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"Are you gonna publish that?" Peer-reviewed publication outcomes of doctoral dissertations in psychology

Spencer c. evans.

1 Clinical Child Psychology Program, University of Kansas, Lawrence, KS, United States of America

2 Department of Psychology, Harvard University, Cambridge, MA, United States of America

Christina M. Amaro

Robyn herbert.

3 Department of Psychology, Washington State University, Pullman, WA, United States of America

Jennifer B. Blossom

4 Department of Psychiatry & Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, United States of America

Michael C. Roberts

Associated data.

Data are publicly available from a variety of third party sources. A complete list of data sources has been included as a Supporting Information file, ' S1 File '.

If a doctoral dissertation represents an original investigation that makes a contribution to one’s field, then dissertation research could, and arguably should, be disseminated into the scientific literature. However, the extent and nature of dissertation publication remains largely unknown within psychology. The present study investigated the peer-reviewed publication outcomes of psychology dissertation research in the United States. Additionally, we examined publication lag, scientific impact, and variations across subfields. To investigate these questions, we first drew a stratified random cohort sample of 910 psychology Ph.D. dissertations from ProQuest Dissertations & Theses. Next, we conducted comprehensive literature searches for peer-reviewed journal articles derived from these dissertations published 0–7 years thereafter. Published dissertation articles were coded for their bibliographic details, citation rates, and journal impact metrics. Results showed that only one-quarter (25.6% [95% CI: 23.0, 28.4]) of dissertations were ultimately published in peer-reviewed journals, with significant variations across subfields (range: 10.1 to 59.4%). Rates of dissertation publication were lower in professional/applied subfields (e.g., clinical, counseling) compared to research/academic subfields (e.g., experimental, cognitive). When dissertations were published, however, they often appeared in influential journals (e.g., Thomson Reuters Impact Factor M = 2.84 [2.45, 3.23], 5-year Impact Factor M = 3.49 [3.07, 3.90]) and were cited numerous times (Web of Science citations per year M = 3.65 [2.88, 4.42]). Publication typically occurred within 2–3 years after the dissertation year. Overall, these results indicate that the large majority of Ph.D. dissertation research in psychology does not get disseminated into the peer-reviewed literature. The non-publication of dissertation research appears to be a systemic problem affecting both research and training in psychology. Efforts to improve the quality and “publishability” of doctoral dissertation research could benefit psychological science on multiple fronts.

Introduction

The doctoral dissertation—a defining component of the Doctor of Philosophy (Ph.D.) degree—is an original research study that meets the scientific, professional, and ethical standards of its discipline and advances a body of knowledge [ 1 ]. From this definition it follows that most dissertations could, and arguably should, be published in the peer-reviewed scientific literature [ 1 – 2 ]. For example, research participants typically volunteer their time and effort for the purposes of generating new knowledge of potential benefit; therefore, to breach this contract by not attempting to disseminate one’s findings is to violate the ethical standards of psychology [ 3 ] and human subjects research [ 2 , 4 ]. The nonpublication of dissertation research can also be detrimental to the advancement of scientific knowledge in other ways. Researchers may unwittingly and unnecessarily duplicate efforts from doctoral research when conducting empirical studies, or draw biased conclusions in meta-analytic and systematic reviews that often deliberately exclude dissertations. Many dissertations go unpublished due to nonsignificant and complicated results, exacerbating the “file drawer” problem [ 5 – 6 ]. Indeed, unpublished dissertations are rarely if ever cited [ 7 – 8 ].

The problem of dissertation non-publication is of critical importance in psychology. Some evidence [ 9 ] suggests that unpublished dissertations can play a key role in alleviating file drawer bias and reproducibility concerns in psychological science [ 10 ]. More broadly, the field of psychology—given its unique strengths, breadth, and diversity—poses a useful case study for examining dissertation nonpublication in the social, behavioral, and health sciences. Like other scientific disciplines, many Ph.D. graduates in psychology may be motivated to revise and submit their dissertations for publication for the usual reasons offered by academic and research careers. However, other new psychologists might not pursue this goal for a variety of reasons. Those in professional and applied subfields may commit most or all of their working time to non-research activities (e.g., professional practice, clinical training) and have little incentive to seek publication. Even those in more research-oriented subfields increasingly take non-research positions (e.g., industry, consultation, teaching, policy work) or other career paths which do not incentivize publications. Negative graduate school experiences, alternative career pursuits, and personal or family matters can all be additional factors that may decrease the likelihood of publication. Moreover, it is typically a challenging and time-consuming task to revise a lengthy document for submission as one or more journal articles. Still, all individuals holding a Ph.D. in psychology have (in theory) produced an original research study of scientific value, which should (again, in theory) be shared with the scientific community. Thus, for scientific, ethical, and training reasons, it is important to understand the frequency and quality of dissertation publication in psychology.

There is an abundance of literature relevant to this topic, including student or faculty perspectives (e.g., [ 11 – 13 ]) and studies of general research productivity during doctoral training and early career periods (e.g., [ 14 – 19 ]). However, evidence specifically regarding dissertation publication is remarkably sparse and inconsistent [ 8 , 20 – 24 ]. This literature is limited by non-representative samples, biased response patterns, and disciplinary scopes that are either too narrow or too broad to offer insights that are useful and generalizable for psychological science. For example, in the only psychology-specific study to our knowledge, Porter and Wolfle [ 23 ] mailed surveys to a random sample of individuals who earned their psychology doctorates. Of 128 respondents, 59% reported that their dissertation research had led to at least one published article. Unfortunately, this study [ 23 ] and others (e.g., [ 8 ]) are now over 40 years old, offering little relevance to the present state of training and research in psychology. A much more recent and rigorous example comes from the field of social work. Using a literature searching methodology and a random sample of 593 doctoral dissertations in social work, Maynard et al [ 22 ] found that 28.8% had led to peer-reviewed publications. However, this estimate likely does not generalize to psychology and its myriad subfields. Thus, there is a need for more comprehensive, rigorous, and recent data to better understand dissertation publication in psychology.

Accordingly, the present study investigated the extent and nature of dissertation publication in psychology, specifically examining the following questions: (a) How many dissertations in psychology are eventually published in peer-reviewed journals? (b) How long does it take from dissertation approval to article publication? (c) What is the scientific impact of published dissertations (PDs)? and (d) Are there differences across subfields of psychology? Based on the literature and our own observations, we hypothesized that (a) a majority of dissertations in psychology would go unpublished; (b) dissertation publication would occur primarily during the first few years after Ph.D. approval, diminishing thereafter; (c) PDs would show evidence of at least moderate scientific influence via citation rates and journal metrics; and (d) professional/applied subfields (clinical, counseling, school/educational, industrial-organizational, behavioral) would yield fewer PDs than research-oriented subfields (social/personality, experimental, cognitive, neuroscience, developmental, quantitative).

Materials and methods

The dataset of psychology dissertations was obtained directly from ProQuest UMI’s Dissertations and Theses Database (PQDT), which is characterized as “the world’s most comprehensive collection of dissertations and theses. . . [including] full text for most of the dissertations added since 1997. . . . More than 70,000 new full text dissertations and theses are added to the database each year through dissertations publishing partnerships with 700 leading academic institutions worldwide” [ 25 ]. While international coverage varies across countries, PQDT’s repository is estimated to include approximately 97% of all U.S. doctoral dissertations [ 26 ], across all disciplines, institutions, and training models.

Upon request, PQDT provided a database of all dissertations indexed with the term “psychology” in the subject field during the year 2007. This resulted in a total population of 6,580 dissertations, which were then screened and sampled according to pre-defined criteria. The number of dissertations included at each stage in the sampling process is summarized in a PRISMA-style [ 27 ] flow diagram for the overall sample in Fig 1 , and broken down by subfield in Table 1 . Dissertations were excluded if written in a language other than English, for any degree other than Ph.D. (e.g., Psy.D., Ed.D.), or in any country other than U.S. The remaining dissertations were recoded for subfields based on the subject term classification in PQDT, with a few modifications (e.g., combining “neuroscience” and “biological psychology”). This left a remaining sample of 3,866 relevant dissertations, representing our population. This figure is approximately in line with the U.S. National Science Foundation’s Survey of Earned Doctorates [ 28 ] estimate that 3,276 research doctorates in psychology were granted during the year 2007, suggesting that PQDT could be slightly broader or more comprehensive in scope.

Note. PQDT = ProQuest Dissertations and Theses. a Categories of excluded dissertations are mutually exclusive, summing to 100%. b PQDT exclusion criteria were applied sequentially in the order presented; thus, the number associated with each exclusion criterion reflects how many were excluded from the sample that remained after the previous criterion was applied. Adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [ 27 ].

Note. “Relevant dissertations” refers to all PQDT dissertations that satisfied screening criteria for inclusion. Dissertations were excluded in the eligibility stage based on the date of approval in the full text (see Fig 1 ). Rates of exclusion were not significantly different across subfields. Sampling weights for each subfield were calculated as the proportion in the population (relevant dissertations) divided by proportion in the full sample, after adjusting for the proportions within each subfield that were from excluded for ineligibility.

From this relevant population of 3,866, we drew a stratified random sample of 1,000 dissertations. This number was selected because it represented over 25% of the population and offered sufficient power to obtain 95% CIs less than ±3% for the overall proportion estimates (i.e., the primary research question). As shown in Table 1 , the sampling procedure was stratified by subfield using a formula that sought to balance (a) power for between-group comparisons, aiming to include ≥50 dissertations from each subfield; and (b) representativeness to the population, aiming to include ≥10% of the dissertations from each subfield. This resulted in subfield sample sizes ranging from 59 for general/miscellaneous (75.6% of relevant subfield population) to 179 for clinical (12.5% of relevant subfield population). Ninety (9.0%) dissertations were later found to be ineligible during the full-text review because the approval date was before or after the year 2007. This incongruence was partly explained by copyright or graduation dates differing from the dissertation year, and was not significantly different across subfields. The resulting final sample consisted of 910 dissertations, with subfield samples ranging from 52 (general/miscellaneous) to 159 (clinical). Because this study did not meet the definition of human subjects research, institutional review board approval was not required.

Search timeframe

We aimed to conduct prospective follow-up searches for PDs within a timeframe that was both (a) long enough to capture nearly 100% of PDs and (b) short enough for results to retain their relevancy to the current state of psychological science. Because the literature does not offer dissertation-publication “lag time” statistics for reference, we used the “half-life” of knowledge—i.e., the average time it takes for half of a body of knowledge to become disproven or obsolete [ 29 – 30 ]. Across methodologies, the half-life of knowledge in psychology has been estimated at 7–9 years [ 31 – 33 ]. Accordingly, we selected a prospective search window allowing 0–7 years for dissertations to be published. Because the doctoral dissertations were sampled from the year 2007, follow-up searches were restricted to articles published between 2007 and 2014. We elected to exclude candidate publications from years prior to 2007 for several reasons. First, most U.S. psychology Ph.D. programs follow a more traditional dissertation model (and this would have been even more ubiquitous in 2007), where the dissertation would have to be completed before it could be published in a peer-reviewed journal. Second, even for the minority of programs that might follow less conventional models such as dissertation-by-publication [ 34 ], the lag-time to publication would likely still result in at least one PD appearing in print concurrently with or after the dissertation, and would therefore be captured by our search strategy. Finally, any potential benefits of searching retrospectively were outweighed by the potential risks of introducing unreliability into the data, such as identifying false positives from student publications, master’s theses, pilot studies, or other analyses from the same sample. On the other end of our search window, candidate publications that appeared in print during or after 2015 were also not considered. Post hoc analyses (see Results ) suggested that this 0–7 year timeframe was adequate.

Publication search and coding procedures

Searches for PDs were conducted in two rounds, utilizing scholarly databases in a manner consistent with the evidence regarding their specificity, sensitivity, and quality. Specifically, searches were conducted first in PsycINFO, which has high specificity for psychological, social, and health sciences [ 35 – 36 ]; and second, in Google Scholar, casting a much broader net but still searching for peer-reviewed scholarly journal articles [ 35 , 37 – 40 ]. The objective of these searches was to locate the PDs or to determine that the dissertation had not been published in the indexed peer-reviewed journals. Although it is never possible to definitively ascertain a thing’s non-existence, we added additional steps and redundancies to ensure that our searches were as exhaustive as possible. First, when no PD was found in either scholarly databases, as a final step we conducted Google searches for the dissertation author and title, then reviewed the search results (e.g., CVs posted online, faculty web pages) for possible PDs. Second, all searching/coding procedures were performed at least twice by trained research assistants. If two coders disagreed on whether a PD was found, which article it was, or if either coder was uncertain, these dissertations were then coded by consensus among three or more members of the research team, including master’s-level researchers (SCE and CMA).

In all literature searches, the following queries were entered for each dissertation: (a) title of dissertation, without punctuation or logical operand terms; (b) author/ student’s name; and (c) chair/ advisor’s name. Search results were assessed for characteristics of authorship (student and chair names), content (title, abstract, acknowledgments, methods), and publication type (specifically targeting peer-reviewed journal articles) by which a PD could be positively identified. Determination of PD status was made and later validated based on global judgments of these criteria. Identified PDs were then coded for their bibliographic characteristics. Results were excluded if published in a non-English journal, outside of the 0–7 year (2007–2014) window, or in a non-refereed or non-journal outlet (e.g., book chapters). Because dissertations can contain multiple studies and be published as multiple articles, searches aimed to identify a single article that was most representative of the dissertation, based on the criteria outlined above and by consensus agreement among coders. All searches were conducted and coding was completed between January 2015 and May 2017.

Dissertation, publication, and year

Although the structure and content of doctoral dissertations varies across institutions, countries, and disciplines, the common unifying factor is that the dissertation represents an original research document produced by the student, approved by faculty, and for which a degree is conferred. Accordingly, in using PQDT as our population of U.S. Ph.D. psychology dissertations, we adhere to this broad but essential definition of a dissertation. This definition includes all different models of dissertations (e.g., ranging from traditional monographs to more recent models, such as briefer publication-ready dissertations and dissertation by publication [ 34 ]), but does not differentiate among them.

In this paper and in common scientific usage, “publication” refers to the dissemination of a written work to a broad audience, typically through a journal article. Accordingly, we do not consider indexing in digital databases for theses and dissertations as a publication such as in PQDT, even though it may be called “publishing” by the company. Rather, we define “dissertation publication” as the dissemination of at least part of one’s Ph.D. dissertation research in the form of an article published in a peer-reviewed journal. The peer-reviewed status of the journal was included among the variables that were coded twice with discrepancies resolved by consensus. Lastly, year of publication (2007, 2008, 2009 … 2014) and years since approval (0, 1, 2 … 7) were coded from when the print/final version of the article appeared, given that advance online access varies and is not available in all journals.

The PQDT subject terms were used as a proxy indicator of the subfield of psychology from which the dissertation was generated. As described above, twelve categories were derived ( Table 1 ). We considered five categories as professional/ applied subfields (clinical, counseling, educational/school, industrial-organizational, and behavioral), given that graduates in these fields are trained for careers that often include professional licensure or applied activities (e.g., consultation, program evaluation). In contrast, seven categories were considered research/ academic subfields (cognitive, developmental, experimental, neuroscience, quantitative, and social/personality), given that these subfields train primarily in a substantive or methodological research area. Note that Ph.D. programs in all of these subfields train their students to conduct research; when professional/ applied training components are present, they are there in addition to, not instead of, research training.

Article citations

The influence of PDs was estimated using article- and journal-level variables. At the article level, we used Web of Science to code the number of citations to the PD occurring each year since publication, tracking from 2007 up through year 2016. Importantly, Web of Science has been found to exhibit the lowest citation counts, but the citations which are included are drawn from a more rigorously controlled and higher quality collection of scholarly publications compared to others like Google Scholar, PubMed, and Scopus [ 35 , 37 – 38 , 40 – 41 ]. Citations were coded and analyzed primarily as the mean number of citations per year in order to account for time since publication. Total citations and citations each year were also calculated.

Journal-level metrics

The following journal impact metrics were recorded for the year in which the PD was published: (a) Impact Factor (IF) and (b) 5-Year IF [ 42 ]; (c) Article Influence Score (AIS) [ 43 ]; (d) Source Normalized Impact (SNIP) [ 44 ]; and (e) SCImago Journal Rank indicator (SJR) [ 45 ]. Each of these indices shares different similarities and distinctions from the others and provides different information about how researchers cite articles in a given journal. While each has its limitations, these five indicators together offer a broad overall characterization of a journal’s influence, without over-relying on any single metric. As a frame of reference, the population-level descriptive statistics for each of these journal metrics (2007–2014) are as follows: IF ( M = 1.8, SD = 2.9), 5-year IF ( M = 2.2, SD = 3.0), SNIP ( M = 0.9, SD = 1.0), SJR ( M = 0.6, SD = 1.1), and AIS ( M = 0.8, SD = 1.4).

As described above, all of the dissertation, literature searching, and outcome data used in the present study were obtained from a variety of online sources available freely or by institutional subscription. Links to these sources can be found in the supplementary materials ( S1 File ).

Analytic plan

Overall descriptive analyses were conducted to examine the univariate and bivariate characteristics of the data, including the frequency and temporal distribution of PDs in psychology. Similar descriptive statistics were provided to characterize the nature of and scholarly influence of the PD via article citations and journal impact metrics. Group-based analyses were conducted using chi-square and ANOVAs to assess whether dissertation publication rates and scientific influence differed across subfields of psychology. The 95% CIs surrounding the total weighted estimate were used as an index of whether subfield estimates were significantly above or below average.

Time-to-publication analyses were conducted in three different ways. First, we used weighted Cox regression and Kaplan-Meier survival analyses to model dissertation publication as a time-to-event outcome, both for the overall sample and separately by subfields. Second, because the large majority of dissertations “survived” the publication outcome past our observation window (i.e., most cases were right-censored), we also conducted between-group comparisons regarding subfield publication times for only those whose dissertations were published. Finally, in order to ensure the adequacy of our 0–7 year search window, we fit a distribution to our observed data and projected this trend several years into the future.

Full-sample analyses were conducted using the complex samples option in SPSS Version 24, which yields weighted estimates that are less biased by sample proportions and more generalizable to the population. Distribution model-fitting and projections were estimated in R. For analyses related to dissertation publication outcomes, there were no missing data because all values could be coded based on obtained dissertations. Data availability for journal- and article-level variables are reported in those results tables.

Frequency of and time to publication

The overall weighted estimate showed that 25.6% (95% CI: 23.0, 28.4) of psychology dissertations were published in peer-reviewed journals within the period of 0–7 years following their completion. The unweighted estimate was similar (27.5% [24.6, 30.4]), but reflected sampling bias due to differences between subfields. Thus, weighted estimates are used in all subsequent results. Significant variations were found across subfields (Rao-Scott adjusted χ 2 ( df = 9.65) = 65.28, F (9.65, 8869.62) = 8.28, p < .001). As shown in Table 2 , greater proportions of PDs were found in neuroscience (59.4% [47.8, 70.1]), experimental (50.0% [37.7, 62.3]), and cognitive (41.0% [31.1, 51.8]), whereas much lower rates were found for industrial-organizational (10.1% [5.0, 19.5]) and general/miscellaneous (13.5% [6.5, 25.7]). All other subfields fell between 19.0 and 29.0%. Quantitative and social/personality fell within the 95% CIs for the weighted total, suggesting no difference; however, most other subfields fell above or below this average. Of note, three core professional subfields (clinical, counseling, and school/educational) were all between 19.0 and 20.8%—below average and not different from one another.

a Greater than weighted total

b Less than weighted total.

The overall time-to-publication results are presented in Table 3 and the left panel of Fig 2 . As shown, over half (56.0% of those ultimately published; 14.3% of the total sample) of PDs appeared in print within 2 years following the year of completion, with the large majority (89.7% of ultimately published; 23.0% of total) being published within 5 years. Among those dissertations that were ultimately published, the time to publication averaged about 2–3 years ( M = 2.58 [2.34, 2.83]), with a median of 2 years and a mode of 1 year. Omnibus comparisons from the Kaplan-Meier survival model revealed significant variations across subfields, χ 2 ( df = 1) = 4.24, p = .039), as plotted in the right panel of Fig 2 . These results generally mirrored the same pattern found for overall binary publication outcomes across subfields. Among only those dissertations that were published, the subfield differences in time-to-publication were marginal overall, F (11, 238) = 5.99, p = .064, but still shed some additional light beyond the binary publication outcomes. Specifically, neuroscience ( M = 1.61, [1.09, 2.13]), counseling ( M = 1.92, [1.04, 2.79]), and experimental ( M = 1.93 [1.45, 2.41]) averaged less than two years to publication, shorter than the weighted average. In contrast, clinical ( M = 2.88, [2.20, 3.56]), social/personality ( M = 2.90, [1.92, 3.89]), school/educational ( M = 2.94, [1.78, 4.1]), industrial-organizational ( M = 3.00, [0.73, 5.27]), and quantitative ( M = 3.06, [2.35, 3.78]) all took longer, approximately three years.

Overall estimates and 95% confidence intervals (left panel) are derived from the weighted Cox regression model (see Table 3 ). Subfield estimates (right panel) are derived from the unweighted Kaplan-Meier regression model. In both plots, cumulative publication estimate = one minus survival function.

Note. Cumulative estimates and 95% confidence intervals are derived from the weighted Cox regression model, where cumulative publication estimate = one minus survival function. All estimates based on the full sample (denominator n = 910 for all percentages). See Fig 2 , left panel, for a visual presentation of these results.

Lastly, as a methodological check, we modeled our time-to-publication data and projected this trend into the future to estimate what percentage of PDs we might have missed by stopping after 7 years. More specifically, these models used the weighted estimates of how many dissertations were published each year as the outcome and time (years 0 to 7) as the predictor. A Poisson model containing quadratic and linear effects for time fit the data best. When projected into the future, this model estimated that an additional 7 dissertations would be published at 8–10 years post-dissertation (4, 2, and 1 PDs, respectively). From 11 years onward, estimates asymptotically approached and rounded down to zero, even cumulatively. Thus, our sampling frame appears to have captured virtually all (97.3%) of the dissertations that ultimately would be published. In other words, had the study been implemented for as long as necessary to capture all PDs, the data suggest that our primary result, the estimated percentage of dissertations published, would increase only modestly from 25.6% to 26.4%.

Scientific impact

As shown in Table 4 , PDs were cited an average of 3.65 times per year since publication, totaling 15.95 citations on average during the years captured by the study. There were significant variations by subfield in terms of both total and per-year citations. Specifically, PDs in cognitive ( M = 5.08 [1.33, 8.83]) and industrial-organizational ( M = 5.18 [0.80, 9.56]) were more highly cited, with over 5 citations/year. On the other end, fields that exhibited relatively lower (but still nontrivial) rates of citations/year included quantitative ( M = 1.42, [0.87, 1.97]), general/miscellaneous ( M = 1.46 [0.15, 2.78]), counseling ( M = 1.64 [0.63, 2.64]), developmental ( M = 2.82 [1.31, 4.32]), and social/personality ( M = 2.86 [2.05, 3.67]).

Note. Estimates are calculated from available years from the publication year (inclusive) through 2016. This sampling range naturally truncates the number of follow-up years available for dissertations published in later years (e.g., 7-year outcomes are available only for dissertations published in 2007 to 2009, n = 126).

The 250 PDs in our sample appeared in 186 different peer-reviewed outlets, including top-tier journals in general (e.g., Nature , Science ) and psychological (e.g., Psychological Science , Journal of Consulting and Clinical Psychology ) science. Notably, several PDs appeared in journals predominately representing professions or disciplines outside psychology (e.g., Public Health Nursing , Endocrinology ). The most common journal titles were all in relatively specialized areas of psychology (e.g., Applied Psychological Measurement , Brain Research ), tending to draw from experimental, social/personality, neuroscience, behavioral, and cognitive. Overall, however, dissertations were disseminated broadly, with no single journal “catching” more than five (2.0%) dissertations from our overall sample, and most journals publishing only one (0.4%).

As shown in Table 5 , PDs appeared in journals of moderate-to-high influence according to all five metrics used. Subfield differences were found for the IF, SNIP, and SJR ( p s < .01, but not in the 5-year IF or the AIS ( p s > .09). Specifically, neuroscience and cognitive PDs appeared in higher-IF journals ( M s = 4.47 [3.17, 5.78] and 3.86 [1.87, 5.86], respectively), while most others fell in the below-average IF, including those still within the 2+ range (clinical, social/personality, general/miscellaneous, developmental, and behavioral; M s = 2.14 to 2.45) and those in the 1–2 range (quantitative, school/educational, counseling, and industrial-organizational; M s = 1.27 to 1.71). Similarly, neuroscience ( M = 2.17 [1.68, 2.66]), cognitive ( M = 1.97 [1.22, 2.72]), and social/personality ( M = 1.65 [1.09, 2.21]) PDs appeared in higher-SJR journals, whereas behavioral, clinical, general/miscellaneous, quantitative, school/educational, industrial-organizational, and counseling PDs had lower SJRs ( M s = 0.51–1.21). Lastly, cognitive ( M = 1.61 [1.17, 2.05]) and social/personality ( M = 1.55 [1.18, 1.92]) were published in higher-SNIP journals, while clinical ( M = 1.28 [1.08, 1.47]), general/miscellaneous ( M = 1.19 [0.23, 2.15]), and counseling ( M = 0.57 [0.26, 0.89]) PDs appeared in journals with lower SNIPs.

The primary finding of this study was that only about one in four psychology Ph.D. dissertations in the U.S. was published in a peer-reviewed journal. Typically this occurred within 2–3 years after completing the dissertation. Despite variations across subfields, dissertation publication appears to be the exception not the rule. When dissertations were published, however, they were often highly cited and appeared in influential journals. The relatively high impact of published dissertations may reflect a gatekeeping effect, whereby only the highest quality or most significant contributions get published; or a refining effect, whereby the dissertation development and committee review process helps strengthen the contribution [ 1 , 46 ], increasing the likelihood and impact of publication. In other words, the dissertation process may add some value to doctoral research, and some doctoral research appears to add value to psychological science. A larger and more important question is why the vast majority of psychology dissertation research does not contribute to the peer-reviewed literature.

Our estimated rate of dissertation publication in psychology (25.6%) is similar to or slightly below a corresponding estimate for social work (28.8%) [ 22 ], the only field in which a similarly rigorous and comparable design had been used. To our knowledge, the present study is the first to offer a reliable estimate of publication rates specific to the dissertation and specific to psychology. Further, the present study advances the literature by demonstrating the impact that these published dissertations have on the scientific literature. Although it was only minority of cases, published dissertations in psychology were disseminated in moderate- to high-impact journals across a wide spectrum of disciplines and specialty interests. Whereas published dissertation articles were cited several times per year, anecdotally we saw very few citations to the actual dissertation documents in PQDT. These observations are consistent with evidence showing that the impact of dissertations themselves has declined markedly [ 7 – 8 ] in recent decades. In contrast, peer-reviewed journal articles are much more likely to be read, cited, and included in systematic and meta-analytic reviews.

Subfield differences were broadly consistent with hypotheses. Dissertations from professional/applied fields were less often published, whereas the more research/academic-oriented subfields published at rates much higher than average. These findings likely reflect differences in the nature of training and motivation in professional and scientific subfields, and also align with evidence about student research productivity in professional/applied subfields. For example, annual results from the Association of Psychology Postdoctoral and Internship Centers applicant survey indicate that only about 50% of advanced doctoral students in professional psychology have authored or co-authored any peer-reviewed publications, while only 10% have published 5 or more [ 18 ]. Given this relatively low baseline rate of productivity during graduate school for this population, the average likelihood of post-graduation publication seems low. On the other hand, individuals in more research-oriented subfields are often training specifically for an academic position which incentivizes publication. Further, lab-based dissertations often include multiple experiments, which may create more publishable units (this also may explain the relatively higher rate of publication in behavioral psychology). The low publication of dissertations in industrial-organizational (10%) is also interesting, and may reflect an applied focus, organizational propriety of data, greater non-academic incentives (e.g., higher salaries in industry), or limited generalizability as market or consultative research. When these and other types of applied/professional dissertations were published, however, they were often cited several times per year.

The time from dissertation completion to publication appears to be a critical consideration. From our main results and longitudinal projections, we can generalize that by two calendar-years post-dissertation, over 50% of ultimately-published dissertations will appear in print. After five years, this number increases to nearly 90% (10% probability of publication). After 7–10 years, the dissertation findings are likely to become outdated, irrelevant, or overturned [ 30 – 32 ], and the probability of publication approaches 0%. Thus, if students wish to publish their dissertation, it is recommended that they proactively develop a plan for adapting the full document into a manuscript (or multiple manuscripts) for publication [ 1 ]. As one example of this, we are aware of some universities that have begun requiring that approved dissertations be accompanied by a form that outlines an agreed-upon plan for publication and authorship.

The present findings raise questions about the reasons for nonpublication. Possible explanations include the burden of revising and submitting a lengthy document, or limited career incentives for pursuing publications in non-academic careers. Alternatively, unpublished dissertations may lack methodological rigor, including “fatal flaws,” or fail to make a novel and substantive contribution. Thus, unpublished dissertations might not pass the bar of peer review. The present results only illustrate how many dissertations were actually published, and cannot speak to how many students attempted to publish their dissertations, or how many dissertations might have been publishable quality. Similarly, these results do not provide direct evidence of the mechanisms underlying publication vs. nonpublication, but the apparently high quality of the published dissertation articles is consistent with the file drawer hypothesis. Interestingly, one recent study in management research found that in the path from dissertation to publication, studies appear to get “beautified,” for example, such that the ratio of supported to unsupported hypotheses more than doubles in at least one discipline [ 47 ]. Such questionable research practices may provide one explanation for how dissertations selectively get published, but this is clearly not an appropriate solution. Whatever the underlying explanations may be, the widespread non-publication of dissertation research is a problem in psychology. To the extent that this non-publication continues, it exacerbates the file drawer problem [ 5 – 6 , 9 ], biases systematic reviews and meta-analyses, and contributes to the replication problem in psychology [ 10 ]. It also amounts to inefficient use of time and resources, raising ethical questions about violating agreements with participants and funding agencies, and about the consequences of not disseminating research findings [ 2 , 4 ].

The present study was designed so that results could be generalized to the population of dissertations produced in U.S. psychology Ph.D. programs. However, some limitations should be noted. First, our stratified random sample was drawn from an archival data source (PQDT), which is an approximation of the population of dissertations in psychology (although a very comprehensive one) and a proxy of the boundaries delineating subfields in psychology. Our outcome variables were likewise drawn from various databases (e.g., PsycINFO, Google Scholar, Web of Science, Thomson Reuters) which are necessarily restricted in different ways. As noted in the Methods section, these databases were selected as the most comprehensive and appropriate sources available for the purposes for which they were used, and their strengths and weaknesses were considered in developing the study protocol.

A second constraint lies in the selection of a single cohort year (2007) and 7-year follow-up period, raising the possibilities of missed cases and of cohort/historical effects. The changing landscape of doctoral training in psychology (e.g., more competitive admissions, increasing emphasis on research productivity, nontraditional dissertation requirements) may limit generalizability to past and future populations. Of note, these results may not generalize broadly to other countries (e.g., Northern/Western Europe, Australia, and New Zealand) and fields (e.g., biomedical, natural, and physical sciences) that are increasingly using a dissertation-by-publication model [ 34 ]. From our read of the literature and our assessment of the present sample, this model has not been widely adopted in U.S. psychology, where more traditional dissertation documents are still the norm. Accordingly, our sampling and search strategies were designed to work reasonably well for all U.S. psychology dissertations, including a suspected minority of nontraditional models; however, we could not differentiate types of dissertations. For all of these reasons, periodic replication of these results would be useful. Nonetheless, the large sample size, stratified sampling method, comprehensive dataset, and thorough multi-stage search protocol help mitigate bias. Narrow 95% confidence intervals support the precision of the overall estimate, and post hoc analyses suggested that the results are unlikely to change substantially given a longer sampling frame.

Finally, we note that this study should not be interpreted as any sort of evaluation of students, advisors, or programs for dissertations that were or were not published. Nor are we advocating that all dissertations be published, regardless of quality. Rather, these findings shed light on what appears to be a systemic problem affecting research and training in all areas of psychology. Efforts aimed at increasing the quality and “publishability” of doctoral dissertation research may have broad benefits for both training and research in psychology. On the training side, these efforts may benefit students and graduates in terms of providing a higher standard of scientific training, more research/publishing experience, and greater early-career productivity. On the research side, such efforts can help promote a higher level of rigor in doctoral research and increase the likelihood that the findings will be shared with the scientific community.

Supporting information

Acknowledgments.

A portion of this research was presented at the 28th Annual Convention of the Association for Psychological Science, Chicago, IL, May 2016, where it received the APS Student Research Award. We thank Austin McLean and the staff of ProQuest Dissertations and Theses for providing the population dataset of dissertations, Patrick Edmonds for offering statistical consultation, Oliver Blossom for reviewing the manuscript, and the following members of our research team for their extensive coding efforts: Maggie Biberstein, Jamie Eschrich, Andrea Garcia, Mackenzie Klaver, Alexa Mallow, Alexandra Monzon, and Emma Rogers.

Funding Statement

The journal's publication fees were covered by an award from the One University Open Access Fund at the University of Kansas. The authors received no other specific funding for this work.

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How to Write a Dissertation | A Guide to Structure & Content

A dissertation or thesis is a long piece of academic writing based on original research, submitted as part of an undergraduate or postgraduate degree.

The structure of a dissertation depends on your field, but it is usually divided into at least four or five chapters (including an introduction and conclusion chapter).

The most common dissertation structure in the sciences and social sciences includes:

• An introduction to your topic
• A literature review that surveys relevant sources
• An explanation of your methodology
• An overview of the results of your research
• A discussion of the results and their implications
• A conclusion that shows what your research has contributed

Dissertations in the humanities are often structured more like a long essay , building an argument by analysing primary and secondary sources . Instead of the standard structure outlined here, you might organise your chapters around different themes or case studies.

Other important elements of the dissertation include the title page , abstract , and reference list . If in doubt about how your dissertation should be structured, always check your department’s guidelines and consult with your supervisor.

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Table of contents

Acknowledgements, table of contents, list of figures and tables, list of abbreviations, introduction, literature review / theoretical framework, methodology, reference list.

The very first page of your document contains your dissertation’s title, your name, department, institution, degree program, and submission date. Sometimes it also includes your student number, your supervisor’s name, and the university’s logo. Many programs have strict requirements for formatting the dissertation title page .

The title page is often used as cover when printing and binding your dissertation .

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The acknowledgements section is usually optional, and gives space for you to thank everyone who helped you in writing your dissertation. This might include your supervisors, participants in your research, and friends or family who supported you.

The abstract is a short summary of your dissertation, usually about 150-300 words long. You should write it at the very end, when you’ve completed the rest of the dissertation. In the abstract, make sure to:

• State the main topic and aims of your research
• Describe the methods you used
• Summarise the main results
• State your conclusions

Although the abstract is very short, it’s the first part (and sometimes the only part) of your dissertation that people will read, so it’s important that you get it right. If you’re struggling to write a strong abstract, read our guide on how to write an abstract .

In the table of contents, list all of your chapters and subheadings and their page numbers. The dissertation contents page gives the reader an overview of your structure and helps easily navigate the document.

All parts of your dissertation should be included in the table of contents, including the appendices. You can generate a table of contents automatically in Word.

If you have used a lot of tables and figures in your dissertation, you should itemise them in a numbered list . You can automatically generate this list using the Insert Caption feature in Word.

If you have used a lot of abbreviations in your dissertation, you can include them in an alphabetised list of abbreviations so that the reader can easily look up their meanings.

If you have used a lot of highly specialised terms that will not be familiar to your reader, it might be a good idea to include a glossary . List the terms alphabetically and explain each term with a brief description or definition.

In the introduction, you set up your dissertation’s topic, purpose, and relevance, and tell the reader what to expect in the rest of the dissertation. The introduction should:

• Establish your research topic , giving necessary background information to contextualise your work
• Narrow down the focus and define the scope of the research
• Discuss the state of existing research on the topic, showing your work’s relevance to a broader problem or debate
• Clearly state your objectives and research questions , and indicate how you will answer them
• Give an overview of your dissertation’s structure

Everything in the introduction should be clear, engaging, and relevant to your research. By the end, the reader should understand the what , why and how of your research. Not sure how? Read our guide on how to write a dissertation introduction .

Before you start on your research, you should have conducted a literature review to gain a thorough understanding of the academic work that already exists on your topic. This means:

• Collecting sources (e.g. books and journal articles) and selecting the most relevant ones
• Critically evaluating and analysing each source
• Drawing connections between them (e.g. themes, patterns, conflicts, gaps) to make an overall point

In the dissertation literature review chapter or section, you shouldn’t just summarise existing studies, but develop a coherent structure and argument that leads to a clear basis or justification for your own research. For example, it might aim to show how your research:

• Addresses a gap in the literature
• Takes a new theoretical or methodological approach to the topic
• Proposes a solution to an unresolved problem
• Advances a theoretical debate
• Builds on and strengthens existing knowledge with new data

The literature review often becomes the basis for a theoretical framework , in which you define and analyse the key theories, concepts and models that frame your research. In this section you can answer descriptive research questions about the relationship between concepts or variables.

The methodology chapter or section describes how you conducted your research, allowing your reader to assess its validity. You should generally include:

• The overall approach and type of research (e.g. qualitative, quantitative, experimental, ethnographic)
• Your methods of collecting data (e.g. interviews, surveys, archives)
• Details of where, when, and with whom the research took place
• Your methods of analysing data (e.g. statistical analysis, discourse analysis)
• Tools and materials you used (e.g. computer programs, lab equipment)
• A discussion of any obstacles you faced in conducting the research and how you overcame them
• An evaluation or justification of your methods

Your aim in the methodology is to accurately report what you did, as well as convincing the reader that this was the best approach to answering your research questions or objectives.

Next, you report the results of your research . You can structure this section around sub-questions, hypotheses, or topics. Only report results that are relevant to your objectives and research questions. In some disciplines, the results section is strictly separated from the discussion, while in others the two are combined.

For example, for qualitative methods like in-depth interviews, the presentation of the data will often be woven together with discussion and analysis, while in quantitative and experimental research, the results should be presented separately before you discuss their meaning. If you’re unsure, consult with your supervisor and look at sample dissertations to find out the best structure for your research.

In the results section it can often be helpful to include tables, graphs and charts. Think carefully about how best to present your data, and don’t include tables or figures that just repeat what you have written  –  they should provide extra information or usefully visualise the results in a way that adds value to your text.

Full versions of your data (such as interview transcripts) can be included as an appendix .

The discussion  is where you explore the meaning and implications of your results in relation to your research questions. Here you should interpret the results in detail, discussing whether they met your expectations and how well they fit with the framework that you built in earlier chapters. If any of the results were unexpected, offer explanations for why this might be. It’s a good idea to consider alternative interpretations of your data and discuss any limitations that might have influenced the results.

The discussion should reference other scholarly work to show how your results fit with existing knowledge. You can also make recommendations for future research or practical action.

The dissertation conclusion should concisely answer the main research question, leaving the reader with a clear understanding of your central argument. Wrap up your dissertation with a final reflection on what you did and how you did it. The conclusion often also includes recommendations for research or practice.

In this section, it’s important to show how your findings contribute to knowledge in the field and why your research matters. What have you added to what was already known?

You must include full details of all sources that you have cited in a reference list (sometimes also called a works cited list or bibliography). It’s important to follow a consistent reference style . Each style has strict and specific requirements for how to format your sources in the reference list.

The most common styles used in UK universities are Harvard referencing and Vancouver referencing . Your department will often specify which referencing style you should use – for example, psychology students tend to use APA style , humanities students often use MHRA , and law students always use OSCOLA . M ake sure to check the requirements, and ask your supervisor if you’re unsure.

To save time creating the reference list and make sure your citations are correctly and consistently formatted, you can use our free APA Citation Generator .

Your dissertation itself should contain only essential information that directly contributes to answering your research question. Documents you have used that do not fit into the main body of your dissertation (such as interview transcripts, survey questions or tables with full figures) can be added as appendices .

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OATD.org aims to be the best possible resource for finding open access graduate theses and dissertations published around the world. Metadata (information about the theses) comes from over 1100 colleges, universities, and research institutions . OATD currently indexes 7,241,108 theses and dissertations.

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You may also want to consult these sites to search for other theses:

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• CAREER FEATURE
• 04 December 2020
• Correction 09 December 2020

How to write a superb literature review

Andy Tay is a freelance writer based in Singapore.

You can also search for this author in PubMed   Google Scholar

Literature reviews are important resources for scientists. They provide historical context for a field while offering opinions on its future trajectory. Creating them can provide inspiration for one’s own research, as well as some practice in writing. But few scientists are trained in how to write a review — or in what constitutes an excellent one. Even picking the appropriate software to use can be an involved decision (see ‘Tools and techniques’). So Nature asked editors and working scientists with well-cited reviews for their tips.

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doi: https://doi.org/10.1038/d41586-020-03422-x

Interviews have been edited for length and clarity.

Updates & Corrections

Correction 09 December 2020 : An earlier version of the tables in this article included some incorrect details about the programs Zotero, Endnote and Manubot. These have now been corrected.

Hsing, I.-M., Xu, Y. & Zhao, W. Electroanalysis 19 , 755–768 (2007).

Article   Google Scholar  

Ledesma, H. A. et al. Nature Nanotechnol. 14 , 645–657 (2019).

Article   PubMed   Google Scholar  

Brahlek, M., Koirala, N., Bansal, N. & Oh, S. Solid State Commun. 215–216 , 54–62 (2015).

Choi, Y. & Lee, S. Y. Nature Rev. Chem . https://doi.org/10.1038/s41570-020-00221-w (2020).

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
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• How to Report Statistics
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Dissertation Structure & Layout 101: How to structure your dissertation, thesis or research project.

By: Derek Jansen (MBA) Reviewed By: David Phair (PhD) | July 2019

So, you’ve got a decent understanding of what a dissertation is , you’ve chosen your topic and hopefully you’ve received approval for your research proposal . Awesome! Now its time to start the actual dissertation or thesis writing journey.

To craft a high-quality document, the very first thing you need to understand is dissertation structure . In this post, we’ll walk you through the generic dissertation structure and layout, step by step. We’ll start with the big picture, and then zoom into each chapter to briefly discuss the core contents. If you’re just starting out on your research journey, you should start with this post, which covers the big-picture process of how to write a dissertation or thesis .

*The Caveat *

In this post, we’ll be discussing a traditional dissertation/thesis structure and layout, which is generally used for social science research across universities, whether in the US, UK, Europe or Australia. However, some universities may have small variations on this structure (extra chapters, merged chapters, slightly different ordering, etc).

So, always check with your university if they have a prescribed structure or layout that they expect you to work with. If not, it’s safe to assume the structure we’ll discuss here is suitable. And even if they do have a prescribed structure, you’ll still get value from this post as we’ll explain the core contents of each section.  

Overview: S tructuring a dissertation or thesis

• Acknowledgements page
• Abstract (or executive summary)
• Table of contents , list of figures and tables
• Chapter 1: Introduction
• Chapter 2: Literature review
• Chapter 3: Methodology
• Chapter 4: Results
• Chapter 5: Discussion
• Chapter 6: Conclusion
• Reference list

As I mentioned, some universities will have slight variations on this structure. For example, they want an additional “personal reflection chapter”, or they might prefer the results and discussion chapter to be merged into one. Regardless, the overarching flow will always be the same, as this flow reflects the research process , which we discussed here – i.e.:

• The introduction chapter presents the core research question and aims .
• The literature review chapter assesses what the current research says about this question.
• The methodology, results and discussion chapters go about undertaking new research about this question.
• The conclusion chapter (attempts to) answer the core research question .

In other words, the dissertation structure and layout reflect the research process of asking a well-defined question(s), investigating, and then answering the question – see below.

To restate that – the structure and layout of a dissertation reflect the flow of the overall research process . This is essential to understand, as each chapter will make a lot more sense if you “get” this concept. If you’re not familiar with the research process, read this post before going further.

Right. Now that we’ve covered the big picture, let’s dive a little deeper into the details of each section and chapter. Oh and by the way, you can also grab our free dissertation/thesis template here to help speed things up.

The title page of your dissertation is the very first impression the marker will get of your work, so it pays to invest some time thinking about your title. But what makes for a good title? A strong title needs to be 3 things:

• Succinct (not overly lengthy or verbose)
• Specific (not vague or ambiguous)
• Representative of the research you’re undertaking (clearly linked to your research questions)

Typically, a good title includes mention of the following:

• The broader area of the research (i.e. the overarching topic)
• The specific focus of your research (i.e. your specific context)
• Indication of research design (e.g. quantitative , qualitative , or  mixed methods ).

For example:

A quantitative investigation [research design] into the antecedents of organisational trust [broader area] in the UK retail forex trading market [specific context/area of focus].

Again, some universities may have specific requirements regarding the format and structure of the title, so it’s worth double-checking expectations with your institution (if there’s no mention in the brief or study material).

Acknowledgements

This page provides you with an opportunity to say thank you to those who helped you along your research journey. Generally, it’s optional (and won’t count towards your marks), but it is academic best practice to include this.

So, who do you say thanks to? Well, there’s no prescribed requirements, but it’s common to mention the following people:

• Your dissertation supervisor or committee.
• Any professors, lecturers or academics that helped you understand the topic or methodologies.
• Any tutors, mentors or advisors.
• Your family and friends, especially spouse (for adult learners studying part-time).

There’s no need for lengthy rambling. Just state who you’re thankful to and for what (e.g. thank you to my supervisor, John Doe, for his endless patience and attentiveness) – be sincere. In terms of length, you should keep this to a page or less.

Abstract or executive summary

The dissertation abstract (or executive summary for some degrees) serves to provide the first-time reader (and marker or moderator) with a big-picture view of your research project. It should give them an understanding of the key insights and findings from the research, without them needing to read the rest of the report – in other words, it should be able to stand alone .

For it to stand alone, your abstract should cover the following key points (at a minimum):

• Your research questions and aims – what key question(s) did your research aim to answer?
• Your methodology – how did you go about investigating the topic and finding answers to your research question(s)?
• Your findings – following your own research, what did do you discover?
• Your conclusions – based on your findings, what conclusions did you draw? What answers did you find to your research question(s)?

So, in much the same way the dissertation structure mimics the research process, your abstract or executive summary should reflect the research process, from the initial stage of asking the original question to the final stage of answering that question.

In practical terms, it’s a good idea to write this section up last , once all your core chapters are complete. Otherwise, you’ll end up writing and rewriting this section multiple times (just wasting time). For a step by step guide on how to write a strong executive summary, check out this post .

Need a helping hand?

Table of contents

This section is straightforward. You’ll typically present your table of contents (TOC) first, followed by the two lists – figures and tables. I recommend that you use Microsoft Word’s automatic table of contents generator to generate your TOC. If you’re not familiar with this functionality, the video below explains it simply:

If you find that your table of contents is overly lengthy, consider removing one level of depth. Oftentimes, this can be done without detracting from the usefulness of the TOC.

Right, now that the “admin” sections are out of the way, its time to move on to your core chapters. These chapters are the heart of your dissertation and are where you’ll earn the marks. The first chapter is the introduction chapter – as you would expect, this is the time to introduce your research…

It’s important to understand that even though you’ve provided an overview of your research in your abstract, your introduction needs to be written as if the reader has not read that (remember, the abstract is essentially a standalone document). So, your introduction chapter needs to start from the very beginning, and should address the following questions:

• What will you be investigating (in plain-language, big picture-level)?
• Why is that worth investigating? How is it important to academia or business? How is it sufficiently original?
• What are your research aims and research question(s)? Note that the research questions can sometimes be presented at the end of the literature review (next chapter).
• What is the scope of your study? In other words, what will and won’t you cover ?
• How will you approach your research? In other words, what methodology will you adopt?
• How will you structure your dissertation? What are the core chapters and what will you do in each of them?

These are just the bare basic requirements for your intro chapter. Some universities will want additional bells and whistles in the intro chapter, so be sure to carefully read your brief or consult your research supervisor.

If done right, your introduction chapter will set a clear direction for the rest of your dissertation. Specifically, it will make it clear to the reader (and marker) exactly what you’ll be investigating, why that’s important, and how you’ll be going about the investigation. Conversely, if your introduction chapter leaves a first-time reader wondering what exactly you’ll be researching, you’ve still got some work to do.

Now that you’ve set a clear direction with your introduction chapter, the next step is the literature review . In this section, you will analyse the existing research (typically academic journal articles and high-quality industry publications), with a view to understanding the following questions:

• What does the literature currently say about the topic you’re investigating?
• Is the literature lacking or well established? Is it divided or in disagreement?
• How does your research fit into the bigger picture?
• How does your research contribute something original?
• How does the methodology of previous studies help you develop your own?

Depending on the nature of your study, you may also present a conceptual framework towards the end of your literature review, which you will then test in your actual research.

Again, some universities will want you to focus on some of these areas more than others, some will have additional or fewer requirements, and so on. Therefore, as always, its important to review your brief and/or discuss with your supervisor, so that you know exactly what’s expected of your literature review chapter.

Now that you’ve investigated the current state of knowledge in your literature review chapter and are familiar with the existing key theories, models and frameworks, its time to design your own research. Enter the methodology chapter – the most “science-ey” of the chapters…

In this chapter, you need to address two critical questions:

• Exactly HOW will you carry out your research (i.e. what is your intended research design)?
• Exactly WHY have you chosen to do things this way (i.e. how do you justify your design)?

Remember, the dissertation part of your degree is first and foremost about developing and demonstrating research skills . Therefore, the markers want to see that you know which methods to use, can clearly articulate why you’ve chosen then, and know how to deploy them effectively.

Importantly, this chapter requires detail – don’t hold back on the specifics. State exactly what you’ll be doing, with who, when, for how long, etc. Moreover, for every design choice you make, make sure you justify it.

In practice, you will likely end up coming back to this chapter once you’ve undertaken all your data collection and analysis, and revise it based on changes you made during the analysis phase. This is perfectly fine. Its natural for you to add an additional analysis technique, scrap an old one, etc based on where your data lead you. Of course, I’m talking about small changes here – not a fundamental switch from qualitative to quantitative, which will likely send your supervisor in a spin!

You’ve now collected your data and undertaken your analysis, whether qualitative, quantitative or mixed methods. In this chapter, you’ll present the raw results of your analysis . For example, in the case of a quant study, you’ll present the demographic data, descriptive statistics, inferential statistics , etc.

Typically, Chapter 4 is simply a presentation and description of the data, not a discussion of the meaning of the data. In other words, it’s descriptive, rather than analytical – the meaning is discussed in Chapter 5. However, some universities will want you to combine chapters 4 and 5, so that you both present and interpret the meaning of the data at the same time. Check with your institution what their preference is.

Now that you’ve presented the data analysis results, its time to interpret and analyse them. In other words, its time to discuss what they mean, especially in relation to your research question(s).

What you discuss here will depend largely on your chosen methodology. For example, if you’ve gone the quantitative route, you might discuss the relationships between variables . If you’ve gone the qualitative route, you might discuss key themes and the meanings thereof. It all depends on what your research design choices were.

Most importantly, you need to discuss your results in relation to your research questions and aims, as well as the existing literature. What do the results tell you about your research questions? Are they aligned with the existing research or at odds? If so, why might this be? Dig deep into your findings and explain what the findings suggest, in plain English.

The final chapter – you’ve made it! Now that you’ve discussed your interpretation of the results, its time to bring it back to the beginning with the conclusion chapter . In other words, its time to (attempt to) answer your original research question s (from way back in chapter 1). Clearly state what your conclusions are in terms of your research questions. This might feel a bit repetitive, as you would have touched on this in the previous chapter, but its important to bring the discussion full circle and explicitly state your answer(s) to the research question(s).

Next, you’ll typically discuss the implications of your findings . In other words, you’ve answered your research questions – but what does this mean for the real world (or even for academia)? What should now be done differently, given the new insight you’ve generated?

Lastly, you should discuss the limitations of your research, as well as what this means for future research in the area. No study is perfect, especially not a Masters-level. Discuss the shortcomings of your research. Perhaps your methodology was limited, perhaps your sample size was small or not representative, etc, etc. Don’t be afraid to critique your work – the markers want to see that you can identify the limitations of your work. This is a strength, not a weakness. Be brutal!

This marks the end of your core chapters – woohoo! From here on out, it’s pretty smooth sailing.

The reference list is straightforward. It should contain a list of all resources cited in your dissertation, in the required format, e.g. APA , Harvard, etc.

It’s essential that you use reference management software for your dissertation. Do NOT try handle your referencing manually – its far too error prone. On a reference list of multiple pages, you’re going to make mistake. To this end, I suggest considering either Mendeley or Zotero. Both are free and provide a very straightforward interface to ensure that your referencing is 100% on point. I’ve included a simple how-to video for the Mendeley software (my personal favourite) below:

Some universities may ask you to include a bibliography, as opposed to a reference list. These two things are not the same . A bibliography is similar to a reference list, except that it also includes resources which informed your thinking but were not directly cited in your dissertation. So, double-check your brief and make sure you use the right one.

The very last piece of the puzzle is the appendix or set of appendices. This is where you’ll include any supporting data and evidence. Importantly, supporting is the keyword here.

Your appendices should provide additional “nice to know”, depth-adding information, which is not critical to the core analysis. Appendices should not be used as a way to cut down word count (see this post which covers how to reduce word count ). In other words, don’t place content that is critical to the core analysis here, just to save word count. You will not earn marks on any content in the appendices, so don’t try to play the system!

Time to recap…

And there you have it – the traditional dissertation structure and layout, from A-Z. To recap, the core structure for a dissertation or thesis is (typically) as follows:

• Acknowledgments page

Most importantly, the core chapters should reflect the research process (asking, investigating and answering your research question). Moreover, the research question(s) should form the golden thread throughout your dissertation structure. Everything should revolve around the research questions, and as you’ve seen, they should form both the start point (i.e. introduction chapter) and the endpoint (i.e. conclusion chapter).

I hope this post has provided you with clarity about the traditional dissertation/thesis structure and layout. If you have any questions or comments, please leave a comment below, or feel free to get in touch with us. Also, be sure to check out the rest of the  Grad Coach Blog .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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36 Comments

many thanks i found it very useful

Glad to hear that, Arun. Good luck writing your dissertation.

Such clear practical logical advice. I very much needed to read this to keep me focused in stead of fretting.. Perfect now ready to start my research!

what about scientific fields like computer or engineering thesis what is the difference in the structure? thank you very much

Thanks so much this helped me a lot!

Very helpful and accessible. What I like most is how practical the advice is along with helpful tools/ links.

Thanks Ade!

Thank you so much sir.. It was really helpful..

You’re welcome!

Hi! How many words maximum should contain the abstract?

Thank you so much 😊 Find this at the right moment

You’re most welcome. Good luck with your dissertation.

best ever benefit i got on right time thank you

Many times Clarity and vision of destination of dissertation is what makes the difference between good ,average and great researchers the same way a great automobile driver is fast with clarity of address and Clear weather conditions .

I guess Great researcher = great ideas + knowledge + great and fast data collection and modeling + great writing + high clarity on all these

You have given immense clarity from start to end.

Morning. Where will I write the definitions of what I’m referring to in my report?

Thank you so much Derek, I was almost lost! Thanks a tonnnn! Have a great day!

Thanks ! so concise and valuable

This was very helpful. Clear and concise. I know exactly what to do now.

Thank you for allowing me to go through briefly. I hope to find time to continue.

Really useful to me. Thanks a thousand times

Very interesting! It will definitely set me and many more for success. highly recommended.

Thank you soo much sir, for the opportunity to express my skills

Usefull, thanks a lot. Really clear

Very nice and easy to understand. Thank you .

That was incredibly useful. Thanks Grad Coach Crew!

My stress level just dropped at least 15 points after watching this. Just starting my thesis for my grad program and I feel a lot more capable now! Thanks for such a clear and helpful video, Emma and the GradCoach team!

Do we need to mention the number of words the dissertation contains in the main document?

It depends on your university’s requirements, so it would be best to check with them 🙂

Such a helpful post to help me get started with structuring my masters dissertation, thank you!

Great video; I appreciate that helpful information

It is so necessary or avital course

This blog is very informative for my research. Thank you

Doctoral students are required to fill out the National Research Council’s Survey of Earned Doctorates

wow this is an amazing gain in my life

This is so good

How can i arrange my specific objectives in my dissertation?

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Web of Science: Introducing the New ProQuest Dissertations & Theses Citation Index

• Can maximize the results of limited research time by providing a comprehensive research experience where faculty and students can browse abstracts of early career research alongside records for journal articles, conference papers, preprints and other scholarly sources within a single platform.
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In addition, in this session you will:

• Discover why Web of Science users value dissertations.
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• Time for Q&A will be included.

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This document originally came from the Journal of Mammalogy courtesy of Dr. Ronald Barry, a former editor of the journal.

AI-assisted writing is quietly booming in academic journals. Here’s why that’s OK

Lecturer in Bioethics, Monash University & Honorary fellow, Melbourne Law School, Monash University

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Julian Koplin does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Monash University provides funding as a founding partner of The Conversation AU.

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If you search Google Scholar for the phrase “ as an AI language model ”, you’ll find plenty of AI research literature and also some rather suspicious results. For example, one paper on agricultural technology says:

As an AI language model, I don’t have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements …

Obvious gaffes like this aren’t the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A recent study examined the frequency of certain words in academic writing (such as “commendable”, “meticulously” and “intricate”), and found they became far more common after the launch of ChatGPT – so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.

(Why do AI models overuse these words? There is speculation it’s because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?

Who should take credit for AI writing?

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as “ contaminating ” scholarly literature.

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.

But there are important differences between “plagiarising” text authored by humans and text authored by AI. Those who plagiarise humans’ work receive credit for ideas that ought to have gone to the original author.

By contrast, it is debatable whether AI systems like ChatGPT can have ideas, let alone deserve credit for them. An AI tool is more like your phone’s autocomplete function than a human researcher.

The question of bias

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models tended to portray people who are female, black and/or gay in distinctly unflattering ways, compared with people who are male, white and/or straight.

This kind of bias is less pronounced in the current version of ChatGPT.

However, other studies have found a different kind of bias in ChatGPT and other large language models : a tendency to reflect a left-liberal political ideology.

Any such bias could subtly distort scholarly writing produced using these tools.

The hallucination problem

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.

For example, when I asked ChatGPT-4 to generate an ASCII image of a mushroom, it provided me with the following output.

It then confidently told me I could use this image of a “mushroom” for my own purposes.

These kinds of overconfident mistakes have been referred to as “ AI hallucinations ” and “ AI bullshit ”. While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when surveying scientific literature or describing the state of a philosophical debate.

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.

Should AI-produced text be banned?

One response to the rise of text generators has been to ban them outright. For example, Science – one of the world’s most influential academic journals – disallows any use of AI-generated text .

I see two problems with this approach.

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT’s own developers, which was taken offline after it was found to have only a 26% accuracy rate (and a 9% false positive rate ). Humans also make mistakes when assessing whether something was written by AI.

It is also possible to circumvent AI text detectors. Online communities are actively exploring how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words “commendable”, “meticulously” and “intricate”).

The second problem is that banning generative AI outright prevents us from realising these technologies’ benefits. Used well, generative AI can boost academic productivity by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.

The problem is poor quality control, not AI

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.

After all, humans can also produce writing with serious errors, and mechanisms such as peer review often fail to prevent its publication.

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.

• Artificial intelligence (AI)
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Journal of Materials Chemistry C

Transition metal-doped srtio 3 : when does a tiny chemical impact have such a great structural response.

The effect of doping on the chemical and physical properties of semiconductors, alloys, ferroelectrics, glasses, and other substances has been a classic topic in materials science for centuries. Strontium titanate, SrTiO 3 , is an archetypal perovskite of interest for both fundamental science as quantum paraelectric and numerous outstanding physical properties and applications, including dielectrics, tunable microwave and photovoltaic devices, superconductors, thermoelectrics, potential multiferroics. Its chemical doping with transition metals leads to new functionalities, but intrinsic mechanisms of structural responses, activated by impurities, have not been systematically investigated. Herein, we present the results of a comparative study of the crystal structure, vibrational spectra, and dielectric properties of SrTiO 3 :M (M = Mn, Ni, and Fe, 2 at. %) single crystals. It is shown that impurities constitute a different tendency to off-centering and the formation of dipoles: Mn and Fe atoms are shifted from the center of the oxygen octahedron, while Ni atoms remain on-centered. As a result, small chemical doping has a dramatic effect on the dielectric response through various structural mechanisms, including the pseudo Jahn-Teller effect, the first-order Jahn-Teller effect, and defect-induced distortion. These findings open up fundamentally new possibilities for the practical solution of a difficult problem: controlling the dielectric responses of quantum paraelectrics by choosing the type of chemical additive.

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M. V. Talanov, A. I. Stash, S. Ivanov, E. Zhukova, B. Gorshunov, B. Nekrasov, A. V. Melentev, V. I. Kozlov, V. Cherepanov, S. Yu. Gavrilkin, A. Yu. Tsvetkov, M. Savinov, V. Talanov and A. A. Bush, J. Mater. Chem. C , 2024, Accepted Manuscript , DOI: 10.1039/D4TC00180J

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• MyU : For Students, Faculty, and Staff

CS&E Announces 2024-25 Doctoral Dissertation Fellowship (DDF) Award Winners

Seven Ph.D. students working with CS&E professors have been named Doctoral Dissertation Fellows for the 2024-25 school year. The Doctoral Dissertation Fellowship is a highly competitive fellowship that gives the University’s most accomplished Ph.D. candidates an opportunity to devote full-time effort to an outstanding research project by providing time to finalize and write a dissertation during the fellowship year. The award includes a stipend of $25,000, tuition for up to 14 thesis credits each semester, and subsidized health insurance through the Graduate Assistant Health Plan.

CS&E congratulates the following students on this outstanding accomplishment:

• Athanasios Bacharis (Advisor: Nikolaos Papanikolopoulos )
• Karin de Langis (Advisor:  Dongyeop Kang )
• Arshia Zernab Hassan (Advisors: Chad Myers )
• Xinyue Hu (Advisors: Zhi-Li Zhang )
• Lucas Kramer (Advisors: Eric Van Wyk )
• Yijun Lin (Advisors: Yao-Yi Chiang )
• Mingzhou Yang (Advisors: Shashi Shekhar )

Athanasios Bacharis

Bacharis’ work centers around the robot-vision area, focusing on making autonomous robots act on visual information. His research includes active vision approaches, namely, view planning and next-best-view, to tackle the problem of 3D reconstruction via different optimization frameworks. The acquisition of 3D information is crucial for automating tasks, and active vision methods obtain it via optimal inference. Areas of impact include agriculture and healthcare, where 3D models can lead to reduced use of fertilizers via phenotype analysis of crops and effective management of cancer treatments. Bacharis has a strong publication record, with two peer-reviewed conference papers and one journal paper already published. He also has one conference paper under review and two journal papers in the submission process. His publications are featured in prestigious robotic and automation venues, further demonstrating his expertise and the relevance of his research in the field.

Karin de Langis

Karin's thesis works at the intersection of Natural Language Processing (NLP) and cognitive science. Her work uses eye-tracking and other cognitive signals to improve NLP systems in their performance and cognitive interpretability, and to create NLP systems that process language more similarly to humans. Her human-centric approach to NLP is motivated by the possibility of addressing the shortcomings of current statistics-based NLP systems, which often become stuck on explainability and interpretability, resulting in potential biases. This work has most recently been accepted and presented at SIGNLL Conference on Computational Natural Language Learning (CoNLL) conference which has a special focus on theoretically, cognitively and scientifically motivated approaches to computational linguistics.

Arshia Zernab Hassan

Hassan's thesis work delves into developing computational methods for interpreting data from genome wide CRISPR/Cas9 screens. CRISPR/Cas9 is a new approach for genome editing that enables precise, large-scale editing of genomes and construction of mutants in human cells. These are powerful data for inferring functional relationships among genes essential for cancer growth. Moreover, chemical-genetic CRISPR screens, where population of mutant cells are grown in the presence of chemical compounds, help us understand the effect the chemicals have on cancer cells and formulate precise drug solutions. Given the novelty of these experimental technologies, computational methods to process and interpret the resulting data and accurately quantify the various genetic interactions are still quite limited, and this is where Hassan’s dissertation is focused on. Her research extends to developing deep-learning based methods that leverage CRISPR chemical-genetic and other genomic datasets to predict cancer sensitivity to candidate drugs. Her methods on improving information content in CRISPR screens was published in the Molecular Systems Biology journal, a highly visible journal in the computational biology field. 

Hu's Ph.D. dissertation is concentrated on how to effectively leverage the power of artificial intelligence and machine learning (AI/ML) – especially deep learning – to tackle challenging and important problems in the design and development of reliable, effective and secure (independent) physical infrastructure networks. More specifically, her research focuses on two critical infrastructures: power grids and communication networks, in particular, emerging 5G networks, both of which not only play a critical role in our daily life but are also vital to the nation’s economic well-being and security. Due to the enormous complexity, diversity, and scale of these two infrastructures, traditional approaches based on (simplified) theoretical models and heuristics-based optimization are no longer sufficient in overcoming many technical challenges in the design and operations of these infrastructures: data-driven machine learning approaches have become increasingly essential. The key question now is: how does one leverage the power of AI/ML without abandoning the rich theory and practical expertise that have accumulated over the years? Hu’s research has pioneered a new paradigm – (domain) knowledge-guided machine learning (KGML) – in tackling challenging and important problems in power grid and communications (e.g., 5G) network infrastructures.

Lucas Kramer

Kramer is now the driving force in designing tools and techniques for building extensible programming languages, with the Minnesota Extensible Language Tools (MELT) group. These are languages that start with a host language such as C or Java, but can then be extended with new syntax (notations) and new semantics (e.g. error-checking analyses or optimizations) over that new syntax and the original host language syntax. One extension that Kramer created was to embed the domain-specific language Halide in MELT's extensible specification of C, called ableC. This extension allows programmers to specify how code working on multi-dimensional matrices is transformed and optimized to make efficient use of hardware. Another embeds the logic-programming language Prolog into ableC; yet another provides a form of nondeterministic parallelism useful in some algorithms that search for a solution in a structured, but very large, search space. The goal of his research is to make building language extensions such as these more practical for non-expert developers.  To this end he has made many significant contributions to the MELT group's Silver meta-language, making it easier for extension developers to correctly specify complex language features with minimal boilerplate. Kramer is the lead author of one journal and four conference papers on his work at the University of Minnesota, winning the distinguished paper award for his 2020 paper at the Software Language Engineering conference, "Strategic Tree Rewriting in Attribute Grammars".

Lin’s doctoral dissertation focuses on a timely, important topic of spatiotemporal prediction and forecasting using multimodal and multiscale data. Spatiotemporal prediction and forecasting are important scientific problems applicable to diverse phenomena, such as air quality, ambient noise, traffic conditions, and meteorology. Her work also couples the resulting prediction and forecasting with multimodal (e.g., satellite imagery, street-view photos, census records, and human mobility data) and multiscale geographic information (e.g., census records focusing on small tracts vs. neighborhood surveys) to characterize the natural and built environment, facilitating our understanding of the interactions between and within human social systems and the ecosystem. Her work has a wide-reaching impact across multiple domains such as smart cities, urban planning, policymaking, and public health.

Mingzhou Yang

Yang is developing a thesis in the broad area of spatial data mining for problems in transportation. His thesis has both societal and theoretical significance. Societally, climate change is a grand challenge due to the increasing severity and frequency of climate-related disasters such as wildfires, floods, droughts, etc. Thus, many nations are aiming at carbon neutrality (also called net zero) by mid-century to avert the worst impacts of global warming. Improving energy efficiency and reducing toxic emissions in transportation is important because transportation accounts for the vast majority of U.S. petroleum consumption as well as over a third of GHG emissions and over a hundred thousand U.S. deaths annually via air pollution. To accurately quantify the expected environmental cost of vehicles during real-world driving, Yang's thesis explores ways to incorporate physics in the neural network architecture complementing other methods of integration: feature incorporation, and regularization. This approach imposes stringent physical constraints on the neural network model, guaranteeing that its outputs are consistently in accordance with established physical laws for vehicles. Extensive experiments including ablation studies demonstrated the efficacy of incorporating physics into the model. 

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