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A Review of the Literature on Socioeconomic Status and Educational Achievement

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Social Capital in the Creation of Human Capital

Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research

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Unequal Childhoods: Class, Race, and Family Life

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• pp. 1 Socioeconomic Inequality and Educational Outcomes: An Introduction
• pp. 7 A Review of the Literature on Socioeconomic Status and Educational Achievement
• pp. 19 Methodology: Constructing a Socioeconomic Index for TIMSS Trend Analyses
• pp. 35 Socioeconomic Achievement Gaps: Trend Results for Education Systems
• pp. 71 Trends in Socioeconomic Achievement Gaps in the Macroeconomic Context: Discussion and Future Research

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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.

Prevent plagiarism. Run a free check.

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.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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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.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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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McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/dissertation/literature-review/

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A Review of the Literature on Socioeconomic Status and Educational Achievement

2019, IEA Research for Education

Related Papers

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Markus Broer , Frank T Fonseca

This open-access book focuses on trends in educational inequality using twenty years of grade 8 student data collected from 13 education systems by the IEA’s Trends in Mathematics and Science Study (TIMSS) between 1995 and 2015. While the overall positive association between family socioeconomic status (SES) and student achievement is well documented in the literature, the magnitude of this relationship is contingent on social contexts and is expected to vary by education system. Research on how such associations differ across societies and how the strength of these relationships has changed over time is limited. This study, therefore, addresses an important research and policy question by examining changes in the inequality of educational outcomes due to SES over this 20-year period, and also examines the extent to which the performance of students from disadvantaged backgrounds has improved over time in each education system. Education systems generally aim to narrow the achievement gap between low- and high-SES students and to improve the performance of disadvantaged students. However, the lack of quantifiable and comprehensible measures makes it difficult to assess and monitor the effect of such efforts. In this study, a novel measure of SES that is consistent across all TIMSS cycles allows students to be categorized into different socioeconomic groups. This measure of SES may also contribute to future research using TIMSS trend data. Readers will gain new insight into how educational inequality has changed in the education systems studied and how such change may relate to the more complex picture of macroeconomic changes in those societies.

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Ola Helenius

We reassess the relation between students’ socioeconomic status (SES) and their achievement by treating SES as multidimensional instead of unidimensional. We use data from almost 600,000 students in 77 countries participating in the 2018 PISA assessment of student achievement in math, science, and reading. The composite measure of SES that PISA uses can be broken down into six component variables that we here use as simultaneous predictors of achievement. This analysis yields several new insights. First, in the typical society, two predictors (books at home and parents’ highest occupational status) clearly outperform the rest. Second, a new composite measure based only on these two components often reveals substantially larger achievement gaps than those reported by PISA. Third, the analysis revealed remarkable differences between societies in the relation between achievement and wealth possessions. In most societies, the independent effect of wealth possessions on student achieveme...

Socioeconomic Inequality and Educational Outcomes

Psychological Bulletin

Nathuram Chaudhary

IRA International Journal of Education and Multidisciplinary Studies (ISSN 2455–2526)

Kazım Çelik

gerald hammond

Abstract Education in our country is one of the major topics for discussion. The conversation around education focuses on the failures of our teachers and students in our schools as compared to foreign countries institutions of higher learning. In a nation of divisive politics, education is an area that both major parties can agree to criticize. Critics claims that our schools are failing to prepare our nations youth for the 21st century world. While many of our nations schools, when examined, are indeed falling well short on standards established by local, State, and Federal officials. What is the truth? Are the schools failing our children? In order to understand the reason why too many students still fail to pass academic measurements, it is necessary to understand and define the major causes. Poverty and its effects on our student’s academic performance have long been identified as one of the leading causes of our academic ineptitude. But is it that simple? What is driving this factor? Surely no one is choosing to be poor, there must be a larger factor-taking place in our culture. This study seeks to show the link between poverty and low academic achievement, but it also strives to show that poverty is not a result of ignorance or inferiority that affects one group over another. Poverty is a part of learned culture, for those raised in this culture of despair and hopelessness there is little chance of escape

IEA Research for Education

Markus Broer

pradip suryawanshi

The following pages will present the influence that socioeconomic status has on school performance. Depending on culture, region and country, the socioeconomic status has an significant impact on school performance and it is seen as a good indicator of it. Method: Participants: a group of 100 young students age between 18 and 24 years old (M.=20.19; S.D.=1.54), all of them aged over 18, being in their fourth year of high school; Instruments: in order to validate the hypothesis we used a socioeconomic questionnaire of our own, since the concept covers several financial factors such as family, parents' academic level, lifestyle, family influence, the number of people in the house. For the academic performance measurement we used the average grades of the students. This group was given a questionnaire measuring the socioeconomic status. School performance was assessed by consulting the students' class books School performance correlates directly proportional to the duration of hours spent learning per day (r =0.221, p <0.05). Another statistically significant correlation is the one between school performance and extracurricular activities (r =-0.30, p <0.01) After the results were analyzed, we were able to determine that school performance is, indeed, influenced by the hours spent learning, free time, the presence of siblings in the family and the family home place, (in the rural or urban area), all of which are metrics for the socioeconomic status.

Scholarly R E S E A R C H Journal

" The present market based global village puts up a barrier in front of those who " cannot read or write or count, and cannot follow written instructions " (Sen, 1998). Education is the basic requirement and the 'Fundamental Right' of the citizens of a nation. While Higher Education is important; the Elementary Education system serves as the base over which the Superstructure of the whole education system is built up. Student's education is closely linked to their life chances, income, and well being. Therefore, it is important to have a clear understanding of what benefits or hinders his/her educational attainment. There are several relevant areas that are most commonly linked to academic performance while the most influencing factor is SocioEconomic Status of the family. Socioeconomic status (SES) is often measured as a combination of education, income, and occupation. Low SES and its correlates, such as lower education, poverty, and poor health, ultimately affect our society as a whole. The main aim of the present paper is to produce a comprehensive literature review of reliable research evidence on the relationship between students' educational attainment and parents' socio economic status.

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Do associations between education and obesity vary depending on the measure of obesity used? A systematic literature review and meta-analysis

Rozemarijn witkam.

a Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, UK

James M. Gwinnutt

Jennifer humphreys.

b NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, UK

Julie Gandrup

Rachel cooper.

c Department of Sport and Exercise Sciences, Musculoskeletal Science and Sports Medicine Research Centre, Manchester Metropolitan University, UK

Suzanne M.M. Verstappen

Associated data.

Consistent evidence suggests a relationship between lower educational attainment and total obesity defined using body mass index (BMI); however, a comparison of the relationships between educational attainment and total obesity (BMI ≥30 kg/m 2 ) and central obesity (waist circumference (WC) > 102 cm for men and WC > 88 cm for women) has yet to be carried out. This systematic literature review (SLR) and meta-analyses aimed to understand whether i) the associations between education and obesity are different depending on the measures of obesity used (BMI and WC), and ii) to explore whether these relationships differ by gender and region.

Medline, Embase and Web of Science were searched to identify studies investigating the associations between education and total and central obesity among adults in the general population of countries in the Organisation for Economic Co-operation and Development (OECD). Meta-analyses and meta-regression were performed in a subset of comparable studies (n=36 studies; 724,992 participants).

86 eligible studies (78 cross-sectional and eight longitudinal) were identified. Among women, most studies reported an association between a lower education and total and central obesity. Among men, there was a weaker association between lower education and central than total obesity (OR central vs total obesity in men 0.79 (95% CI 0.60, 1.03)). The association between lower education and obesity was stronger in women compared with men (OR women vs men 1.66 (95% CI 1.32, 2.08)). The relationship between lower education and obesity was less strong in women from Northern than Southern Europe (OR Northern vs Southern Europe in women 0.37 (95% CI 0.27, 0.51)), but not among men.

Conclusions

Associations between education and obesity differ depending on whether total or central obesity is used among men, but not in women. These associations are stronger among women than men, particularly in Southern European countries.

• • Associations of lower education with total and central obesity are stronger among women than men.
• • There was a stronger association of lower education and total obesity than central obesity in men.
• • Education and obesity were more strongly associated in women from South vs North Europe.

Introduction

The most recent global estimates for adults suggest that 11.6% (95% confidence interval (CI) 10.6%–12.6%) of males and 15.7% (95% CI 14.6%–16.8%) of females were obese in 2016 ( NCD-RisC, 2017 ). The prevalence is highest among high income countries ( Afshin et al., 2017 ), with a mean prevalence of 19.5% (95% CI not reported) in OECD countries in 2015 ( OECD, 2017 ). This poses enormous individual and public health risks as obesity is associated with increased all-cause mortality and significant morbidity ( Abranches et al., 2015 ; Carbone et al., 2013 , 2018 ; Thijssen et al., 2015 ). Total obesity is usually identified using body mass index (BMI), where a BMI ≥30 kg/m 2 is classed as obese in both men and women ( WHO, 2000 ). However, central obesity has received increased attention because of the additional prognostic information it may provide for some health outcomes, such as cardiovascular disease and type 2 diabetes ( Balkau et al., 2007 ; Janssen et al., 2004 ). Central obesity is usually identified measuring waist circumference (WC) (>102 cm for men and >88 cm for women). Although there are more precise measures of adiposity, such as body fat mass derived from skinfold thickness or dual energy X-ray absorptiometry (DXA), BMI and WC are the most commonly utilised measures as they are inexpensive and practical to use in epidemiological studies and routine clinical practice ( Hu, 2008 ).

The complex factors that play a role in the development of obesity can be described by the ‘social determinants of health’ model ( Whitehead and Dahlgren, 1991 ), which describes the multiple socioeconomic circumstances that can together influence a person's behaviour and health. Previous reviews have shown that lower socioeconomic position (SEP) is associated with obesity in high-income countries ( Cohen, Rai, Rehkopf, & Abrams, 2013a ; El-Sayed et al., 2012 ; Kim et al., 2017 ; McLaren, 2007 ; Newton et al., 2017 ; Parsons et al., 1999 ; Senese et al., 2009 ), but not in low-income countries ( Cohen, Rai, Rehkopf, & Abrams, 2013a ), suggesting that region (or more specifically economic status of a country) may modify the relationship between SEP and obesity. In studies examining SEP-obesity associations in high income countries, this was reported more consistently among women than men, suggesting that gender may modify the relationship between SEP and obesity ( Cohen, Rai, Rehkopf, & Abrams, 2013a ; El-Sayed et al., 2012 ; Kim et al., 2017 ; McLaren, 2007 ; Newton et al., 2017 ; Senese et al., 2009 ). Importantly, most of these studies focussed on BMI and few compared the associations of indicators of SEP with total and central adiposity. One review indicated that men and women with cumulative exposure to lower SEP across life had a higher mean BMI compared with those with a higher SEP across life; however, men with a lower SEP across life had lower mean WC compared with men with a higher SEP across life ( Newton et al., 2017 ). Therefore, associations between SEP and obesity may differ depending on whether the outcome is total or central obesity, but this has not been investigated.

Most reviews about SEP and obesity use multiple indicators of SEP including educational attainment, occupation, income or deprivation ( El-Sayed et al., 2012 ; McLaren, 2007 ; Newton et al., 2017 ; Senese et al., 2009 ). However, McLaren (2007) reported that adiposity outcomes vary by SEP indicator and thus they cannot be used interchangeably. This review focuses on educational attainment (numbers of years at school/highest qualifications obtained), because more so than occupation or income, it is an important indicator of SEP in early life, reflecting a family's lifestyle, material and intellectual resources, and it is also a strong predictor of SEP and life chances across adulthood ( Beebe-Dimmer et al., 2004 ; Smith et al., 1997 ). It has been proposed that increased health literacy and material and financial resources among people with higher levels of educational attainment lead to healthier lifestyles and reduced obesity rates ( Hulshof et al., 1991 ; Mazzocchi et al., 2009 ). Other advantages of studying educational attainment over other SEP indicators is that it is easy to measure, usually has a high response rate when measured in studies and can be assessed in all people regardless of age or working circumstances ( Galobardes et al., 2006 ). Understanding the link between educational attainment and different definitions of obesity may lead to the development of targeted education-based policy interventions that help to prevent obesity and related chronic diseases ( Devaux et al., 2011 ).

We therefore aimed to conduct a systematic literature review (SLR) and meta-analysis to: 1) understand whether the associations between educational attainment and obesity are different depending on the measures used to identify obesity (BMI and WC), and 2) explore whether these relationships differ by gender and region.

The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( Moher et al., 2009 ). The following PICO model defined the search strategy ( Table S1 ): Population (P), adults (aged ≥16 years) from the Organisation for Economic Co-operation and Development (OECD) countries (as of 2020 ( OECD, 2020a )); Intervention/exposure (I), educational attainment/years of education; Comparison (C), none (limited to observational studies); and Outcome (O), total obesity (BMI ≥30 kg/m 2 ) and central obesity (WC > 102 cm for men and WC > 88 cm for women).

Inclusion and exclusion criteria

Medline, Embase and Web of Science were searched for studies from January 1, 2000 until February 28, 2021 to summarise the literature most relevant to today's social environment. The inclusion criteria were 1) peer-reviewed articles including statistical analysis with an effect size for the association between educational status and obesity in the total study population and/or by gender, 2) total obesity or central obesity defined by BMI ≥30 or WC > 102 cm for men and WC > 88 cm for women ( WHO, 2000 ), 3) participants aged ≥16 years, 4) cross-sectional or prospective observational cohort studies, 5) OECD countries as of March 2020 ( OECD, 2020a ), and 6) English language articles only. Conference abstracts were excluded.

We focussed specifically on the state of total obesity or central obesity as weight change is not a definite proxy for excess adiposity. Only studies with participants aged ≥16 years were included in this review as children and younger adolescents were unlikely to have completed their education. Lastly, Cohen et al. (2013a) reported that the direction of the association between education and obesity depends on a country's economic status; therefore, only countries within the OECD as of 2020 were included to minimise sources of heterogeneity between studies.

Titles and abstracts were independently screened by RW and JG, and disagreements were solved through consensus discussion. Subsequently, full texts were screened by one reviewer (RW) and a random sample of 10% by a second reviewer (JMG) to confirm agreement. Disagreements of inclusion and exclusion of articles were resolved with an independent reviewer (SV). Reference lists of two previously conducted systematic literature reviews ( Cohen, Rai, Rehkopf, & Abrams, 2013a ; Kim et al., 2017 ) and of the included studies were also screened.

Data abstraction

Descriptive data on study population and design were extracted from all manuscripts using a standard pro forma. If a study presented results from unadjusted and adjusted models, only the independent effect sizes from the adjusted models were included in this review. If different countries, ethnicities or multiple time points were assessed in one article, estimates from each country, ethnicity or time point were reported as separate ‘data points’ where possible, though some studies pooled multiple time points into one data point. Countries were grouped by geographic region using the United Nations ‘M49 standard’ ( UNSD, 1999 ).

Data synthesis

For both BMI and WC, meta-analyses were performed if studies stratified results based on gender and if they reported an odds ratio (OR) with three or four educational categories. For BMI, an additional meta-analysis was performed for studies that estimated the effect of education with the relative index of inequality (RII) separately for men and women. RII is a regression based measure that compares the risk of obesity between those with the lowest and the highest education in a sample ( Mackenbach & Kunst, 1997 ). For the meta-analyses, pooled ORs were calculated using random-effect models. The lowest with the highest educational category was compared; if studies did not report in this order, an inverse of the OR and 95% CI was calculated. All meta-analyses were checked for publication bias using the Egger's test for asymmetry. Moreover, random-effect meta-regression analyses were performed to investigate differences between measures (BMI vs WC), gender (women vs men) and regions. Only the different regions in Europe were included in the meta-regression as there was a lack of data on the other regions. All statistical analyses were performed using Stata version 14, with Metan and Metareg packages. Studies that did not meet the above criteria for the meta-analyses and meta-regression are reported in a narrative summary.

Quality assessment

Study quality was assessed by RW using the Quality In Prognosis Studies (QUIPS) tool ( Hayden et al., 2013 ), recommended by the Cochrane Prognosis Methods Group ( Riley et al., 2019 ). Six domains were evaluated for each study: study participation, study attrition, prognostic factor measurement, outcome measurement, confounding and statistical analysis and reporting. For each domain, the risk of bias was rated ‘low’, ‘moderate’ or ‘high’.

The initial database search identified 3230 articles of which 2506 were unique records ( Fig. 1 ). After full-text review and reference list screening, 86 studies were included.

PRISMA flowchart of the selection of studies.

Description of included studies

Studies from thirty-two OECD countries were included in this review, representing all geographic regions of the M49 standard, except for South America. Of the 86 studies, the majority were cross-sectional (n=78), which means that the exposure (educational attainment) and outcome (obesity) were measured at the same time point. The median sample size of all studies was 6548 (interquartile range (IQR): 3410, 11,497). Mean age ranged from 18 years (SD: not reported (NR)) (a sample of 18 year old Portuguese conscripts)( Padez, 2006 ) to 68.7 years (SD: 0.2 [sic]) ( Pérez-Hernández et al., 2017 ), but the majority of studies (n=78, 90.7%) reported a mean age of above 40 years. Overall, studies were of good quality ( Table S6 ). The domains ‘attrition/response rate’, ‘outcome measurement’ and ‘statistical analysis’ received the most moderate to high bias ratings due to, respectively, no information about missing data, self-reported instead of measured height and weight data and no reporting of the obesity reference category (healthy weight or non-obese). The measurement of educational attainment and categorisation of educational level varied across studies ( Table S3 ). Table 1 , Table 3 report estimates comparing the lowest and highest educational categories.

Association between education and total obesity defined by BMI ≥30 kg/m 2 lowest vs highest educational categories.

N, sample size; CI, confidence interval; RII, relative index of inequality; NR, not reported; OR, odds ratio; RR, risk ratio; PR, prevalence ratio; SE, standard error; SD, standard deviation; USA, United States of America; U, urban; R, rural; B, Black; W, White; M-A, Mexican-American; A-A, African American. Only the estimate of the most recent year and of the lowest vs the highest or the highest vs the lowest education categories are shown here; however, all estimates are shown in Table S3 .*Subgroup meta-analysis based on one study †Indicate an inverse association (i.e. an association between lower education and obesity) based on statistical significance. ‡Estimates from linear probability models. § Regression coefficients from multivariable logistic regression models converted to ORs. ‖ Included in meta-analyses and meta-regression analyses ( Table 2A , Table 2B a and 2b).

Association between education and central obesity defined by WC > 102 cm for men and WC > 88 cm for women for the lowest vs the highest educational categories.

N, sample size; CI, confidence interval; OR, odds ratio; h, highest education; l, lowest education; NR, not reported; RII, relative index of inequality; RR, risk ratio; PR, prevalence ratio. Only the estimate of the most recent year and of the lowest vs the highest or the highest vs the lowest education categories are shown here; however, all estimates are shown in Table S4 . †Results that show an inverse association (i.e. an association between lower education and obesity) based on statistical significance. ‖ Included in meta-analyses and meta-regression analyses ( Fig. 2 and Table 4 ).

*Studies are ordered in the same way as Table 1 , Table 3 , based on region and date of survey.

Total and central obesity prevalence in different study samples are shown in Table S2 . In studies that reported estimates separately for men and women, total obesity prevalence was similar in men and women (mean prevalence 16.9% in women vs 17.0% in men), whereas prevalence of central obesity was often higher in women than men (mean prevalence 34.3% in women vs 23.8% in men). In studies presenting both measures (BMI and WC), central obesity prevalence was generally higher than total obesity prevalence. Obesity prevalence varied across countries and within countries: generally, the highest total and central obesity prevalence estimates were found in Northern America (survey years range 1993–2016) and Spain (survey years range 1997–2013) (ranges from 7.0 to 44.1% for total obesity and 21.8–59.7% for central obesity), and the lowest were found in Italy (survey years 2000, 2005), France (survey years range 1996–2008) and Denmark (survey years range 1994–2003) (ranges from 4.8 to 12% for total obesity and 13.6–15.4% for central obesity) ( Table S2 ).

Association between educational attainment and obesity defined by BMI

In total, 85 studies reported on associations between education and obesity defined using BMI ( Table S3 ). There were eight longitudinal studies (follow-ups were five ( Camões et al., 2010 ), 10 ( Chung and Kim, 2020 ), 13 (von Hippel & Lynch, 2014) , 14 ( Coogan et al., 2012 ), 23 ( Salsberry and Reagan, 2009 ), 29 (Cohen, Rehkopf, Deardorff, & Abrams, 2013b) , 33 ( Salonen et al., 2009 ) and 36 years ( Kim, 2016 )). Six studies reported results of multiple countries ( Devaux & Sassi, 2013 ; Drewnowski et al., 2005 ; Hughes et al., 2017 ; Klumbiene et al., 2004 ; Roskam et al., 2010 ; Sarlio-Lähteenkorva et al., 2006 ). Another six studies, all performed in the USA, reported on multiple ethnicities ( Beltrán-Sánchez et al., 2016 ; Cohen, Rehkopf, Deardorff, & Abrams, 2013b ; Ng et al., 2011 ; Qobadi and Payton, 2017 ; Salsberry and Reagan, 2009 ; Zhang and Wang, 2004 ). Therefore, the 85 studies included 101 data points for women, 91 for men and 35 data points for studies that combined men and women. 82 of the 85 studies reported results adjusted for covariates, and for three studies it was not clear ( Kilicarslan et al., 2006 ; Rurik et al., 2014 ; Zatońska et al., 2011 ). 65 studies reported stratified results for men and women ( Table 1 ). Five studies were eligible for the meta-analysis for studies that reported on the association of education modelled as RII, and 31 studies were included in the meta-analysis of studies that compared three or four educational categories. In both these meta-analyses, there was no evidence of publication bias using Egger's test (p=0.217 and p=0.686, respectively) (funnel plots are shown in Figs. S1 and S2 ).

Of the data points including women, 86.1% (87/101) found an association between lower levels of education (for example, fewer years of schooling or no qualifications) and higher odds of total obesity. This was 65.9% (60/91) for men. Subgroup meta-analysis of data points that reported on the association of education modelled as RII and odds of obesity showed higher pooled ORs for women (2.95 (95% CI 2.37, 3.68), I 2 =89.9% and 2.02 (95% CI 1.78, 2.31), I 2 =92.7%) compared with men (2.12 (95% CI 1.80, 2.48), I 2 =63.2% and 1.46 (95% CI 1.16, 1.83), I 2 =98.6%). These gender differences were tested in meta-regression analyses ( Table 2 a) and were found to be statistically significant: adjusted for region and number of educational categories the ORs were 1.66 (95% CI 1.32, 2.08), I 2 =58.92% for the RII subset of studies and 1.40 (95% CI 1.09, 1.81), I 2 =94.46%) for the OR subset of studies. Statistical heterogeneity was higher in studies that looked at the odds of obesity with three and four educational categories compared with RII, and subgroup meta-analysis indicate high statistical heterogeneity particularly in Western and Southern Europe ( Table 1 ).

Meta-regression to confirm gender differences for the association between education and total obesity defined by BMI ≥30 kg/m 2 , in a subset of studies modelling RII (n=5 studies) and OR with three to four educational categories (n=30 studies).

OR, odds ratio; CI, confidence interval; RII, relative index of inequality.

The association between a lower education and total obesity was more consistent in women than men in Northern America and Eastern, Western and Southern Europe compared with Northern Europe and Oceania, where effect sizes differed less between genders. These differences were confirmed by the meta-regression analyses in a subset of RII and studies with three or four educational categories respectively, which showed that there was a stronger association between a lower education and total obesity in women in Southern compared with Northern Europe (ORs for Northern vs Southern Europe: 0.37 (95% CI 0.27, 0.51), I 2 =20.31% and 0.59 (95% CI 0.40, 0.88), I 2 =91.81%), but this was not the case for men (ORs for Northern vs Southern Europe 0.77 (95% CI 0.40, 1.51), I 2 =67.05% and 0.88 (95% CI 0.66, 1.16), I 2 =74.0%) ( Table 2 b). There were no statistically significant differences between other regions in Europe ( Table S5 ), and due to a small amount of studies it was not possible to formally test differences between the other regions.

Meta-regression to confirm regional differences for the association between education and total obesity defined by BMI ≥30 kg/m 2 , in a subset of studies modelling RII (n=5 studies) and OR with three to four educational categories (n=30 studies).

OR, odds ratio; CI, confidence interval. Only the estimates of statistically significant differences between regions are shown here; however, comparisons of all regions that have enough data points are shown in Table S5 .

Association between educational attainment and central obesity defined by WC

16 studies reported on WC ( Table S4 ), of which 12 stratified results based on gender and eight studies were included in the meta-analysis ( Table 3 ). In 81.8% (9/11) ( Cameron et al., 2003 ; Camões et al., 2010 ; Ko et al., 2015 ; Marques-Vidal et al., 2008 ; Pérez-Hernández et al., 2017 ; Rurik et al., 2014 ; Sardinha et al., 2012 ; Stringhini et al., 2012 ; Yoon et al., 2006 ) of studies of women, a relationship between lower education and central obesity was found, with a pooled OR of 1.7 (95% CI 1.3, 2.1), I 2 = 82.5%. This was 50.0% (6/12) ( Cameron et al., 2003 ; Marques-Vidal et al., 2008 ; Pérez-Hernández et al., 2017 ; Rurik et al., 2014 ; Sardinha et al., 2012 ; Stringhini et al., 2012 ) for studies of men, with a pooled OR of 1.3 (95% CI 1.1, 1.6), I 2 = 74.4%. Similar to the results for BMI, among women there was more likely to be an association between lower levels of education and increased odds of central obesity than among men (OR women vs men 1.63 (95% CI 1.05, 2.54)) ( Table 4 ). At least one study of every region reported on WC, except for Western Asia, Northern America and Southern America. There were no clear differences in the effect sizes or the direction of the association between different regions; however, it was not possible to formally test this due to a small amount of studies. There was no evidence of publication bias in the meta-analysis using Egger's test (p=0.652) (funnel plot is shown in Fig. S3 ).

Meta-regression of a subset of studies reporting an OR for both BMI and WC for the association between education and obesity stratified by gender and obesity measure.

OR, odds ratio; CI, confidence interval. Based on eight studies that reported OR and that used three or four educational categories. Only the effect sizes of the lowest vs the highest education categories were included in the meta-analysis and meta-regression.

Comparing the results for BMI and WC

15 studies reported on both BMI and WC in the same sample. Eight of these reported on both men and women and had comparable educational categories and were included in the meta-analysis ( Fig. 2 ). The pooled ORs of total obesity were larger for both men and women (respectively, 1.66 (95% CI 1.31, 2.10) and 2.52 (95% CI 2.04, 3.11)) than for central obesity (1.32 (95% CI 1.09, 1.59) for men and 2.15 (95% CI 1.60, 2.88) for women). Meta-regression indicated that men were less likely to have an association between lower education and central obesity compared with total obesity (OR central vs total obesity 0.79 (95% CI 0.60, 1.03)) ( Table 4 ). This was less so the case among women (OR central vs total obesity 0.84 (95% CI 0.48, 1.47)).

Meta-analyses of studies reporting an OR for both BMI and WC for the association between education and obesity, stratified by measure and gender.

This SLR investigated how the association between education and obesity varies depending on the measure used to identify obesity, for men and women and between different regions of the OECD. The results show that, in OECD countries, the association between lower education levels and total and central obesity is stronger among women than men. Among men, more studies reported an association between lower education and total obesity compared with central obesity. Moreover, the association between lower education and total obesity was stronger among Southern compared with Northern European women.

The results of this SLR are similar to those found in a previous SLR, published in 2017, looking at the associations between multiple measures of SEP across life (e.g. parents or own occupation, income, education or material possessions) and obesity. Men and women with a lower life course SEP had a higher mean BMI; however, mean WC was lower among men with a lower compared to a higher life course SEP, whereas the opposite was seen for women ( Newton et al., 2017 ). This may suggest that educational inequalities manifest differently in men and women due to occupational differences. Research has shown that lower SEP was linked to increased occupational physical activity among men (i.e. manual occupations), but not among females (i.e. administrative or caring occupations) ( Beenackers et al., 2012 , Stalsberg and Pedersen, 2018 ) Increased occupational physical activity in men with lower education levels may lead to increased lean muscle mass ( Bann et al., 2014 ), resulting in higher BMI but normal WC. By contrast, this happens less often in women ( Wardle et al., 2002 ).

In general, the relationship between a lower SEP and obesity defined by BMI in high income countries have been confirmed by other SLRs among women, whereas more inconsistent results were found among men ( Cohen, Rai, Rehkopf, & Abrams, 2013a ; El-Sayed et al., 2012 ; Kim et al., 2017 ; McLaren, 2007 ; Newton et al., 2017 ; Senese et al., 2009 ); two of these focussed specifically on education ( Cohen, Rai, Rehkopf, & Abrams, 2013a ; Kim et al., 2017 ). Mechanisms through which education and SEP may affect obesity are outlined in the ‘social determinants of health’ model ( Whitehead and Dahlgren, 1991 ), where education influences living and working conditions and social and community networks which, in turn, influence individual lifestyle factors and health. This has been supported by studies that show that in high-income countries higher educated individuals eat healthier diets ( Irala-Estévez et al., 2000 ) and perform more leisure time physical activity ( Stalsberg and Pedersen, 2018 ), presumably due to increased health literacy ( Hulshof et al., 1991 ) and having better financial and emotional support ( Berkman, 1995 ). The ‘health belief model’ might help us to understand the stronger association between education and obesity observed among women compared with men, where perceived severity, susceptibility, benefits and barriers influence weight control practices ( Saghafi-Asl et al., 2020 ). Compared with men, women experience increased weight-related ideals, where a lower weight is seen as healthier and more attractive (perceived benefit of weight control practices). These weight-related ideals might be more difficult to sustain for women with a lower SEP ( Jeffery & French, 1996 ) (perceived barrier for weight control practices). Because of this, education may influence weight to a greater extent in women; however, this needs further investigation.

Our review also indicated geographical variation regarding the influence of gender on the relationship between education and obesity defined by BMI; in women, the association between lower education and obesity was stronger in Southern compared with Northern Europe. This difference was not seen in men. This might be explained by the fact that Northern European countries (compared to other OECD countries) have had a longstanding progressive agenda for gender equality, with concrete policies to ensure women and men from all educational backgrounds are equally represented in the workforce ( Borchorst & Siim, 2008 ; OECD, 2018 ). This has proven effective as figures show that compared to other OECD countries, Northern European countries have smaller gender gaps in labour market participation and working hours, and mothers are more likely to work ( Bann et al., 2014 ). In contrast, women with lower levels of education in Southern Europe often have a more ‘traditional’ role and participate less in the workforce, which might be reinforced by limited opportunities to work part-time and less financial support for child care ( Jurado-Guerrero & Naldini, 2018 ). Participating in the workforce increases social support, which may lead to increased empowerment to access health care services, and increase income levels to support a healthy lifestyle ( Berkman, 1995 ).

There are some disadvantages to using education as an indicator for SEP. Firstly, the meaning of education differs for different birth cohorts; trends of improving educational opportunities have resulted in increased educational attainment for women and ethnic minorities in recent decades, which means that people with lower levels of education are overrepresented in older birth cohorts ( Galobardes et al., 2006 ). These effects have not been accounted for in the included studies. Although using a publication cut-off of the year 2000 might have reduced these effects, there were still studies that included data from 1987 ( Table 1 ) and, thus, there will be some generational differences unaccounted for. One of the inclusion criteria was participants aged ≥16 years; as some included participants might not have finished their formal education yet, in some studies the highest levels of educational attainment may be underrepresented. Nonetheless, the results of four studies that included participants aged ≥16 years ( Devaux & Sassi, 2013 ; Martorell et al., 2000 ; Ogna et al., 2014 ; Tchicaya and Lorentz, 2012 ) do not differ substantially from the rest of the studies that included participants aged ≥18 years. Furthermore, qualifications and quality of education are not standardised across different countries and therefore makes comparisons across countries challenging ( OECD, 2020b ). However, the advantages of using education as an indicator in observational studies is that it is easy to measure and usually has a high response rate when assessed in clinical and epidemiological studies ( Galobardes et al., 2006 ). Although BMI and WC are the most commonly used measures of obesity in research and clinical settings, it is recognised that these measures lack some precision and do not directly measure fat mass. The relationship between life course SEP and body composition using more sophisticated, but more expensive, measures, such as DXA, computer tomography and magnetic resonance imaging, is assessed in another SLR ( Staatz et al., 2019 ).

Most studies presented low or moderate risk of bias in most of the domains of the QUIPS tool ( Table S6 ). When studies relied on self-reported height and weight to calculate BMI, they scored a ‘moderate risk of bias’ in the outcome measurement domain, as self-reported height and weight data are prone to social desirability bias and consequently measurement error bias (i.e. underreporting of weight and over reporting of height) ( Stommel and Schoenborn, 2009 ). Moreover, many studies presented no information about the reference category of obesity (healthy weight or non-obese), which impacted the score on the ‘statistical analysis’ domain. Despite these variabilities, the results were mostly consistent between studies and, therefore, unlikely to influence our conclusions. Most studies were cross-sectional and reverse causality cannot be ruled out (i.e. childhood obesity leads to lower education), a possibility that is supported by previous studies that showed that a proportion of the association is accounted by the reverse causation ( Kim et al., 2017 ; Howe et al., 2020 ). Because some studies have pooled data from multiple years, the survey years range from 1987 to 2016; in this time period, obesity has increased substantially ( Afshin et al., 2017 ). Variability in obesity prevalence ( Table S2 ) across and within countries may partly be due to variations in survey years. Sample selection bias may also play a role; for example, the national prevalence of obesity in France was estimated to be 11.9% (95% CI 11.5%, 12.3%) in 2003 ( Charles et al., 2008 ) whereas Roskam et al. (2010) reported an obesity prevalence of 6.0% in 2004, indicating that the study sample is not generalizable to the whole population of France at that time. Lastly, the Egger's test has been criticised because type 1 errors are likely to occur, leading to an overestimation of the presence of publication bias ( Peters et al., 2006 ; Schwarzer et al., 2002 ; Sterne et al., 2000 ). However, as none of the results from our Egger's tests were statistically significant, i.e. they did not indicate publication bias, this was not a concern in our review. Nonetheless, it is important to note that we only included formally published data in English language journals, and may therefore have missed some studies that were published in other languages.

A strength of this systematic literature review is that established protocols were followed and a large number of studies were synthesised. Furthermore, meta-analyses and meta-regression were performed in a subset of studies to formally test differences between measures, gender and region. To take into account the heterogeneity in definitions of education, it was decided to perform subgroup meta-analysis in studies with a similar education definition, where studies were combined based on the number of educational categories. This means that studies that did not define education based on three or four categories or did not estimate the relationship between education and obesity using RII were omitted for the meta-analyses; as a result, it is important to interpret the findings of the meta-analysis with some caution. Statistical heterogeneity was slightly reduced when adjusting for region or educational categories; the high degree of the remaining statistical heterogeneity might be caused by other factors, such as the inconsistent reporting of the obesity reference category. Moreover, only studies from OECD countries were included so that we could compare results of countries of a similar economic status. However, this does limit generalisability of our findings to countries outside the OECD. Although OECD countries are all considered high-income countries, there are still large differences socioeconomically, with the highest gross domestic product (GDP) of US$ 118,582 in Luxembourg and the lowest GDP of US$ 14,994 in Colombia ( OECD, 2021a ) in 2020 and in income inequality, with a Gini coefficient (an indicator of income inequality, where zero would represent an equal income for everyone) of 0.37 in the UK in 2019 and 0.26 in Belgium in 2018 ( OECD, 2021b ). Moreover, there are institutional and cultural differences between OECD countries, such as costs of further education, equal opportunities for men and women and compulsory military service (e.g. in South Korea and Israel) that may reflect educational attainment differences in different countries ( OECD, 2020b ). This means that direct comparison between countries may be problematic. Lastly, the majority of studies adjusted their analyses for relevant covariates such as age, gender (if applicable), other socioeconomic indicators and lifestyle factors.

This SLR has shown that both BMI and WC are important when researching obesity inequalities, particularly when examining gender differences. This might also be the case for other more accurate indicators (i.e. body fat percentage); therefore, there is a need to ensure a wide range of indicators of obesity are included in population surveys and public health interventions.

When devising strategies to prevent and treat obesity, it is important to take into account educational differences. A previous SLR indicated that targeted weight loss interventions for low SEP individuals delivered at schools, communities and primary care settings were effective in reducing weight in the short term ( Bambra, Hillier, & Cairns, 2015 ). Further research should also investigate whether interventions such as raising the compulsory education age reduces obesity levels over time.

In conclusion, this review strengthened the knowledge that lower educational attainment is associated with obesity, particularly for women. In addition, this study found that the association differed depending on the measure of obesity used: among men, there was more consistent evidence of the association between lower educational attainment and total obesity than central obesity, indicating the importance of using multiple measures of adiposity in future research and public health interventions.

CRediT authorship contribution statement

Rozemarijn Witkam: Conceptualization, Methodology, Formal analysis, Writing – original draft. James M. Gwinnutt: Conceptualization, Methodology, Formal analysis, Supervision, Writing – review & editing. Jennifer Humphreys: Conceptualization, Methodology, Supervision, Writing – review & editing. Julie Gandrup: Formal analysis, Writing – review & editing. Rachel Cooper: Writing – review & editing. Suzanne M.M. Verstappen: Conceptualization, Methodology, Supervision, Writing – review & editing.

Declaration of competing interest

Acknowledgements.

RW received a studentship award from the Economic and Social Research Council (reference number 10613098). JMG is funded by a Medical Research Council Skills Development Fellowship. This work is supported by Versus Arthritis (grant numbers 20385, 20380) and the NIHR Manchester Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ssmph.2021.100884 .

Ethics statement

As we did a systematic literature review, no ethics approval and consent were needed for this study.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

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• Library website

Education Literature Review: Education Literature Review

What does this guide cover.

Writing the literature review is a long, complex process that requires you to use many different tools, resources, and skills.

This page provides links to the guides, tutorials, and webinars that can help you with all aspects of completing your literature review.

The Basic Process

These resources provide overviews of the entire literature review process. Start here if you are new to the literature review process.

• Literature Reviews Overview : Writing Center
• How to do a Literature Review : Library
• Video: Common Errors Made When Conducting a Lit Review (YouTube)  

The Role of the Literature Review

Your literature review gives your readers an understanding of the evolution of scholarly research on your topic.

In your literature review you will:

• survey the scholarly landscape
• provide a synthesis of the issues, trends, and concepts
• possibly provide some historical background

Review the literature in two ways:

• Section 1: reviews the literature for the Problem
• Section 3: reviews the literature for the Project

The literature review is NOT an annotated bibliography. Nor should it simply summarize the articles you've read. Literature reviews are organized thematically and demonstrate synthesis of the literature.

For more information, view the Library's short video on searching by themes:

Short Video: Research for the Literature Review

(4 min 10 sec) Recorded August 2019 Transcript 

Search for Literature

The iterative process of research:

• Find an article.
• Read the article and build new searches using keywords and names from the article.
• Mine the bibliography for other works.
• Use “cited by” searches to find more recent works that reference the article.
• Repeat steps 2-4 with the new articles you find.

These are the main skills and resources you will need in order to effectively search for literature on your topic:

• Subject Research: Education by Jon Allinder Last Updated Aug 7, 2023 4777 views this year
• Keyword Searching: Finding Articles on Your Topic by Lynn VanLeer Last Updated Sep 12, 2023 24246 views this year
• Google Scholar by Jon Allinder Last Updated Aug 16, 2023 15313 views this year
• Quick Answer: How do I find books and articles that cite an article I already have?
• Quick Answer: How do I find a measurement, test, survey or instrument?

Video: Education Databases and Doctoral Research Resources

(6 min 04 sec) Recorded April 2019 Transcript 

Staying Organized

The literature review requires organizing a variety of information. The following resources will help you develop the organizational systems you'll need to be successful.

• Organize your research
• Citation Management Software

You can make your search log as simple or complex as you would like.  It can be a table in a word document or an excel spread sheet.  Here are two examples.  The word document is a basic table where you can keep track of databases, search terms, limiters, results and comments.  The Excel sheet is more complex and has additional sheets for notes, Google Scholar log; Journal Log, and Questions to ask the Librarian.  

• Search Log Example Sample search log in Excel
• Search Log Example Sample search log set up as a table in a word document.
• Literature Review Matrix with color coding Sample template for organizing and synthesizing your research

Writing the Literature Review

The following resources created by the Writing Center and the Academic Skills Center support the writing process for the dissertation/project study. 

• Critical Reading
• What is Synthesis 
• Walden Templates
• Quick Answer: How do I find Walden EdD (Doctor of Education) studies?
• Quick Answer: How do I find Walden PhD dissertations?

Beyond the Literature Review

The literature review isn't the only portion of a dissertation/project study that requires searching. The following resources can help you identify and utilize a theory, methodology, measurement instruments, or statistics.

• Education Theory by Jon Allinder Last Updated May 17, 2024 599 views this year
• Tests & Measures in Education by Kimberly Burton Last Updated Nov 18, 2021 47 views this year
• Education Statistics by Jon Allinder Last Updated Feb 22, 2022 60 views this year
• Office of Research and Doctoral Services

Books and Articles about the Lit Review

The following articles and books outline the purpose of the literature review and offer advice for successfully completing one.

• Chen, D. T. V., Wang, Y. M., & Lee, W. C. (2016). Challenges confronting beginning researchers in conducting literature reviews. Studies in Continuing Education, 38(1), 47-60. https://doi.org/10.1080/0158037X.2015.1030335 Proposes a framework to conceptualize four types of challenges students face: linguistic, methodological, conceptual, and ontological.
• Randolph, J.J. (2009). A guide to writing the dissertation literature review. Practical Assessment, Research & Evaluation 14(13), 1-13. Provides advice for writing a quantitative or qualitative literature review, by a Walden faculty member.
• Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404–428. https://doi.org/10.1177/1534484316671606 This article presents the integrative review of literature as a distinctive form of research that uses existing literature to create new knowledge.
• Wee, B. V., & Banister, D. (2016). How to write a literature review paper?. Transport Reviews, 36(2), 278-288. http://doi.org/10.1080/01441647.2015.1065456 Discusses how to write a literature review with a focus on adding value rather and suggests structural and contextual aspects found in outstanding literature reviews.
• Winchester, C. L., & Salji, M. (2016). Writing a literature review. Journal of Clinical Urology, 9(5), 308-312. https://doi.org/10.1177/2051415816650133 Reviews the use of different document types to add structure and enrich your literature review and the skill sets needed in writing the literature review.
• Xiao, Y., & Watson, M. (2017). Guidance on conducting a systematic literature review. Journal of Planning Education and Research. https://doi.org/10.1177/0739456X17723971 Examines different types of literature reviews and the steps necessary to produce a systematic review in educational research.

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• DOI: 10.1007/978-3-030-11991-1_2
• Corpus ID: 181862913

A Review of the Literature on Socioeconomic Status and Educational Achievement

• M. Broer , Yifan Bai , F. Fonseca
• Published in IEA Research for Education 2019
• Education, Sociology, Economics

49 Citations

Socioeconomic status and academic achievement in primary and secondary education: a meta-analytic review, school characteristics mediating the relationship between school socioeconomic status and mathematics achievement, the relationship of national, school, and student socioeconomic status with academic achievement: a model for programme for international student assessment reading and mathematics scores, teachers’ role in enhancing equity—a multilevel structural equation modelling with mediated moderation, cluster analysis of socio-economic factors and academic performance of school students, equity gaps in literacy among elementary school students from two countries: the negative social resonance effect of intersectional disadvantage and the dampening effect of learning capital, an investigation of pakistani university students’ socioeconomic classes, gender and dimensions in epistemological beliefs: dependencies and interlinks probed by structural equation modeling approach, educational achievement among children with a disability: do parental resources compensate for disadvantage, impact of socioeconomic status on academic achievement of medical students at alzaiem alazhari university 2021-2022, utilizing maternal prenatal cognition as a predictor of newborn brain measures of intellectual development.

• Highly Influenced

62 References

Socioeconomic status and academic achievement: a meta-analytic review of research, the influence of socioeconomic status, self-efficacy, and anxiety on mathematics achievement in england, greece, hong kong, the netherlands, turkey, and the usa, dimensions of socio-economic status and their relationship to mathematics and science achievement at individual and collective levels, cross-national differences in educational achievement inequality, achievement inequality and the institutional structure of educational systems: a comparative perspective, educational inequality in south korea: the widening socioeconomic gap in student achievement, social class differences in family-school relationships: the importance of cultural capital, methodological advances in cross-national surveys of educational achievement, achievement gaps in education, social capital and educational achievements: coleman vs. bourdieu, related papers.

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USCIS  announced  that it is extending the validity of certain Employment Authorization Documents (EADs) issued to Temporary Protected Status (TPS) beneficiaries from El Salvador, Honduras, Nepal, Nicaragua, and Sudan until March 9, 2025. Currently, most of these individuals are the beneficiaries of automatic extensions through June 30, 2024.

To implement these new extensions, USCIS is issuing Forms I-797, Notices of Action, to TPS beneficiaries who have pending applications to extend their EADs and to those who are eligible to re-register for TPS.

For  Form I-9 Employment Eligibility Verification purposes , employees may present their Form I-797, along with their TPS-based EAD (EADs with Codes A12 or C19) that expire on one of the 17 expiration dates listed on I-9 Central.

The agency also  reminded individuals eligible for TPS  from El Salvador, Haiti, Honduras, Nepal, Nicaragua, and Sudan who have not re-registered that they must do so by the below dates to maintain their TPS.

• El Salvador until March 9, 2025
• Honduras until July 5, 2025
• Nepal until June 24, 2025
• Nicaragua until July 5, 2025
• Sudan until April 19, 2025
• Haiti  until August 3, 2024

A separate announcement regarding the  extension of TPS for Haiti  beyond August 3, 2024,  will be published  in the Federal Register on July 1, 2024.

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A Review of the Literature on Socioeconomic Status and Educational Achievement

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The paper states that there is a positive association between family socioeconomic status (SES) and student achievement, but the magnitude of this relationship varies across countries due to differences in education systems and societal changes over time.

Socioeconomic status is positively associated with educational achievement, but the magnitude of this relationship varies across social contexts and education systems.

The literature review identifies researchable problems related to the association between socioeconomic status and educational attainment, including differences among education systems and changes over time.