, which is one of its limitations. Theoretical research relies heavily on logic and deductive reasoning, which can sometimes be biased or limited by the researcher’s assumptions and perspectives.

Furthermore, theoretical research may not be directly applicable to real-world situations without empirical validation. Applying theoretical ideas to practical situations is difficult if no empirical evidence supports or refutes them.

Furthermore, theoretical research may be limited by the availability of data and the researcher’s ability to access and interpret it, which can further limit the validity and applicability of theories.

What are the Limitations of Empirical Research?

There are many limitations to empirical research, including the limitations of the data available and the quality of the data that can be collected. Data collection can be limited by the resources available to collect the data, accessibility to populations or individuals of interest, or ethical constraints.

The researchers or participants may also introduce biases into empirical research, resulting in inaccurate or unreliable findings.

Lastly, due to confounding variables or other methodological limitations, empirical research may be limited by the inability to establish causal relationships between variables, even when statistical associations are identified.

What Methods Are Used In Theoretical Research?

In theoretical research, deductive reasoning, logical analysis, and conceptual frameworks generate new ideas and hypotheses. To identify gaps and inconsistencies in the present understanding of a phenomenon, theoretical research may involve analyzing existing literature and theories.

To test hypotheses and generate predictions, mathematical or computational models may also be developed.

Researchers may also use thought experiments or simulations to explore the implications of their ideas and hypotheses without collecting empirical data as part of theoretical research.

Theoretical research seeks to develop a conceptual framework for empirically testing and validating phenomena.

What Methods Are Used In Empirical Research?

Methods used in empirical research depend on the research questions, type of data collected, and study design. Surveys, experiments, observations, case studies, and interviews are common methods used in empirical research.

An empirical study tests hypotheses and generates new knowledge about phenomena by systematically collecting and analyzing data.

These methods may utilize standardized instruments or protocols for data collection consistency and reliability. Statistical analysis, content analysis, or qualitative analysis may be used for the data collection type.

As a result of empirical research, the findings can inform theories, models, and practical applications.

Conclusion: Theoretical vs Empirical Research

In conclusion, theoretical and empirical research are two distinct but interrelated approaches to scientific inquiry, and they serve different purposes and employ different methods.

Theoretical research involves the development of ideas and models, while empirical research involves testing and validating these ideas.

Both approaches are essential to research and can be combined to provide a more complete understanding of the world.

• Dictionary.com. “ Empirical vs Theoretical “.
• PennState University Libraries. “ Empirical Research in the Social Sciences and Education “.
• William M. Landes and Richard A. Posner. “ Legal Precedent: A Theoretical and Empirical Analysis “, The Journal of Law and Economics, 1976.

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Original research article, impact of industrial policy on urban green innovation: empirical evidence of china’s national high-tech zones based on double machine learning.

• College of Economics and Management, Taiyuan University of Technology, Taiyuan, China

Effective industrial policies need to be implemented, particularly aligning with environmental protection goals to drive the high-quality growth of China’s economy in the new era. Setting up national high-tech zones falls under the purview of both regional and industrial policies. Using panel data from 163 prefecture-level cities in China from 2007 to 2019, this paper empirically analyzes the impact of national high-tech zones on the level of urban green innovation and its underlying mechanisms. It utilizes the national high-tech zones as a quasi-natural experiment and employs a double machine learning model. The study findings reveal that the policy for national high-tech zones greatly enhances urban green innovation. This conclusion remains consistent even after adjusting the measurement method, empirical samples, and controlling for other policy interferences. The findings from the heterogeneity analysis reveal that the impact of the national high-tech zone policy on green innovation exhibits significant regional heterogeneity, with a particularly significant effect in the central and western regions. Among cities, there is a notable push for green innovation levels in second-tier, third-tier, and fourth-tier cities. The moderating effect results indicate that, at the current stage of development, transportation infrastructure primarily exerts a negative moderating effect on how the national high-tech zone policy impacts the level of urban green innovation. This research provides robust empirical evidence for informing the optimization of the industrial policy of China and the establishment of a future ecological civilization system.

1 Introduction

The Chinese economy currently focuses on high-quality development rather than quick growth. The traditional demographic and resource advantages gradually diminish, making the earlier crude development model reliant on excessive resource input and consumption unsustainable. Simultaneously, resource impoverishment, environmental pollution, and carbon emissions are growing more severe ( Wang F. et al., 2022 ). Consequently, pursuing a mutually beneficial equilibrium between the economy and the environment has emerged as a critical concern in China’s economic growth. Green innovation, the integration of innovation with sustainability development ideas, is progressively gaining significance within the framework of reshaping China’s economic development strategy and addressing the challenges associated with resource and environmental limitations. In light of the present circumstances, and with the objectives outlined in the “3060 Plan” for carbon peak and carbon neutral, the pursuit of a green and innovative development trajectory, emphasizing heightened innovation alongside environ-mental preservation, has emerged as a pivotal concern within the context of China’s contemporary economic progress.

Industrial policy is pivotal in government intervention within market-driven resource allocation and correcting structural disparities. The government orchestrates this initiative to bolster industrial expansion and operational effectiveness. In contrast to Western industrial policies, those in China are predominantly crafted within the administrative framework and promulgated through administrative regulations. Over an extended period, numerous industrial policies have been devised in response to regional disparities in industrial development. These policies aim to identify new growth opportunities in diverse regions, focusing on optimizing and upgrading industrial structures. These strategies have been implemented at various administrative levels, from the central government to local authorities ( Sun and Sun, 2015 ). As a distinctive regional economic policy in China, the national high-tech zone represents one of the foremost supportive measures a city can acquire at the national level. Its crucial role involves facilitating the dissemination and advancement of regional economic growth. Over more than three decades, it has evolved into the primary platform through which China executes its strategy of concentrating on high-tech industries and fostering development driven by innovation. Concurrently, the national high-tech zone, operating as a geographically focused policy customized for a specific region ( Cao, 2019 ), enhances the precision of policy support for the industries under its purview, covering a more limited range of municipalities, counties, and regions. Contrasting with conventional regional industrial policies, the industry-focused policy within national high-tech zones prioritizes comprehensive resource allocation advice and economic foundations to maximize synergy and promote the long-term sustainable growth of the regional economy, and this represents a significant paradigm shift in location-based policies within the framework of carrying out the new development idea. Its inception embodies a combination of central authorization, high-level strategic planning, local grassroots decision-making, and innovative system development. In recent years, driven by the objective of dual carbon, national high-tech have proactively promoted environmentally friendly innovation. Nevertheless, given the proliferation of new industrial policies and the escalating complexity of the policy framework, has the setting up of national high-tech zones genuinely elevated the level of urban green innovation in contrast to conventional regional industrial policies? What are the underlying mechanisms? Simultaneously, concerning the variations among different cities, have the industrial policy tools within the national high-tech zones been employed judiciously and adaptable? What are the concrete practical outcomes? Investigating these matters has emerged as a significant subject requiring resolution by government, industry and academia.

2 Literature review and research hypothesis

2.1 literature review.

When considering industrial policy, the setting up national high-tech zones embodies the intersection of regional and industrial policies. Domestic and international academic research concerning setting up national high-tech zones primarily centers on economic activities and innovation. Notably, the economic impact of national high-tech zones encompasses a wide range of factors, including their influence on total factor productivity ( Tan and Zhang, 2018 ; Wang and Liu, 2023 ), foreign trade ( Alder et al., 2016 ), industrial structure upgrades ( Yuan and Zhu, 2018 ), and economic growth ( Liu and Zhao, 2015 ; Huang and Fernández-Maldonado, 2016 ; Wang Z. et al., 2022 ). Regarding innovation, numerous researchers have confirmed the positive effects of national high-tech zones on company innovation ( Vásquez-Urriago et al., 2014 ; Díez-Vial and Fernández-Olmos, 2017 ; Wang and Xu, 2020 ); Nevertheless, a few scholars have disagreed on this matter ( Hong et al., 2016 ; Sosnovskikh, 2017 ). In general, the consensus among scholars is that setting up high-tech national zones fosters regional innovation significantly. This consensus is supported by various aspects of innovation, including innovation efficiency ( Park and Lee, 2004 ; Chandrashekar and Bala Subrahmanya, 2017 ), agglomeration effect ( De Beule and Van Beveren, 2012 ), innovation capability ( Yang and Guo, 2020 ), among other relevant dimensions. The existing literature predominantly delves into the correlation between the setting up of national high-tech zones, innovation, and economic significance. However, the rise of digital economic developments, notably industrial digitization, has accentuated the limitations of the traditional innovation paradigm. These shortcomings, such as the inadequate exploration of the social importance and sustainability of innovation, have become apparent in recent years. As the primary driver of sustainable development, green innovation represents a potent avenue for achieving economic benefits and environmental value ( Weber et al., 2014 ). Its distinctiveness from other innovation forms lies in its potential to facilitate the transformation of development modes, reshape economic structures, and address pollution prevention and control challenges. However, in the context of green innovation, based on the double-difference approach, Wang et al. (2020) has pointed out that national high-tech zones enhance the effectiveness of urban green innovation, but this is only significant in the eastern region.

Furthermore, scholars have also explored the mechanisms underlying the innovation effects of national high-tech. For example, Cattapan et al. (2012) focused on science parks in Italy. They found that green innovation represents a potent avenue for achieving economic benefits as the primary driver of sustainable development, and environmental value technology transfer services positively influence product innovation. Albahari et al. (2017) confirmed that higher education institutions’ involvement in advancing corporate innovation within technology and science parks has a beneficial moderating effect. Using the moderating effect of spatial agglomeration as a basis, Li WH. et al. (2022) found that industrial agglomeration has a significantly unfavorable moderating influence on the effectiveness of performance transformation in national high-tech zones. Multiple studies have examined the national high-tech zone industrial policy’s regulatory framework and urban innovation. However, in the age of rapidly expanding new infrastructure, infrastructure construction is concentrated on information technologies like blockchain, big data, cloud computing, artificial intelligence, and the Internet; Further research is needed to explore whether traditional infrastructure, particularly transportation infrastructure, can promote urban green innovation. Transportation infrastructure has consistently been vital in fostering economic expansion, integrating regional resources, and facilitating coordinated development ( Behrens et al., 2007 ; Zhang et al., 2018 ; Pokharel et al., 2021 ). Therefore, it is necessary to investigate whether transportation infrastructure can continue encouraging innovative urban green practices in the digital economy.

In summary, the existing literature has extensively examined the influence of national high-tech zones on economic growth and innovation from various levels and perspectives, establishing a solid foundation and offering valuable research insights for this study. Nonetheless, previous studies frequently overlooked the impact of national high-tech zones on urban green innovation levels, and a subsequent series of work in this paper aims to address this issue. Further exploration and expansion are needed to understand the industrial policy framework’s strategy for relating national high-tech zones to urban green innovation. Furthermore, there is a need for further improvement and refinement of the research model and methodology. Based on these, this paper aims to discuss the industrial policy effects of national high-tech zones from the perspective of urban green innovation to enrich and expand the existing research.

In contrast to earlier research, the marginal contribution of this paper is organized into three dimensions: 1) Most scholars have primarily focused on the effects of national high-tech zones on economic activity and innovation, with less emphasis on green innovation and rare studies according to the level of green innovation perspective. The study on national high-tech zones as an industrial policy that has already been done is enhanced by this work. 2) Regarding the research methodology, the Double Machine Learning (DML) approach is used to evaluate the policy effects of national high-tech zones, leveraging the advantages of machine learning algorithms for high-dimensional and non-parametric prediction. This approach circumvents the problems of model setting bias and the “curse of dimensionality” encountered in traditional econometric models ( Chernozhukov et al., 2018 ), enhancing the credibility of the research findings. 3) By introducing transportation infrastructure as a moderator variable, this study investigates the underlying mechanism of national high-tech zones on urban green innovation, offering suggestions for maximizing the influence of these zones on policy.

2.2 Theoretical analysis and hypotheses

2.2.1 national high-tech zones’ industrial policies and urban green innovation.

As one of the ways to land industrial policies at the national level, national high-tech zones serve as effective driving forces for enhancing China’s ability to innovate regionally and its contribution to economic growth ( Xu et al., 2022 ). Green innovation is a novel form of innovation activity that harmoniously balances the competing goals of environmental preservation and technological advancement, facilitating the superior expansion of the economy by alleviating the strain on resources and the environment ( Li, 2015 ). National high-tech zones mainly impact urban green innovation through three main aspects. Firstly, based on innovation compensation effects, national high-tech zones, established based on the government’s strategic planning, receive special treatment in areas such as land, taxation, financing, credit, and more, serving as pioneering special zones and experimental fields established by the government to promote high-quality regional development. When the government offers R&D subsidies to enterprises engaged in green innovation activities within the zones, enterprises are inclined to respond positively to the government’s policy support and enhance their level of green innovation as a means of seeking external legitimacy ( Fang et al., 2021 ), thereby contributing to the advancement of urban green innovation. Secondly, based on the industrial restructuring effect, strict regulation of businesses with high emissions, high energy consumption, and high pollution levels is another aspect of implementing the national high-tech zone program. Consequently, businesses with significant emissions and energy consumption are required to optimize their industrial structure to access various benefits within the park, resulting in the gradual transformation and upgrading of high-energy-consumption industries towards green practices, thereby further contributing to regional green innovation. Based on Porter’s hypothesis, the green and low-carbon requirements of the park policy increase the production costs for polluting industries, prompting polluting enterprises to upgrade their existing technology and adopt green innovation practices. Lastly, based on the theory of industrial agglomeration, the national high-tech zones’ industrial policy facilitates the concentration of innovative talents to a certain extent, resulting in intensified competition in the green innovation market. Increased competition fosters the sharing of knowledge, technology, and talent, stimulating a market environment where the survival of the fittest prevails ( Melitz and Ottaviano, 2008 ). These increase the effectiveness of urban green innovation, helping to propel urban green innovation forward. Furthermore, the infrastructure development within the national high-tech zones establishes a favorable physical environment for enterprises to engage in creative endeavors. Also, it enables the influx of high-quality innovation capital from foreign sources, complementing the inherent characteristics of national high-tech zones that attract such capital and concentrate green innovation resources, ultimately resulting in both environmental and economic benefits. Based on the above analysis, Hypothesis 1 is proposed:

Hypothesis 1. Implementing industrial policies in national high-tech zones enhances levels of urban green innovation.

2.2.2 Heterogeneity analysis

Given the variations in economic foundations, industrial statuses, and population distributions across different regions, development strategies in different regions are also influenced by these variations ( Chen and Zheng, 2008 ). Theoretically, when using administrative boundaries or geographic locations as benchmarks, the impact of national high-tech zone industrial policy on urban green innovation should be achieved through strategies like aligning with the region’s existing industrial structure. Compared to the western and central regions, the eastern region exhibits more incredible innovation and dynamism due to advantages such as a developed economy, good infrastructure, advanced management concepts, and technologies, combined with a relatively high initial level of green innovation factor endowment. Considering the diminishing marginal effect principle of green innovation, the industrial policy implementation in national high-tech zones favors an “icing on the cake” approach in the eastern region, contrasting with a “send carbon in the snow” approach in the central and western regions. In other words, the economic benefits of national high-tech zones for promoting urban green innovation may need to be more robust than their impact on the central and western regions. Literature confirms that establishing national high-tech zones yields a more beneficial technology agglomeration effect in the less developed central and western regions ( Liu and Zhao, 2015 ), leading to a more substantial impact on enhancing the level of urban green innovation.

Moreover, local governments consider economic development, industrial structure, and infrastructure levels when establishing national high-tech zones. These factors serve as the foundation for regional classification to address variations in regional quality and to compensate for gaps in theoretical research on the link between national high-tech zone industrial policy implementation and urban green innovation. Consequently, the execution of industrial policies in national high-tech zones relies on other vital factors influencing urban green innovation. Significant variations exist in economic development and infrastructure levels among cities of different grades ( Luo and Wang, 2023 ). Generally, cities with higher rankings exhibit strong economic growth and infrastructure, contrasting those with lower rankings. Consequently, the effect of establishing a national high-tech zone on green innovation may vary across different city grades. Thus, considering the disparities across city rankings, we delve deeper into identifying the underlying reasons for regional diversity in the green innovation outcomes of industrial policies implemented in national high-tech zones based on city grades. Based on the above analysis, Hypothesis 2 is proposed:

Hypothesis 2. There is regional heterogeneity and city-level heterogeneity in the impact of national high-tech zone policies on the level of urban green innovation.

2.2.3 The moderating effect of transportation infrastructure

Implementing industrial policies and facilitating the flow of innovation factors are closely intertwined with the role of transport infrastructure as carriers and linkages. Generally, enhanced transportation infrastructure facilitates the absorption of local factors and improves resource allocation efficiency, thereby influencing the spatial redistribution of production factors like labor, resources, and technology across cities. Enhanced transportation infrastructure fosters the development of more robust and advanced innovation networks ( Fritsch and Slavtchev, 2011 ). Banister and Berechman (2001) highlighted that transportation infrastructure exhibits network properties that are fundamental to its agglomeration or diffusion effects. From this perspective, robust infrastructure impacts various economic activities, including interregional labor mobility, factor agglomeration, and knowledge exchange among firms, thereby expediting the spillover effects of green technological innovations ( Yu et al., 2013 ). In turn, this could positively moderate the influence of national hi-tech zone policies on green innovation. On the other hand, while transportation infrastructure facilitates the growth of national high-tech zone policies, it also brings negative impacts, including high pollution, emissions, and ecological landscape fragmentation. Improving transportation infrastructure can also lead to the “relative congestion effect” in national high-tech zones. This phenomenon, observed in specific regions, refers to the excessive concentration of similar enterprises across different links of the same industrial chain, which exacerbates the competition for innovation resources among enterprises, making it challenging for enterprises in the region to allocate their limited innovation resources to technological research and development activities ( Li et al., 2015 ). As a result, there needs to be a higher green innovation level. Therefore, the impact of transportation infrastructure in the current stage of development will be more complex. When the level of transport infrastructure is moderate, adequate transport infrastructure supports the promotion of urban green innovation through national high-tech zone policies. However, the impact of transport infrastructure regulation may be harmful. Based on the above analysis, Hypothesis 3 is proposed:

Hypothesis 3. Transportation infrastructure moderates the relationship between national high-tech zones and levels of urban green invention.

3 Research design

3.1 model setting.

This research explores the impact of industrial policies of national high-tech zones on the level of urban green innovation. Many related studies utilize traditional causal inference models to assess the impact of these policies. However, these models have several limitations in their application. For instance, the commonly used double-difference model in the parallel trend test has stringent requirements for the sample data. Although the synthetic control approach can create a virtual control group that meets parallel trends’ needs, it is limited to addressing the ‘one-to-many’ problem and requires excluding groups with extreme values. The selection of matching variables in propensity score matching is subjective, among other limitations ( Zhang and Li, 2023 ). To address the limitations of conventional causal inference models, scholars have started to explore applying machine learning to infer causality ( Chernozhukov et al., 2018 ; Knittel and Stolper, 2021 ). Machine learning algorithms excel at an impartial assessment of the effect on the intended target variable for making accurate predictions.

In contrast to traditional machine learning algorithms, the formal proposal of DML was made in 2018 ( Chernozhukov et al., 2018 ). This approach offers a more robust approach to causal inference by mitigating bias through the incorporation of residual modeling. Currently, some scholars utilize DML to assess causality in economic phenomena. For instance, Hull and Grodecka-Messi (2022) examined the effects of local taxation, crime, education, and public services on migration using DML in the context of Swedish cities between 2010 and 2016. These existing research findings serve as valuable references for this study. Compared to traditional causal inference models, DML offers distinct advantages in variable selection and model estimation ( Zhang and Li, 2023 ). However, in promoting urban green innovation in China, there is a high probability of non-linear relationships between variables, and the traditional linear regression model may lead to bias and errors. Moreover, the double machine learning model can effectively avoid problems such as setting bias. Based on this, the present study employs a DML model to evaluate the policy implications of establishing a national high-tech zone.

3.1.1 Double machine learning framework

Prior to applying the DML algorithm, this paper refers to the practice of Chernozhukov et al. (2018) to construct a partially linear DML model, as depicted in Eq. 1 below:

where i represents the city, t represents the year, and l n G I i t represents the explained variable, which in this paper is the green innovation level of the city. Z o n e i t represents the disposition variable, which in this case is a national high-tech zone’s policy variable. It takes a value of 1 after the implementation of the pilot and 0 otherwise. θ 0 is the disposal factor that is the focus of this paper. X i t represents the set of high-dimensional control variables. Machine learning algorithms are utilized to estimate the specific form of g ^ X i t , whereas U i t , which has a conditional mean of 0, stands for the error term. n represents the sample size. Direct estimation of Eq. 1 provides an estimate for the coefficient of dispositions.

We can further explore the estimation bias by combining Eqs 1 , 2 as depicted in Eq. ( 3 ) below:

where a = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t U i t , by a normal distribution having 0 as the mean, b = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t g X i t − g ^ X i t . It is important to note that DML utilizes machine learning and a regularization algorithm to estimate a specific functional form g ^ X i t . The introduction of “canonical bias” is inevitable as it prevents the estimates from having excessive variance while maintaining their unbiasedness. Specifically, the convergence of g ^ X i t to g X i t , n −φg > n −1/2 , as n tends to infinity, b also tends to infinity, θ ^ 0 is difficult to converge to θ 0 . To expedite convergence and ensure unbiasedness of the disposal coefficient estimates with small samples, an auxiliary regression is constructed as follows:

where m X i t represents the disposition variable’s regression function on the high-dimensional control variable, this function also requires estimation using a machine learning algorithm in the specific form of m ^ X i t . Additionally, V i t represents the error term with a 0 conditional mean.

3.1.2 The test of the mediating effect within the DML framework

This study investigates how the national high-tech zone industrial policy influences the urban green innovation. It incorporates moderating variables within the DML framework, drawing on the testing procedure outlined by Jiang (2022) , and integrates it with the practice of He et al. (2022) , as outlined below:

Equation 5 is based on Eq. 1 with the addition of variables l n t r a i t and Z o n e i t * l n t r a i t .Where l n t r a i t represents the moderating variable, which in this paper is the transportation infrastructure. Z o n e i t * l n t r a i t represents the interaction term of the moderating variable and the disposition variable. The variables l n t r a i t and Z o n e i t are added to the high-dimensional control variables X i t , and the rest of the variables in Eq. 5 are identical to Eq. 1 . θ 1 represents the disposal factor to focus on.

3.2 Variable selection

3.2.1 dependent variable: level of urban green innovation (lngi).

Nowadays, many academics use indicators like the number of applications for patents or authorizations to assess the degree of urban innovation. To be more precise, the quantity of patent applications is a measure of technological innovation effort, while the number of patents authorized undergoes strict auditing and can provide a more direct reflection of the achievements and capacity of scientific and technological innovation. Thus, this paper refers to the studies of Zhou and Shen (2020) and Li X. et al. (2022) to utilize the count of authorized green invention patents in each prefecture-level city to indicate the level of green innovation. For the empirical study, the count of authorized green patents plus 1 is transformed using logarithm.

3.2.2 Disposal variable: dummy variables for national high-tech zones (Zone)

The national high-tech zone dummy variable’s value correlates with the city in which it is located and the list of national high-tech zones released by China’s Ministry of Science and Technology. If a national high-tech zone was established in the city by 2017, the value is set to 1 for the year the high-tech zone is established and subsequent years. Otherwise, it is set to 0.

3.2.3 Moderating variable: transportation infrastructure (lntra)

Previous studies have shown that China’s highway freight transport comprises 75% of the total freight transport ( Li and Tang, 2015 ). Highway transportation infrastructure has a significant influence on the evolution of the Chinese economy. The development and improvement of highway infrastructure are crucial for modern transportation. This paper uses the research methods of Wu (2019) and uses the roadway mileage (measured in kilometers) to population as a measure of the quality of the transportation system.

3.2.4 Control variables

(1) Foreign direct investment (lnfdi): There is general agreement among academics that foreign direct investment (FDI) significantly influences urban green innovation, as FDI provides expertise in management, human resources, and cutting-edge industrial technology ( Luo et al., 2021 ). Thus, it is necessary to consider and control the level of FDI. This paper uses the ratio of foreign investment to the local GDP in a million yuan.

(2) Financial development level (lnfd): Innovation in science and technology is greatly aided by finance. For the green innovation-driven strategy to advance, it is imperative that funding for science and technology innovation be strengthened. The amount of capital raised for innovation is strongly impacted by the state of urban financial development ( Zhou and Du, 2021 ). Thus, this paper uses the loan balance to GDP ratio as an indicator.

(3) Human capital (lnhum): Highly skilled human capital is essential for cities to drive green innovation. Generally, highly qualified human capital significantly boosts green innovation ( Ansaris et al., 2016 ). Therefore, a measure was employed: the proportion of people in the city who had completed their bachelor’s degree or above.

(4) Industrial structure (lnind): Generally, the secondary industry in China is the primary source of pollution, and there is a significant impact of industrial structure on green innovation ( Qiu et al., 2023 ). The metric used in this paper is the secondary industry-to-GDP ratio for the area.

(5) Regional economic development level (lnagdp): A region’s level of economic growth is indicative of the material foundation for urban green innovation and in-fluences the growth of green innovation in the region ( Bo et al., 2020 ). This research uses the annual gross domestic product per capita as a measurement.

3.3 Data source

By 2017, China had developed 157 national high-tech zones in total. In conjunction with the study’s objectives, this study performs sample adjustments and a screening process. The study’s sample period spans from 2007 to 2019. 57 national high-tech zones that were created prior to 2000 are omitted to lessen the impact on the test results of towns having high-tech zones founded before 2007. Due to the limitations of high-tech areas in cities at the county level in promoting urban green innovation, 8 high-tech zones located in county-level cities are excluded. And 4 high-tech zones with missing severe data are excluded. Among the list of established national high-tech zones, 88 high-tech zones are distributed across 83 prefecture-level cities due to multiple districts within a single city. As a result, 83 cities are selected as the experimental group for this study. Additionally, a control group of 80 cities was selected from among those that did not have high-tech zones by the end of 2019, resulting in a final sample size of 163 cities. This paper collects green patent data for each city from the China Green Patent Statistical Report published by the State Intellectual Property Office. The author compiled the list of national high-tech zones and the starting year of their establishment on the official government website. In addition, the remaining data in this paper primarily originated from the China Urban Statistical Yearbook (2007–2019), the EPS database, and the official websites of the respective city’s Bureau of Statistics. Missing values were addressed through linear interpolation. To address heteroskedasticity in the model, the study logarithmically transforms the variables, excluding the disposal variable. Table 1 shows the descriptive analysis of the variables.

Table 1 . Descriptive analysis.

4 Empirical analysis

4.1 national high-tech zones’ policy effects on urban green innovation.

This study utilizes the DML model to estimate the impact of industrial policies implemented in national high-tech zones at the level of urban green innovation. Following the approach of Zhang and Li (2023) , the sample is split in a ratio of 1:4, and the random forest algorithm is used to perform predictions and combine Eq. ( 1 ) with Eq. ( 4 ) for the regression. Table 2 presents the results with and without controlling for time and city effects. The results indicate that the treatment effect sizes for these four columns are 0.376, 0.293, 0.396, and 0.268, correspondingly, each of which was significant at a 1% level. Thus, Hypothesis 1 is supported.

Table 2 . Benchmark regression results.

4.2 Robustness tests

4.2.1 eliminate the influence of extreme values.

To reduce the impact of extreme values on the estimation outcomes, all variables on the benchmark regression, excluding the disposal variable, undergo a shrinkage process based on the upper and lower 1% and 5% quantiles. Values lower than the lowest and higher than the highest quantile are replaced accordingly. Regression analyses are conducted. Table 3 demonstrates that removing outliers did not substantially alter the findings of this study.

Table 3 . Extreme values removal results.

4.2.2 Considering province-time interaction fixed effects

Since provinces are critical administrative units in the governance system of the Chinese government, cities within the same province often share similarities in policy environment and location characteristics. Therefore, to account for the influence of temporal changes across different provinces, this study incorporates province-time interaction fixed effects based on the benchmark regression. Table 4 presents the individual regression results. Based on the regression results, after accounting for the correlation between different city characteristics within the same province, national high-tech zone policies continue to significantly influence urban green innovation, even at the 1% level.

Table 4 . The addition of province and time fixed effects interaction terms.

4.2.3 Excluding other policy disturbances

When analyzing how national high-tech zones affect strategy for urban green innovation, it is susceptible to the influence of concurrent policies. This study accounts for other comparable policies during the same period to ensure an accurate estimation of the policy effect. Since 2007, national high-tech zone policies have been successively implemented, including the development of “smart cities.” Therefore, this study incorporates a policy dummy variable for “smart cities” in the benchmark regression. The specific regression findings are shown in Table 5 . After controlling for the impact of concurrent policies, the importance of national high-tech zones’ policy impact remains consistent.

Table 5 . Results of removing the impact of parallel policies.

4.2.4 Resetting the DML model

To mitigate the potential bias introduced by the settings in the DML model on the conclusions, the purpose of this study is to assess the conclusions’ robustness using the following methods. First, the sample split ratio of the DML model is adjusted from 1:4 to 1:2 to examine the potential impact of the sample split ratio on the conclusions of this study. Second, the machine learning algorithm is substituted, replacing the random forest algorithm, which has been utilized as a prediction algorithm, with lasso regression, gradient boosting, and neural networks to investigate the potential influence of prediction algorithms on the conclusions of this study. Third, regarding benchmark regression, additional linear models were constructed and analyzed using DML, which involves subjective decisions regarding model form selection. Therefore, DML was employed to construct more comprehensive interactive models, aiming to assess the influence of model settings on the conclusions of this study. The main and auxiliary regressions utilized for the analysis were modified as follows:

Combining Eqs ( 7 ), ( 8 ) for the regression, the interactive model yielded estimated coefficients for the disposition effect:

The results of Eq. ( 9 ) are shown in column (5) of Table 6 . And all the regression results obtained from the modified DML model are presented in Table 6 .

Table 6 . Results of resetting the DML model.

The findings indicate that the sample split ratio in the DML model, the prediction algorithm used, or the model estimation approach does not impact the conclusion that the national high-tech zone policy raises urban areas’ level of green innovation. These factors only modify the magnitude of the policy effect to some degree.

4.3 Heterogeneity analysis

4.3.1 regional heterogeneity.

The sample cities were further divided into the east, central, and west regions based on the three major economic subregions to examine regional variations in national high-tech zone policies ' effects on urban green innovation, with the results presented in Table 7 . National high-tech zone policies do not statistically significantly affect urban green innovation in the eastern region. However, they have a considerable beneficial influence in the central and western areas. The lack of statistical significance may be explained by the possibility that the setting up of national high-tech zones in the eastern region will provide obstacles to the growth of urban green innovation, such as resource strain and environmental pollution. Given the central and western regions’ relatively underdeveloped economic status and industrial structure, coupled with the preceding theoretical analysis, establishing national high-tech zones is a crucial catalyst, significantly boosting urban green innovation levels. Furthermore, the central government emphasizes that setting high-tech national zones should consider regional resource endowments and local conditions, implementing tailored policies. The central and western regions possess unique geographic locations and natural conditions that make them well-suited for developing solar energy, wind energy, and other forms of green energy. Compared to the central region, the national high-tech zone initiative has a more pronounced impact on promoting urban green innovation in the western region. While further optimization is needed for the western region’s urban innovation environment, the policy on national high-tech zones has a more substantial incentive effect in this region due to its more significant development potential, positive transformation of industrial structure, and increased policy support from the state, including the development strategy for the western region.

Table 7 . Heterogeneity test results for different regions.

4.3.2 Urban hierarchical heterogeneity

The New Tier 1 Cities Institute’s ‘2020 City Business Charm Ranking’ is the basis for this study, with the sample cities categorized into Tier 1 (New Tier 1), Tier 2, Tier 3, Tier 4, and Tier 5. Table 8 presents the regression findings for each of the groups.

Table 8 . Heterogeneity test results for different classes of cities.

The results in Table 8 reveal significant heterogeneity at the city level regarding national high-tech zones’ effects on urban green innovation, confirming Hypothesis 2 . In particular, the coefficients for the first-tier cities are not statistically significant due to the small sample size, and the same applies to the fifth-tier cities. This could be attributed to the relatively weak economy and infrastructure development issues in the fifth-tier cities. Additionally, due to their limited level of development, the fifth-tier cities may have a relatively homogeneous industrial structure, with a dominance of traditional industries or agriculture and a need for a more diversified industrial layout. National high-tech zones have not greatly aided the development of green innovation in these cities. In contrast, national high-tech zone policies in second-tier, third-tier, and fourth-tier cities have a noteworthy favorable impact on green innovation, indicating their favorable influence on enhancing green innovation in these cities. Despite the lower level of economic development in fourth-tier cities compared to second-tier and third-tier cities, the fourth-tier cities’ national high-tech zones have the most pronounced impact on promoting green innovation. This could be attributed to the ongoing transformation of industries in fourth-tier cities, which are still in the technology diffusion and imitation stage, allowing these cities’ national high-tech zones to maintain a high marginal effect. Thus, Hypothesis 2 is supported.

5 Further analysis

According to the empirical findings, setting high-tech national zones significantly raises the bar for urban green innovation. Therefore, it is essential to understand the underlying factors and mechanisms that contribute to the positive correlation. This paper constructs a moderating effect test model using Eqs 5 , 6 and provides a detailed discussion by introducing transportation infrastructure as a moderating variable.

The empirical finding of the moderating impact of transportation infrastructure is shown in Table 9 . The dichotomous interaction term Zone*lntra is significantly negative at the 5% level, suggesting that the impact of national high-tech zone policies on the level of urban green innovation is negatively moderated by transportation infrastructure. This result deviates from the general expectation, but it aligns with the complexity of the role played by transportation infrastructure in the context of modern economic development, as discussed in the previous theoretical analysis. This could be attributed to the insufficient green innovation benefits generated by the policy on national high-tech zones at the current stage, which fails to compensate for the adverse effects of excessive resource consumption and environmental pollution caused by the construction of the zone. Furthermore, transportation infrastructure can lead to an excessive concentration of similar enterprises in the high-tech zones. This excessive concentration creates a relative crowding effect, intensifying competition among enterprises. It diminishes their inclination to engage in green innovation collaboration and investment and hinders their effective implementation of technological research and development activities. Moreover, the excessive clustering of similar enterprises implies a need for more diversity in green innovation activities among businesses located in national high-tech zones. This results in duplicated green innovation outputs and hinders the advancement of green innovation. Thus, Hypothesis 3 is supported.

Table 9 . Empirical results of moderating effects.

6 Conclusion and policy recommendations

6.1 conclusion.

Based on panel data from 163 prefecture-level cities in China from 2007 to 2019, the net effect of setting national high-tech zones on urban green innovation was analyzed using the double machine learning model. The results found that: firstly, the national high-tech zone policy significantly raises the degree of local green innovation, and these results remain robust even after accounting for various factors that could affect the estimation results. Secondly, in the central and western regions, the level of urban green innovation is positively impacted by the national high-tech zone policy; However, this impact is less significant in the eastern region. In the western region compared to the central region, the national high-tech zone initiative has a stronger impact on increasing the level of urban green innovation. Across different city levels, compared to second-tier and third-tier cities, the high-tech zone policy has a more substantial impact on increasing the level of green innovation in fourth-tier cities. Thirdly, based on the moderating effect mechanism test, the construction of transportation infrastructure weakens the promotional effect of national high-tech zones on urban green innovation.

6.2 Policy recommendations

In order that national high-tech zones can better promote China’s high-quality development, this paper proposes the following policy recommendations:

(1) Urban green innovation in China depends on accelerating the setting up of national high-tech zones and creating an atmosphere that supports innovation. Establishing national high-tech zones as testbeds for high-quality development and green innovation has significantly elevated urban green innovation. Thus, cities can efficiently foster urban green innovation by supporting the development of national high-tech zones. Cities that have already established national high-tech zones should further encourage enterprises within these zones to increase their investment in research and development. They should also proceed to foster the leadership of national high-tech zones for urban green innovation, assuming the role of pilot cities as models and leaders. Additionally, it is essential to establish mechanisms for cooperation and synergy between the pilot cities and their neighboring cities to promote collective green development in the region.

(2) Expanding the pilot program and implementing tailored policies based on local conditions are essential. Industrial policies about national high-tech zones have differing effects on urban green innovation. Regions should leverage their comparative advantages, consider urban development’s commonalities and unique aspects, and foster a stable and sustainable green innovation ecosystem. The western and central regions should prioritize constructing and enhancing new infrastructure and bolster support for the high-tech green industry. The western region should seize the opportunity presented by national policies that prioritize support, quicken the rate of environmental innovation, and progressively bridge the gap with the eastern and central regions in various aspects. Furthermore, second-tier, third-tier, and fourth-tier cities should enhance the advantages of national high-tech zone policies, further maintaining the high standard of green innovation and keeping green innovation at an elevated level. Regions facing challenges in green innovation, particularly fifth-tier cities, should learn from the development experiences of advanced regions with national high-tech zones to compensate for their deficiencies in green innovation.

(3) Highlighting the importance of transportation regulation and enhancing collaboration in green innovation is crucial. Firstly, transportation infrastructure should be maximized to strengthen coordination and cooperation among regions, facilitate the smooth movement of innovative talents across regions, and facilitate the rational sharing of innovative resources, collectively enhancing green innovation. Additionally, attention ought to be given to the industrial clustering effect of parks to prevent the wastage of resources and inefficiencies resulting from the excessive clustering of similar industries. Efforts should be focused on effectively harnessing the latent potential of crucial transportation infrastructure areas as long-term drivers of development, promptly mitigating the negative impact of transportation infrastructure construction, and gradually achieving the synergistic promotion of the setting up of national high-tech zones and the raising of urban levels of green innovation, among other overarching objectives.

6.3 Limitations and future research

Our study has some limitations because the research in this paper is conducted in the institutional context of China. For example, not all countries are suitable for implementing similar industrial policies to develop the economy while focusing on environmental protection. However, we recognize that this study is interesting and relevant, and it encourages us to focus more intensely on environmental protection from an industrial policy perspective. Moreover, this paper exhibits certain limitations in the research process. Firstly, the urban green innovation measurement index was developed using the quantity of green patent authorizations. Future studies could focus on green innovation processes, such as the quality of green patents granted. Secondly, the paper employs machine learning techniques for causal inference. Subsequent investigations could delve further into the potential applications of machine learning algorithms in environmental sciences to maximize the benefits of innovative research methodologies.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

WC: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing–review and editing. YJ: Conceptualization, Data curation, Formal Analysis, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. BT: Investigation, Project administration, Writing–review and editing.

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Youth Fund for Humanities and Social Science research of Ministry of Education (20YJC790004).

Acknowledgments

The authors are grateful to the editors and the reviewers for their insightful comments.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: national high-tech zone, industrial policy, green innovation, heterogeneity analysis, moderating effect, double machine learning

Citation: Cao W, Jia Y and Tan B (2024) Impact of industrial policy on urban green innovation: empirical evidence of China’s national high-tech zones based on double machine learning. Front. Environ. Sci. 12:1369433. doi: 10.3389/fenvs.2024.1369433

Received: 12 January 2024; Accepted: 15 March 2024; Published: 04 April 2024.

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*Correspondence: Yu Jia, [email protected]