Research based on traditional scientific methods, which generates numerical data and usually seeks to establish causal relationships between two or more variables, using statistical methods to test the strength and significance of the relationships.
Observations in
Observations in
A situation the researcher can observe
A
Participants are comfortable with the researcher. They are honest and forthcoming, so that the researcher can make robust observations.
Others can repeat the findings of the study
Variables are defined and correlations between them are studied
If the researcher is biased, or is expecting to find certain results, it can be difficult to make completely objective observations
Researchers may be so careful about measurement methods that they do not make connections to a greater context
Open-ended interviews
Focus groups
Observation
Participant observation
Close-ended interviews
Surveys
Clinical Trials
Laboratory Experiments
From A Dictionary of Nursing
Qualitative research includes all modes of inquiry that do not rely on numbers or statistical methods.
Naturalistic [qualitative] approaches comprise a wide array of research traditions, most often in the categories of ethnography, grounded theory, and phenomenology, but they also include ethnology, ethnomethodology, hermeneutics, oral and life histories, discourse analysis, case study methods, and critical, philosophical, and historical approaches to inquiry.
Learn more! Encyclopedia of Nursing Research
Finding qualitative studies can be slightly more challenging because this type of methodology is less commonly used in nursing research.
Try adding one of the following keywords to your search:
Look at the following qualitative article example for more search ideas:
Consider using one of the following when examining qualitative research:
Quantitative research consists of the collection, tabulation, summarization, and analysis of numerical data for the purpose of answering research questions or hypotheses.
Quantitative research uses statistical methodology at every stage in the research process. At the inception of a research project, when the research questions are formulated, thought must be given to how the research variables are to be quantified, defined, measured, and analyzed.
Learn more! Dictionary of Nursing Theory and Research
According to the Encyclopedia of Nursing Research, "The vast majority of all nursing studies can be classified as quantitative."
As a result, you'll likely find quantitative research articles when you search for your topic.
You can also try adding one of the following keywords to your search:
Look at the following quantitative article example for more search ideas.
Consider using one of the following when examining quantitative research:
If you're still wondering if the article you have is qualitative or quantitative, below you'll find a table that highlights some of the key differences in qualitative versus quantitative research methods.
Image from the Oak Ridge Institute for Science and Education .
Mixed methods research combines quantitative and qualitative research methods in a single study. The use of mixed methods research is increasingly popular in nursing and health sciences research. This growth in popularity has been driven by the increasing complexity of research problems relating to human health and wellbeing.
Mixed Methods Research for Nursing and Health Sciences
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Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing
* E-mail: [email protected]
Affiliation Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
Roles Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing
Identifying determinants of health and understanding their role in health production constitutes an important research theme. We aimed to document the state of recent multi-country research on this theme in the literature.
We followed the PRISMA-ScR guidelines to systematically identify, triage and review literature (January 2013—July 2019). We searched for studies that performed cross-national statistical analyses aiming to evaluate the impact of one or more aggregate level determinants on one or more general population health outcomes in high-income countries. To assess in which combinations and to what extent individual (or thematically linked) determinants had been studied together, we performed multidimensional scaling and cluster analysis.
Sixty studies were selected, out of an original yield of 3686. Life-expectancy and overall mortality were the most widely used population health indicators, while determinants came from the areas of healthcare, culture, politics, socio-economics, environment, labor, fertility, demographics, life-style, and psychology. The family of regression models was the predominant statistical approach. Results from our multidimensional scaling showed that a relatively tight core of determinants have received much attention, as main covariates of interest or controls, whereas the majority of other determinants were studied in very limited contexts. We consider findings from these studies regarding the importance of any given health determinant inconclusive at present. Across a multitude of model specifications, different country samples, and varying time periods, effects fluctuated between statistically significant and not significant, and between beneficial and detrimental to health.
We conclude that efforts to understand the underlying mechanisms of population health are far from settled, and the present state of research on the topic leaves much to be desired. It is essential that future research considers multiple factors simultaneously and takes advantage of more sophisticated methodology with regards to quantifying health as well as analyzing determinants’ influence.
Citation: Varbanova V, Beutels P (2020) Recent quantitative research on determinants of health in high income countries: A scoping review. PLoS ONE 15(9): e0239031. https://doi.org/10.1371/journal.pone.0239031
Editor: Amir Radfar, University of Central Florida, UNITED STATES
Received: November 14, 2019; Accepted: August 28, 2020; Published: September 17, 2020
Copyright: © 2020 Varbanova, Beutels. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This study (and VV) is funded by the Research Foundation Flanders ( https://www.fwo.be/en/ ), FWO project number G0D5917N, award obtained by PB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Identifying the key drivers of population health is a core subject in public health and health economics research. Between-country comparative research on the topic is challenging. In order to be relevant for policy, it requires disentangling different interrelated drivers of “good health”, each having different degrees of importance in different contexts.
“Good health”–physical and psychological, subjective and objective–can be defined and measured using a variety of approaches, depending on which aspect of health is the focus. A major distinction can be made between health measurements at the individual level or some aggregate level, such as a neighborhood, a region or a country. In view of this, a great diversity of specific research topics exists on the drivers of what constitutes individual or aggregate “good health”, including those focusing on health inequalities, the gender gap in longevity, and regional mortality and longevity differences.
The current scoping review focuses on determinants of population health. Stated as such, this topic is quite broad. Indeed, we are interested in the very general question of what methods have been used to make the most of increasingly available region or country-specific databases to understand the drivers of population health through inter-country comparisons. Existing reviews indicate that researchers thus far tend to adopt a narrower focus. Usually, attention is given to only one health outcome at a time, with further geographical and/or population [ 1 , 2 ] restrictions. In some cases, the impact of one or more interventions is at the core of the review [ 3 – 7 ], while in others it is the relationship between health and just one particular predictor, e.g., income inequality, access to healthcare, government mechanisms [ 8 – 13 ]. Some relatively recent reviews on the subject of social determinants of health [ 4 – 6 , 14 – 17 ] have considered a number of indicators potentially influencing health as opposed to a single one. One review defines “social determinants” as “the social, economic, and political conditions that influence the health of individuals and populations” [ 17 ] while another refers even more broadly to “the factors apart from medical care” [ 15 ].
In the present work, we aimed to be more inclusive, setting no limitations on the nature of possible health correlates, as well as making use of a multitude of commonly accepted measures of general population health. The goal of this scoping review was to document the state of the art in the recent published literature on determinants of population health, with a particular focus on the types of determinants selected and the methodology used. In doing so, we also report the main characteristics of the results these studies found. The materials collected in this review are intended to inform our (and potentially other researchers’) future analyses on this topic. Since the production of health is subject to the law of diminishing marginal returns, we focused our review on those studies that included countries where a high standard of wealth has been achieved for some time, i.e., high-income countries belonging to the Organisation for Economic Co-operation and Development (OECD) or Europe. Adding similar reviews for other country income groups is of limited interest to the research we plan to do in this area.
In view of its focus on data and methods, rather than results, a formal protocol was not registered prior to undertaking this review, but the procedure followed the guidelines of the PRISMA statement for scoping reviews [ 18 ].
We focused on multi-country studies investigating the potential associations between any aggregate level (region/city/country) determinant and general measures of population health (e.g., life expectancy, mortality rate).
Within the query itself, we listed well-established population health indicators as well as the six world regions, as defined by the World Health Organization (WHO). We searched only in the publications’ titles in order to keep the number of hits manageable, and the ratio of broadly relevant abstracts over all abstracts in the order of magnitude of 10% (based on a series of time-focused trial runs). The search strategy was developed iteratively between the two authors and is presented in S1 Appendix . The search was performed by VV in PubMed and Web of Science on the 16 th of July, 2019, without any language restrictions, and with a start date set to the 1 st of January, 2013, as we were interested in the latest developments in this area of research.
Records obtained via the search methods described above were screened independently by the two authors. Consistency between inclusion/exclusion decisions was approximately 90% and the 43 instances where uncertainty existed were judged through discussion. Articles were included subject to meeting the following requirements: (a) the paper was a full published report of an original empirical study investigating the impact of at least one aggregate level (city/region/country) factor on at least one health indicator (or self-reported health) of the general population (the only admissible “sub-populations” were those based on gender and/or age); (b) the study employed statistical techniques (calculating correlations, at the very least) and was not purely descriptive or theoretical in nature; (c) the analysis involved at least two countries or at least two regions or cities (or another aggregate level) in at least two different countries; (d) the health outcome was not differentiated according to some socio-economic factor and thus studied in terms of inequality (with the exception of gender and age differentiations); (e) mortality, in case it was one of the health indicators under investigation, was strictly “total” or “all-cause” (no cause-specific or determinant-attributable mortality).
The following pieces of information were extracted in an Excel table from the full text of each eligible study (primarily by VV, consulting with PB in case of doubt): health outcome(s), determinants, statistical methodology, level of analysis, results, type of data, data sources, time period, countries. The evidence is synthesized according to these extracted data (often directly reflected in the section headings), using a narrative form accompanied by a “summary-of-findings” table and a graph.
The initial yield contained 4583 records, reduced to 3686 after removal of duplicates ( Fig 1 ). Based on title and abstract screening, 3271 records were excluded because they focused on specific medical condition(s) or specific populations (based on morbidity or some other factor), dealt with intervention effectiveness, with theoretical or non-health related issues, or with animals or plants. Of the remaining 415 papers, roughly half were disqualified upon full-text consideration, mostly due to using an outcome not of interest to us (e.g., health inequality), measuring and analyzing determinants and outcomes exclusively at the individual level, performing analyses one country at a time, employing indices that are a mixture of both health indicators and health determinants, or not utilizing potential health determinants at all. After this second stage of the screening process, 202 papers were deemed eligible for inclusion. This group was further dichotomized according to level of economic development of the countries or regions under study, using membership of the OECD or Europe as a reference “cut-off” point. Sixty papers were judged to include high-income countries, and the remaining 142 included either low- or middle-income countries or a mix of both these levels of development. The rest of this report outlines findings in relation to high-income countries only, reflecting our own primary research interests. Nonetheless, we chose to report our search yield for the other income groups for two reasons. First, to gauge the relative interest in applied published research for these different income levels; and second, to enable other researchers with a focus on determinants of health in other countries to use the extraction we made here.
https://doi.org/10.1371/journal.pone.0239031.g001
The most frequent population health indicator, life expectancy (LE), was present in 24 of the 60 studies. Apart from “life expectancy at birth” (representing the average life-span a newborn is expected to have if current mortality rates remain constant), also called “period LE” by some [ 19 , 20 ], we encountered as well LE at 40 years of age [ 21 ], at 60 [ 22 ], and at 65 [ 21 , 23 , 24 ]. In two papers, the age-specificity of life expectancy (be it at birth or another age) was not stated [ 25 , 26 ].
Some studies considered male and female LE separately [ 21 , 24 , 25 , 27 – 33 ]. This consideration was also often observed with the second most commonly used health index [ 28 – 30 , 34 – 38 ]–termed “total”, or “overall”, or “all-cause”, mortality rate (MR)–included in 22 of the 60 studies. In addition to gender, this index was also sometimes broken down according to age group [ 30 , 39 , 40 ], as well as gender-age group [ 38 ].
While the majority of studies under review here focused on a single health indicator, 23 out of the 60 studies made use of multiple outcomes, although these outcomes were always considered one at a time, and sometimes not all of them fell within the scope of our review. An easily discernable group of indices that typically went together [ 25 , 37 , 41 ] was that of neonatal (deaths occurring within 28 days postpartum), perinatal (fetal or early neonatal / first-7-days deaths), and post-neonatal (deaths between the 29 th day and completion of one year of life) mortality. More often than not, these indices were also accompanied by “stand-alone” indicators, such as infant mortality (deaths within the first year of life; our third most common index found in 16 of the 60 studies), maternal mortality (deaths during pregnancy or within 42 days of termination of pregnancy), and child mortality rates. Child mortality has conventionally been defined as mortality within the first 5 years of life, thus often also called “under-5 mortality”. Nonetheless, Pritchard & Wallace used the term “child mortality” to denote deaths of children younger than 14 years [ 42 ].
As previously stated, inclusion criteria did allow for self-reported health status to be used as a general measure of population health. Within our final selection of studies, seven utilized some form of subjective health as an outcome variable [ 25 , 43 – 48 ]. Additionally, the Health Human Development Index [ 49 ], healthy life expectancy [ 50 ], old-age survival [ 51 ], potential years of life lost [ 52 ], and disability-adjusted life expectancy [ 25 ] were also used.
We note that while in most cases the indicators mentioned above (and/or the covariates considered, see below) were taken in their absolute or logarithmic form, as a—typically annual—number, sometimes they were used in the form of differences, change rates, averages over a given time period, or even z-scores of rankings [ 19 , 22 , 40 , 42 , 44 , 53 – 57 ].
Despite our decision to confine this review to high-income countries, some variation in the countries and regions studied was still present. Selection seemed to be most often conditioned on the European Union, or the European continent more generally, and the Organisation of Economic Co-operation and Development (OECD), though, typically, not all member nations–based on the instances where these were also explicitly listed—were included in a given study. Some of the stated reasons for omitting certain nations included data unavailability [ 30 , 45 , 54 ] or inconsistency [ 20 , 58 ], Gross Domestic Product (GDP) too low [ 40 ], differences in economic development and political stability with the rest of the sampled countries [ 59 ], and national population too small [ 24 , 40 ]. On the other hand, the rationales for selecting a group of countries included having similar above-average infant mortality [ 60 ], similar healthcare systems [ 23 ], and being randomly drawn from a social spending category [ 61 ]. Some researchers were interested explicitly in a specific geographical region, such as Eastern Europe [ 50 ], Central and Eastern Europe [ 48 , 60 ], the Visegrad (V4) group [ 62 ], or the Asia/Pacific area [ 32 ]. In certain instances, national regions or cities, rather than countries, constituted the units of investigation instead [ 31 , 51 , 56 , 62 – 66 ]. In two particular cases, a mix of countries and cities was used [ 35 , 57 ]. In another two [ 28 , 29 ], due to the long time periods under study, some of the included countries no longer exist. Finally, besides “European” and “OECD”, the terms “developed”, “Western”, and “industrialized” were also used to describe the group of selected nations [ 30 , 42 , 52 , 53 , 67 ].
As stated above, it was the health status of the general population that we were interested in, and during screening we made a concerted effort to exclude research using data based on a more narrowly defined group of individuals. All studies included in this review adhere to this general rule, albeit with two caveats. First, as cities (even neighborhoods) were the unit of analysis in three of the studies that made the selection [ 56 , 64 , 65 ], the populations under investigation there can be more accurately described as general urban , instead of just general. Second, oftentimes health indicators were stratified based on gender and/or age, therefore we also admitted one study that, due to its specific research question, focused on men and women of early retirement age [ 35 ] and another that considered adult males only [ 68 ].
A great diversity of sources was utilized for data collection purposes. The accessible reference databases of the OECD ( https://www.oecd.org/ ), WHO ( https://www.who.int/ ), World Bank ( https://www.worldbank.org/ ), United Nations ( https://www.un.org/en/ ), and Eurostat ( https://ec.europa.eu/eurostat ) were among the top choices. The other international databases included Human Mortality [ 30 , 39 , 50 ], Transparency International [ 40 , 48 , 50 ], Quality of Government [ 28 , 69 ], World Income Inequality [ 30 ], International Labor Organization [ 41 ], International Monetary Fund [ 70 ]. A number of national databases were referred to as well, for example the US Bureau of Statistics [ 42 , 53 ], Korean Statistical Information Services [ 67 ], Statistics Canada [ 67 ], Australian Bureau of Statistics [ 67 ], and Health New Zealand Tobacco control and Health New Zealand Food and Nutrition [ 19 ]. Well-known surveys, such as the World Values Survey [ 25 , 55 ], the European Social Survey [ 25 , 39 , 44 ], the Eurobarometer [ 46 , 56 ], the European Value Survey [ 25 ], and the European Statistics of Income and Living Condition Survey [ 43 , 47 , 70 ] were used as data sources, too. Finally, in some cases [ 25 , 28 , 29 , 35 , 36 , 41 , 69 ], built-for-purpose datasets from previous studies were re-used.
In most of the studies, the level of the data (and analysis) was national. The exceptions were six papers that dealt with Nomenclature of Territorial Units of Statistics (NUTS2) regions [ 31 , 62 , 63 , 66 ], otherwise defined areas [ 51 ] or cities [ 56 ], and seven others that were multilevel designs and utilized both country- and region-level data [ 57 ], individual- and city- or country-level [ 35 ], individual- and country-level [ 44 , 45 , 48 ], individual- and neighborhood-level [ 64 ], and city-region- (NUTS3) and country-level data [ 65 ]. Parallel to that, the data type was predominantly longitudinal, with only a few studies using purely cross-sectional data [ 25 , 33 , 43 , 45 – 48 , 50 , 62 , 67 , 68 , 71 , 72 ], albeit in four of those [ 43 , 48 , 68 , 72 ] two separate points in time were taken (thus resulting in a kind of “double cross-section”), while in another the averages across survey waves were used [ 56 ].
In studies using longitudinal data, the length of the covered time periods varied greatly. Although this was almost always less than 40 years, in one study it covered the entire 20 th century [ 29 ]. Longitudinal data, typically in the form of annual records, was sometimes transformed before usage. For example, some researchers considered data points at 5- [ 34 , 36 , 49 ] or 10-year [ 27 , 29 , 35 ] intervals instead of the traditional 1, or took averages over 3-year periods [ 42 , 53 , 73 ]. In one study concerned with the effect of the Great Recession all data were in a “recession minus expansion change in trends”-form [ 57 ]. Furthermore, there were a few instances where two different time periods were compared to each other [ 42 , 53 ] or when data was divided into 2 to 4 (possibly overlapping) periods which were then analyzed separately [ 24 , 26 , 28 , 29 , 31 , 65 ]. Lastly, owing to data availability issues, discrepancies between the time points or periods of data on the different variables were occasionally observed [ 22 , 35 , 42 , 53 – 55 , 63 ].
Together with other essential details, Table 1 lists the health correlates considered in the selected studies. Several general categories for these correlates can be discerned, including health care, political stability, socio-economics, demographics, psychology, environment, fertility, life-style, culture, labor. All of these, directly or implicitly, have been recognized as holding importance for population health by existing theoretical models of (social) determinants of health [ 74 – 77 ].
https://doi.org/10.1371/journal.pone.0239031.t001
It is worth noting that in a few studies there was just a single aggregate-level covariate investigated in relation to a health outcome of interest to us. In one instance, this was life satisfaction [ 44 ], in another–welfare system typology [ 45 ], but also gender inequality [ 33 ], austerity level [ 70 , 78 ], and deprivation [ 51 ]. Most often though, attention went exclusively to GDP [ 27 , 29 , 46 , 57 , 65 , 71 ]. It was often the case that research had a more particular focus. Among others, minimum wages [ 79 ], hospital payment schemes [ 23 ], cigarette prices [ 63 ], social expenditure [ 20 ], residents’ dissatisfaction [ 56 ], income inequality [ 30 , 69 ], and work leave [ 41 , 58 ] took center stage. Whenever variables outside of these specific areas were also included, they were usually identified as confounders or controls, moderators or mediators.
We visualized the combinations in which the different determinants have been studied in Fig 2 , which was obtained via multidimensional scaling and a subsequent cluster analysis (details outlined in S2 Appendix ). It depicts the spatial positioning of each determinant relative to all others, based on the number of times the effects of each pair of determinants have been studied simultaneously. When interpreting Fig 2 , one should keep in mind that determinants marked with an asterisk represent, in fact, collectives of variables.
Groups of determinants are marked by asterisks (see S1 Table in S1 Appendix ). Diminishing color intensity reflects a decrease in the total number of “connections” for a given determinant. Noteworthy pairwise “connections” are emphasized via lines (solid-dashed-dotted indicates decreasing frequency). Grey contour lines encircle groups of variables that were identified via cluster analysis. Abbreviations: age = population age distribution, associations = membership in associations, AT-index = atherogenic-thrombogenic index, BR = birth rate, CAPB = Cyclically Adjusted Primary Balance, civilian-labor = civilian labor force, C-section = Cesarean delivery rate, credit-info = depth of credit information, dissatisf = residents’ dissatisfaction, distrib.orient = distributional orientation, EDU = education, eHealth = eHealth index at GP-level, exch.rate = exchange rate, fat = fat consumption, GDP = gross domestic product, GFCF = Gross Fixed Capital Formation/Creation, GH-gas = greenhouse gas, GII = gender inequality index, gov = governance index, gov.revenue = government revenues, HC-coverage = healthcare coverage, HE = health(care) expenditure, HHconsump = household consumption, hosp.beds = hospital beds, hosp.payment = hospital payment scheme, hosp.stay = length of hospital stay, IDI = ICT development index, inc.ineq = income inequality, industry-labor = industrial labor force, infant-sex = infant sex ratio, labor-product = labor production, LBW = low birth weight, leave = work leave, life-satisf = life satisfaction, M-age = maternal age, marginal-tax = marginal tax rate, MDs = physicians, mult.preg = multiple pregnancy, NHS = Nation Health System, NO = nitrous oxide emissions, PM10 = particulate matter (PM10) emissions, pop = population size, pop.density = population density, pre-term = pre-term birth rate, prison = prison population, researchE = research&development expenditure, school.ref = compulsory schooling reform, smoke-free = smoke-free places, SO = sulfur oxide emissions, soc.E = social expenditure, soc.workers = social workers, sugar = sugar consumption, terror = terrorism, union = union density, UR = unemployment rate, urban = urbanization, veg-fr = vegetable-and-fruit consumption, welfare = welfare regime, Wwater = wastewater treatment.
https://doi.org/10.1371/journal.pone.0239031.g002
Distances between determinants in Fig 2 are indicative of determinants’ “connectedness” with each other. While the statistical procedure called for higher dimensionality of the model, for demonstration purposes we show here a two-dimensional solution. This simplification unfortunately comes with a caveat. To use the factor smoking as an example, it would appear it stands at a much greater distance from GDP than it does from alcohol. In reality however, smoking was considered together with alcohol consumption [ 21 , 25 , 26 , 52 , 68 ] in just as many studies as it was with GDP [ 21 , 25 , 26 , 52 , 59 ], five. To aid with respect to this apparent shortcoming, we have emphasized the strongest pairwise links. Solid lines connect GDP with health expenditure (HE), unemployment rate (UR), and education (EDU), indicating that the effect of GDP on health, taking into account the effects of the other three determinants as well, was evaluated in between 12 to 16 studies of the 60 included in this review. Tracing the dashed lines, we can also tell that GDP appeared jointly with income inequality, and HE together with either EDU or UR, in anywhere between 8 to 10 of our selected studies. Finally, some weaker but still worth-mentioning “connections” between variables are displayed as well via the dotted lines.
The fact that all notable pairwise “connections” are concentrated within a relatively small region of the plot may be interpreted as low overall “connectedness” among the health indicators studied. GDP is the most widely investigated determinant in relation to general population health. Its total number of “connections” is disproportionately high (159) compared to its runner-up–HE (with 113 “connections”), and then subsequently EDU (with 90) and UR (with 86). In fact, all of these determinants could be thought of as outliers, given that none of the remaining factors have a total count of pairings above 52. This decrease in individual determinants’ overall “connectedness” can be tracked on the graph via the change of color intensity as we move outwards from the symbolic center of GDP and its closest “co-determinants”, to finally reach the other extreme of the ten indicators (welfare regime, household consumption, compulsory school reform, life satisfaction, government revenues, literacy, research expenditure, multiple pregnancy, Cyclically Adjusted Primary Balance, and residents’ dissatisfaction; in white) the effects on health of which were only studied in isolation.
Lastly, we point to the few small but stable clusters of covariates encircled by the grey bubbles on Fig 2 . These groups of determinants were identified as “close” by both statistical procedures used for the production of the graph (see details in S2 Appendix ).
There was great variation in the level of statistical detail reported. Some authors provided too vague a description of their analytical approach, necessitating some inference in this section.
The issue of missing data is a challenging reality in this field of research, but few of the studies under review (12/60) explain how they dealt with it. Among the ones that do, three general approaches to handling missingness can be identified, listed in increasing level of sophistication: case-wise deletion, i.e., removal of countries from the sample [ 20 , 45 , 48 , 58 , 59 ], (linear) interpolation [ 28 , 30 , 34 , 58 , 59 , 63 ], and multiple imputation [ 26 , 41 , 52 ].
Correlations, Pearson, Spearman, or unspecified, were the only technique applied with respect to the health outcomes of interest in eight analyses [ 33 , 42 – 44 , 46 , 53 , 57 , 61 ]. Among the more advanced statistical methods, the family of regression models proved to be, by and large, predominant. Before examining this closer, we note the techniques that were, in a way, “unique” within this selection of studies: meta-analyses were performed (random and fixed effects, respectively) on the reduced form and 2-sample two stage least squares (2SLS) estimations done within countries [ 39 ]; difference-in-difference (DiD) analysis was applied in one case [ 23 ]; dynamic time-series methods, among which co-integration, impulse-response function (IRF), and panel vector autoregressive (VAR) modeling, were utilized in one study [ 80 ]; longitudinal generalized estimating equation (GEE) models were developed on two occasions [ 70 , 78 ]; hierarchical Bayesian spatial models [ 51 ] and special autoregressive regression [ 62 ] were also implemented.
Purely cross-sectional data analyses were performed in eight studies [ 25 , 45 , 47 , 50 , 55 , 56 , 67 , 71 ]. These consisted of linear regression (assumed ordinary least squares (OLS)), generalized least squares (GLS) regression, and multilevel analyses. However, six other studies that used longitudinal data in fact had a cross-sectional design, through which they applied regression at multiple time-points separately [ 27 , 29 , 36 , 48 , 68 , 72 ].
Apart from these “multi-point cross-sectional studies”, some other simplistic approaches to longitudinal data analysis were found, involving calculating and regressing 3-year averages of both the response and the predictor variables [ 54 ], taking the average of a few data-points (i.e., survey waves) [ 56 ] or using difference scores over 10-year [ 19 , 29 ] or unspecified time intervals [ 40 , 55 ].
Moving further in the direction of more sensible longitudinal data usage, we turn to the methods widely known among (health) economists as “panel data analysis” or “panel regression”. Most often seen were models with fixed effects for country/region and sometimes also time-point (occasionally including a country-specific trend as well), with robust standard errors for the parameter estimates to take into account correlations among clustered observations [ 20 , 21 , 24 , 28 , 30 , 32 , 34 , 37 , 38 , 41 , 52 , 59 , 60 , 63 , 66 , 69 , 73 , 79 , 81 , 82 ]. The Hausman test [ 83 ] was sometimes mentioned as the tool used to decide between fixed and random effects [ 26 , 49 , 63 , 66 , 73 , 82 ]. A few studies considered the latter more appropriate for their particular analyses, with some further specifying that (feasible) GLS estimation was employed [ 26 , 34 , 49 , 58 , 60 , 73 ]. Apart from these two types of models, the first differences method was encountered once as well [ 31 ]. Across all, the error terms were sometimes assumed to come from a first-order autoregressive process (AR(1)), i.e., they were allowed to be serially correlated [ 20 , 30 , 38 , 58 – 60 , 73 ], and lags of (typically) predictor variables were included in the model specification, too [ 20 , 21 , 37 , 38 , 48 , 69 , 81 ]. Lastly, a somewhat different approach to longitudinal data analysis was undertaken in four studies [ 22 , 35 , 48 , 65 ] in which multilevel–linear or Poisson–models were developed.
Regardless of the exact techniques used, most studies included in this review presented multiple model applications within their main analysis. None attempted to formally compare models in order to identify the “best”, even if goodness-of-fit statistics were occasionally reported. As indicated above, many studies investigated women’s and men’s health separately [ 19 , 21 , 22 , 27 – 29 , 31 , 33 , 35 , 36 , 38 , 39 , 45 , 50 , 51 , 64 , 65 , 69 , 82 ], and covariates were often tested one at a time, including other covariates only incrementally [ 20 , 25 , 28 , 36 , 40 , 50 , 55 , 67 , 73 ]. Furthermore, there were a few instances where analyses within countries were performed as well [ 32 , 39 , 51 ] or where the full time period of interest was divided into a few sub-periods [ 24 , 26 , 28 , 31 ]. There were also cases where different statistical techniques were applied in parallel [ 29 , 55 , 60 , 66 , 69 , 73 , 82 ], sometimes as a form of sensitivity analysis [ 24 , 26 , 30 , 58 , 73 ]. However, the most common approach to sensitivity analysis was to re-run models with somewhat different samples [ 39 , 50 , 59 , 67 , 69 , 80 , 82 ]. Other strategies included different categorization of variables or adding (more/other) controls [ 21 , 23 , 25 , 28 , 37 , 50 , 63 , 69 ], using an alternative main covariate measure [ 59 , 82 ], including lags for predictors or outcomes [ 28 , 30 , 58 , 63 , 65 , 79 ], using weights [ 24 , 67 ] or alternative data sources [ 37 , 69 ], or using non-imputed data [ 41 ].
As the methods and not the findings are the main focus of the current review, and because generic checklists cannot discern the underlying quality in this application field (see also below), we opted to pool all reported findings together, regardless of individual study characteristics or particular outcome(s) used, and speak generally of positive and negative effects on health. For this summary we have adopted the 0.05-significance level and only considered results from multivariate analyses. Strictly birth-related factors are omitted since these potentially only relate to the group of infant mortality indicators and not to any of the other general population health measures.
Starting with the determinants most often studied, higher GDP levels [ 21 , 26 , 27 , 29 , 30 , 32 , 43 , 48 , 52 , 58 , 60 , 66 , 67 , 73 , 79 , 81 , 82 ], higher health [ 21 , 37 , 47 , 49 , 52 , 58 , 59 , 68 , 72 , 82 ] and social [ 20 , 21 , 26 , 38 , 79 ] expenditures, higher education [ 26 , 39 , 52 , 62 , 72 , 73 ], lower unemployment [ 60 , 61 , 66 ], and lower income inequality [ 30 , 42 , 53 , 55 , 73 ] were found to be significantly associated with better population health on a number of occasions. In addition to that, there was also some evidence that democracy [ 36 ] and freedom [ 50 ], higher work compensation [ 43 , 79 ], distributional orientation [ 54 ], cigarette prices [ 63 ], gross national income [ 22 , 72 ], labor productivity [ 26 ], exchange rates [ 32 ], marginal tax rates [ 79 ], vaccination rates [ 52 ], total fertility [ 59 , 66 ], fruit and vegetable [ 68 ], fat [ 52 ] and sugar consumption [ 52 ], as well as bigger depth of credit information [ 22 ] and percentage of civilian labor force [ 79 ], longer work leaves [ 41 , 58 ], more physicians [ 37 , 52 , 72 ], nurses [ 72 ], and hospital beds [ 79 , 82 ], and also membership in associations, perceived corruption and societal trust [ 48 ] were beneficial to health. Higher nitrous oxide (NO) levels [ 52 ], longer average hospital stay [ 48 ], deprivation [ 51 ], dissatisfaction with healthcare and the social environment [ 56 ], corruption [ 40 , 50 ], smoking [ 19 , 26 , 52 , 68 ], alcohol consumption [ 26 , 52 , 68 ] and illegal drug use [ 68 ], poverty [ 64 ], higher percentage of industrial workers [ 26 ], Gross Fixed Capital creation [ 66 ] and older population [ 38 , 66 , 79 ], gender inequality [ 22 ], and fertility [ 26 , 66 ] were detrimental.
It is important to point out that the above-mentioned effects could not be considered stable either across or within studies. Very often, statistical significance of a given covariate fluctuated between the different model specifications tried out within the same study [ 20 , 49 , 59 , 66 , 68 , 69 , 73 , 80 , 82 ], testifying to the importance of control variables and multivariate research (i.e., analyzing multiple independent variables simultaneously) in general. Furthermore, conflicting results were observed even with regards to the “core” determinants given special attention, so to speak, throughout this text. Thus, some studies reported negative effects of health expenditure [ 32 , 82 ], social expenditure [ 58 ], GDP [ 49 , 66 ], and education [ 82 ], and positive effects of income inequality [ 82 ] and unemployment [ 24 , 31 , 32 , 52 , 66 , 68 ]. Interestingly, one study [ 34 ] differentiated between temporary and long-term effects of GDP and unemployment, alluding to possibly much greater complexity of the association with health. It is also worth noting that some gender differences were found, with determinants being more influential for males than for females, or only having statistically significant effects for male health [ 19 , 21 , 28 , 34 , 36 , 37 , 39 , 64 , 65 , 69 ].
The purpose of this scoping review was to examine recent quantitative work on the topic of multi-country analyses of determinants of population health in high-income countries.
Measuring population health via relatively simple mortality-based indicators still seems to be the state of the art. What is more, these indicators are routinely considered one at a time, instead of, for example, employing existing statistical procedures to devise a more general, composite, index of population health, or using some of the established indices, such as disability-adjusted life expectancy (DALE) or quality-adjusted life expectancy (QALE). Although strong arguments for their wider use were already voiced decades ago [ 84 ], such summary measures surface only rarely in this research field.
On a related note, the greater data availability and accessibility that we enjoy today does not automatically equate to data quality. Nonetheless, this is routinely assumed in aggregate level studies. We almost never encountered a discussion on the topic. The non-mundane issue of data missingness, too, goes largely underappreciated. With all recent methodological advancements in this area [ 85 – 88 ], there is no excuse for ignorance; and still, too few of the reviewed studies tackled the matter in any adequate fashion.
Much optimism can be gained considering the abundance of different determinants that have attracted researchers’ attention in relation to population health. We took on a visual approach with regards to these determinants and presented a graph that links spatial distances between determinants with frequencies of being studies together. To facilitate interpretation, we grouped some variables, which resulted in some loss of finer detail. Nevertheless, the graph is helpful in exemplifying how many effects continue to be studied in a very limited context, if any. Since in reality no factor acts in isolation, this oversimplification practice threatens to render the whole exercise meaningless from the outset. The importance of multivariate analysis cannot be stressed enough. While there is no “best method” to be recommended and appropriate techniques vary according to the specifics of the research question and the characteristics of the data at hand [ 89 – 93 ], in the future, in addition to abandoning simplistic univariate approaches, we hope to see a shift from the currently dominating fixed effects to the more flexible random/mixed effects models [ 94 ], as well as wider application of more sophisticated methods, such as principle component regression, partial least squares, covariance structure models (e.g., structural equations), canonical correlations, time-series, and generalized estimating equations.
Finally, there are some limitations of the current scoping review. We searched the two main databases for published research in medical and non-medical sciences (PubMed and Web of Science) since 2013, thus potentially excluding publications and reports that are not indexed in these databases, as well as older indexed publications. These choices were guided by our interest in the most recent (i.e., the current state-of-the-art) and arguably the highest-quality research (i.e., peer-reviewed articles, primarily in indexed non-predatory journals). Furthermore, despite holding a critical stance with regards to some aspects of how determinants-of-health research is currently conducted, we opted out of formally assessing the quality of the individual studies included. The reason for that is two-fold. On the one hand, we are unaware of the existence of a formal and standard tool for quality assessment of ecological designs. And on the other, we consider trying to score the quality of these diverse studies (in terms of regional setting, specific topic, outcome indices, and methodology) undesirable and misleading, particularly since we would sometimes have been rating the quality of only a (small) part of the original studies—the part that was relevant to our review’s goal.
Our aim was to investigate the current state of research on the very broad and general topic of population health, specifically, the way it has been examined in a multi-country context. We learned that data treatment and analytical approach were, in the majority of these recent studies, ill-equipped or insufficiently transparent to provide clarity regarding the underlying mechanisms of population health in high-income countries. Whether due to methodological shortcomings or the inherent complexity of the topic, research so far fails to provide any definitive answers. It is our sincere belief that with the application of more advanced analytical techniques this continuous quest could come to fruition sooner.
S1 checklist. preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr) checklist..
https://doi.org/10.1371/journal.pone.0239031.s001
https://doi.org/10.1371/journal.pone.0239031.s002
https://doi.org/10.1371/journal.pone.0239031.s003
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Some nurses feel that they lack the necessary skills to read a research paper and to then decide if they should implement the findings into their practice. This is particularly the case when considering the results of quantitative research, which often contains the results of statistical testing. However, nurses have a professional responsibility to critique research to improve their practice, care and patient safety. 1 This article provides a step by step guide on how to critically appraise a quantitative paper.
The authors’ names may not mean much, but knowing the following will be helpful:
Their position, for example, academic, researcher or healthcare practitioner.
Their qualification, both professional, for example, a nurse or physiotherapist and academic (eg, degree, masters, doctorate).
This can indicate how the research has been conducted and the authors’ competence on the subject. Basically, do you want to read a paper on quantum physics written by a plumber?
The abstract is a resume of the article and should contain:
Introduction.
Research question/hypothesis.
Methods including sample design, tests used and the statistical analysis (of course! Remember we love numbers).
Main findings.
Conclusion.
The subheadings in the abstract will vary depending on the journal. An abstract should not usually be more than 300 words but this varies depending on specific journal requirements. If the above information is contained in the abstract, it can give you an idea about whether the study is relevant to your area of practice. However, before deciding if the results of a research paper are relevant to your practice, it is important to review the overall quality of the article. This can only be done by reading and critically appraising the entire article.
Example: the effect of paracetamol on levels of pain.
My hypothesis is that A has an effect on B, for example, paracetamol has an effect on levels of pain.
My null hypothesis is that A has no effect on B, for example, paracetamol has no effect on pain.
My study will test the null hypothesis and if the null hypothesis is validated then the hypothesis is false (A has no effect on B). This means paracetamol has no effect on the level of pain. If the null hypothesis is rejected then the hypothesis is true (A has an effect on B). This means that paracetamol has an effect on the level of pain.
The literature review should include reference to recent and relevant research in the area. It should summarise what is already known about the topic and why the research study is needed and state what the study will contribute to new knowledge. 5 The literature review should be up to date, usually 5–8 years, but it will depend on the topic and sometimes it is acceptable to include older (seminal) studies.
In quantitative studies, the data analysis varies between studies depending on the type of design used. For example, descriptive, correlative or experimental studies all vary. A descriptive study will describe the pattern of a topic related to one or more variable. 6 A correlational study examines the link (correlation) between two variables 7 and focuses on how a variable will react to a change of another variable. In experimental studies, the researchers manipulate variables looking at outcomes 8 and the sample is commonly assigned into different groups (known as randomisation) to determine the effect (causal) of a condition (independent variable) on a certain outcome. This is a common method used in clinical trials.
There should be sufficient detail provided in the methods section for you to replicate the study (should you want to). To enable you to do this, the following sections are normally included:
Overview and rationale for the methodology.
Participants or sample.
Data collection tools.
Methods of data analysis.
Ethical issues.
Data collection should be clearly explained and the article should discuss how this process was undertaken. Data collection should be systematic, objective, precise, repeatable, valid and reliable. Any tool (eg, a questionnaire) used for data collection should have been piloted (or pretested and/or adjusted) to ensure the quality, validity and reliability of the tool. 9 The participants (the sample) and any randomisation technique used should be identified. The sample size is central in quantitative research, as the findings should be able to be generalised for the wider population. 10 The data analysis can be done manually or more complex analyses performed using computer software sometimes with advice of a statistician. From this analysis, results like mode, mean, median, p value, CI and so on are always presented in a numerical format.
The author(s) should present the results clearly. These may be presented in graphs, charts or tables alongside some text. You should perform your own critique of the data analysis process; just because a paper has been published, it does not mean it is perfect. Your findings may be different from the author’s. Through critical analysis the reader may find an error in the study process that authors have not seen or highlighted. These errors can change the study result or change a study you thought was strong to weak. To help you critique a quantitative research paper, some guidance on understanding statistical terminology is provided in table 1 .
Some basic guidance for understanding statistics
Quantitative studies examine the relationship between variables, and the p value illustrates this objectively. 11 If the p value is less than 0.05, the null hypothesis is rejected and the hypothesis is accepted and the study will say there is a significant difference. If the p value is more than 0.05, the null hypothesis is accepted then the hypothesis is rejected. The study will say there is no significant difference. As a general rule, a p value of less than 0.05 means, the hypothesis is accepted and if it is more than 0.05 the hypothesis is rejected.
The CI is a number between 0 and 1 or is written as a per cent, demonstrating the level of confidence the reader can have in the result. 12 The CI is calculated by subtracting the p value to 1 (1–p). If there is a p value of 0.05, the CI will be 1–0.05=0.95=95%. A CI over 95% means, we can be confident the result is statistically significant. A CI below 95% means, the result is not statistically significant. The p values and CI highlight the confidence and robustness of a result.
The final section of the paper is where the authors discuss their results and link them to other literature in the area (some of which may have been included in the literature review at the start of the paper). This reminds the reader of what is already known, what the study has found and what new information it adds. The discussion should demonstrate how the authors interpreted their results and how they contribute to new knowledge in the area. Implications for practice and future research should also be highlighted in this section of the paper.
A few other areas you may find helpful are:
Limitations of the study.
Conflicts of interest.
Table 2 provides a useful tool to help you apply the learning in this paper to the critiquing of quantitative research papers.
Quantitative paper appraisal checklist
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Commissioned; internally peer reviewed.
Correction notice This article has been updated since its original publication to update p values from 0.5 to 0.05 throughout.
Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.
Understanding Quantitative Research Questions
Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:
Stats Camp: Your Solution to Mastering Quantitative Research Methodologies
At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.
Bringing Your Own Data
One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.
Courses Offered at StatsCamp.org
As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!
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Quantitative research focuses on gathering numerical data.
To locate qualitative research articles, use a subject-specific database or a general library database like Academic Search Ultimate or Google Scholar .
Finding this types of research takes a bit of investigation. Try this method.
Begin by entering your keywords and conducting a search. Example: gardening AND mental health AND students
Since quantitative research is based on the collection and analysis of data (like numbers or statistics), you will need to look at article titles and abstracts for clues. If a title or abstract contains terms like these, it's probably a quantitative research article.
You could also experiment with using one of those terms in your search query. Example: gardening AND mental health AND data analysis
See this guide from the University of Texas. Quantitative and Qualitative Research
Download this guide from Winston Salem State University Key Elements of a Research Proposal: Quantitative Design (PDF)
Related faqs, frequently asked questions.
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Methodology
Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.
Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.
Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.
Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.
The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.
Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.
Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).
Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).
However, some methods are more commonly used in one type or the other.
A rule of thumb for deciding whether to use qualitative or quantitative data is:
For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.
You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”
You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.
You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”
Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.
You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.
It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.
Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.
Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.
Applications such as Excel, SPSS, or R can be used to calculate things like:
Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.
Some common approaches to analyzing qualitative data include:
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
The research methods you use depend on the type of data you need to answer your research question .
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
There are various approaches to qualitative data analysis , but they all share five steps in common:
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Streefkerk, R. (2023, June 22). Qualitative vs. Quantitative Research | Differences, Examples & Methods. Scribbr. Retrieved August 29, 2024, from https://www.scribbr.com/methodology/qualitative-quantitative-research/
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Coghlan, D., Brydon-Miller, M. (2014). The SAGE encyclopedia of action research (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406
What is the purpose of quantitative research?
The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.
Allen, M. (2017). The SAGE encyclopedia of communication research methods (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411
How do I know if the study is a quantitative design? What type of quantitative study is it?
Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?
Studies do not always explicitly state what kind of research design is being used. You will need to know how to decipher which design type is used. The following video will help you determine the quantitative design type.
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Unit 6: Qual vs Quant.
Quantitative methods in communication research.
In communication research, both quantitative and qualitative methods are essential for understanding different aspects of communication processes and effects. Here’s how quant methods can be applied:
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Psychological Research
This seven-hour course provides a comprehensive exploration of research methodologies, beginning with the foundational steps of the scientific method. Students will learn about hypotheses, experimental design, data collection, and the analysis of results. Emphasis is placed on defining variables accurately, distinguishing between independent, dependent, and controlled variables, and understanding their roles in research.
The course delves into major research designs, including experimental, correlational, and observational studies. Students will compare and contrast these designs, evaluating their strengths and weaknesses in various contexts. This comparison extends to the types of research questions scientists pose, highlighting how different designs are suited to different inquiries.
A critical component of the course is developing the ability to judge the quality of sources for literature reviews. Students will learn criteria for evaluating the credibility, relevance, and reliability of sources, ensuring that their understanding of the research literature is built on a solid foundation.
Reliability and validity are key concepts addressed in the course. Students will explore what it means for an observation to be reliable, focusing on consistency and repeatability. They will also compare and contrast different forms of validity, such as internal, external, construct, and criterion validity, and how these apply to various research designs.
The course concepts are thoroughly couched in examples drawn from the psychological research literature. By the end of the course, students will be equipped with the skills to design robust research studies, critically evaluate sources, and understand the nuances of reliability and validity in scientific research. This knowledge will be essential for conducting high-quality research and contributing to the scientific community.
This program does not offer CE credit.
Introduces applying statistical methods effectively in psychology or related fields for undergraduates, high school students, and professionals.
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Introduces the importance of ethical practice in scientific research for undergraduates, high school students, and professionals.
Home » 500+ Quantitative Research Titles and Topics
Table of Contents
Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.
Quantitative Research Titles are as follows:
Quantitative Research Topics are as follows:
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Ever wondered what makes the difference between a hit product and a missed opportunity? It’s often the precise insights that come from analyzing quantitative data. But with so many types of quantitative data available, where do you start?
In this article, we’ll explore various examples of quantitative data + how to collect them and make smarter decisions that keep users engaged .
Quantitative data refers to numerical data that can be measured, such as adoption rates, number of users, or net promoter scores.
Collecting this data is useful because it provides objective and measurable insights that you can analyze statistically and benchmark, minimizing subjective interpretation and bias.
Quantitative data refers to information that can be measured and expressed numerically, allowing for objective analysis . It answers the questions such as “what” and “how many.”
In contrast, qualitative data involves non-numerical information, such as opinions, behaviors, and experiences. You typically gather this through interviews, observations, or open-ended surveys to understand “why” and “how.”
While quantitative data provides measurable and comparable results, qualitative data offers deeper insights into the underlying reasons, opinions, and motivations behind those numbers.
Together, quantitative data and qualitative data offer a comprehensive understanding of user behavior and decision-making processes.
You can categorize quantitative data into several high-level types, each crucial to data-driven analysis methods.
Discrete data is a type of quantitative data that comprises specific and countable numerical values that cannot be subdivided meaningfully. For example, discrete data could be the number of customer support tickets that are counted individually—you cannot have 2.5 support tickets.
Continuous data is a type of quantitative data that represents measurements that can take any numerical value within a range. For example, you can measure time-to-value in minutes and seconds and divide it into smaller increments, such as 5 minutes and 34 seconds, 5 minutes and 35 seconds, etc.
Interval data is numerical data where the differences between values are meaningful, but there is no true zero point. A typical example is the temperature, where you can measure the difference between numerical values, but 0°C does not mean the absence of temperature.
Ratio data is quantitative data that allows for meaningful differences and ratios between numerical values, with a true zero point showing the absence of the measured quantity. An example of ratio data is MRR, where $0 MRR indicates no recurring revenue , and you can compare it meaningfully, such as saying one company has twice the MRR of another.
Here are some SaaS examples of quantitative data that PLG companies should track.
User activation rate is quantitative data that measures the percentage of users who complete a key action that signifies they are gaining value from the product. It helps you understand how your onboarding process converts new users into active, engaged customers.
You can calculate this metric with the following formula:
User Activation Rate = (Number of Activated Users / Total Number of Sign-Ups) × 100.
According to our metrics report , the average user activation rate is 37.5%.
Time to Value (TTV) is a type of quantitative data that measures the time it takes for a new user to realize the value of your product. This metric helps you understand whether your onboarding process effectively guides users to that “Aha” moment .
You can calculate this product metric as the time elapsed between the user’s initial sign-up and the “Aha” moment .
The average TTV across different industries based on our first-party data is one day, 12 hours, and 23 minutes.
Among examples of quantitative data, the onboarding checklist completion rate measures the percentage of users who complete all the tasks in your onboarding checklist . This rate is a key indicator of how effectively your user onboarding process guides new users through the essential steps.
You can calculate this metric using the following formula:
Onboarding Checklist Completion Rate = (Number of Users Who Completed the Checklist / Total Number of Users Who Started the Checklist) × 100
According to our report, the average checklist completion rate is 19%.
The core feature adoption rate is quantitative data that measures the percentage of users who adopt and regularly use your product’s most essential features.
This metric shows how well users integrate your product’s key functionalities into their workflows, which can directly affect customer retention and satisfaction.
Core Feature Adoption Rate = (Number of Monthly Active Users / Total Number of User Logins) × 100
Based on our findings, the average core feature adoption rate is 24.5%.
The 1-month retention rate is quantitative data that measures the percentage of users who continue to use your product one month after signing up. This metric shows how well your product meets user needs and keeps them engaged over the critical initial period.
To calculate the 1-month retention rate , you can use the following formula:
1-Month Retention Rate = (Number of Users Who Remain Active After 1 Month / Total Number of Users at the Start of the Month) × 100
Our data shows that the average 1-month retention rate is 46.9%.
Customer churn rate is quantitative data that measures the percentage of customers who stop using your product or service within a specific period.
This metric is crucial for understanding customer satisfaction and the overall health of your business because a high churn rate can show underlying issues with product value, user experience, or customer support.
To calculate the customer churn rate , you can use the following formula:
If you started the month with 1,000 customers and 50 customers churned by the end of the month, your churn rate would be as follows:
(50 / 1,000) × 100 = 5%
User stickiness is quantitative data that measures how often users return to your product within a specific period. This metric is a key indicator of user engagement and loyalty, showing how well your product keeps users returning regularly.
High customer stickiness typically means your product is valuable and engaging enough to become a regular part of users’ routines.
You can use the following formula to calculate stickiness:
If your product has 5,000 Daily Active Users (DAU) and 20,000 Monthly Active Users ( MAU ), the stickiness expressed in percentage would be:
(5,000 / 20,000) × 100 = 25%
Net Promoter Score (NPS) is quantitative data that measures customer loyalty and satisfaction by asking users how likely they are to recommend your product or service to others. NPS helps you understand the overall perception of your brand and can show areas for improvement in customer experience.
To calculate NPS , ask customers to rate their likelihood of recommending your product on a scale from 0 to 10. Based on their responses, customers are categorized into three groups:
You can calculate NPS using the following formula:
(Net Promoter Score) = % of Promoters – % of Detractors
Our report records the average NPS to be 35.7%.
Customer Satisfaction Score (CSAT) measures how satisfied customers are with your product.
To calculate CSAT, you typically ask customers to rate their satisfaction from 1 to 5, with one being very dissatisfied and five being very satisfied. After the quantitative data collection, you count the number of satisfied customers who gave a rating of 4 or 5.
Then, apply this formula to get your CSAT score:
For example, if you surveyed 100 customers and 80 of them gave you a rating of 4 or 5, your CSAT would be:
(80 / 100) × 100 = 80%
Among quantitative data examples, Customer Effort Score (CES) measures how much effort a customer has to exert to use your product or resolve an issue. CES is critical for understanding how user-friendly your product is.
To calculate this metric, you typically ask customers to rate their agreement with a statement like “The product is easy to use” on a Likert scale, usually ranging from 1 (strongly disagree) to 5 (strongly agree). After collecting responses, you count the number of customers who answered “agree” (4) or “strongly agree” (5).
Then, you can calculate the CES with this formula:
For instance, if you surveyed 100 customers and 70 of them responded with “agree” or “strongly agree,” your CES score would be:
(70 / 100) × 100 = 70%
Now that you know which examples of quantitative data you should collect, the question is: how? Here are three simple ways to collect quantitative data with product growth tools like Userpilot:
With Userpilot’s auto-capture functionality , you can automatically track quantitative data on clicks, text inputs, and form submissions without manually tagging each interaction.
Using retroactive analysis saves your valuable time and removes the dependencies on engineering as they don’t need to write code. Also, there are no data gaps, and you don’t have to decide which data to track in advance. Pretty neat, huh?
With Userpilot, you can easily set up A/B testing and multivariate testing to collect valuable quantitative data.
For example, you can test different elements, such as onboarding flows, and get data on how different segments interact with them.
You can launch in-app surveys with Userpilot to efficiently gather NPS, CSAT, and CES data. These surveys provide a reliable method for collecting and analyzing quantitative data on user sentiment and overall satisfaction.
Plus, you can enrich these surveys with open-ended questions , allowing you to gather additional qualitative feedback . This combination of quantitative and qualitative data provides a more comprehensive understanding of user experiences and sentiments.
There are many examples of quantitative data, but thankfully there are product analytics tools that make collecting them easier. One of the best ways of achieving this is by automatically capturing key events, which is exactly what Userpilot enables.
If you want to auto-capture key user actions, launch no-code surveys, perform quantitative data analysis, and then create personalized product experiences, book a demo now to see how we can help.
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Examples of quantitative data, types & collection methods.
Exploring Primary Market Research: Types, Benefits, and Real-World Examples
In today’s competitive business landscape, understanding your market and audience is crucial. Primary market research offers a direct line to insights that can drive successful strategies. In this comprehensive guide, we delve deep into answering the question of what primary market research is and compare the differences between primary and secondary market research, as well as qualitative and quantitative methods. Ultimately, this guide will help entrepreneurs and market researchers understand the importance, methods, and benefits of primary market research.
What is Primary Market Research?
Unlike secondary research, which relies on existing data, primary market research is all about gathering fresh, firsthand information directly from your target market.
Think of it as opening a direct line of communication with your potential customers . Through primary research, you can delve into their:
By gathering this in-depth data, you gain a richer understanding of your target market and can make more informed business decisions. This can involve anything from product development and marketing strategies to pricing and customer service initiatives.
Here’s an analogy: Imagine you’re a chef looking to create a new dish. Secondary research would be like reviewing existing cookbooks, while primary research would involve talking to your customers directly, asking about their favorite flavors and dietary restrictions. This personalized approach allows you to craft a dish that truly caters to their preferences.
While both primary and secondary market research serve the purpose of informing business decisions, they differ significantly in their approach to data collection. Here’s a breakdown to help you understand their strengths and weaknesses:
Actively collected new data (surveys, interviews, focus groups, experiments) | Existing data compiled by others (industry reports, government publications, market research databases) | |
In-depth, specific information about target market; uncovers unique insights | Broader understanding of market landscape, trends, and industry benchmarks; may lack specific details | |
More time-consuming and resource-intensive; potentially higher cost | Quicker and often lower cost, especially when using free online resources | |
Tailored to specific needs and target audience | May not address specific business needs or niche markets | |
Conduct focus groups to understand user preferences and habits | Analyze existing market reports on fitness app industry trends and competitors | |
Use in combination with secondary research for comprehensive understanding | Use as a foundation and to identify areas for further primary research |
To better understand this, let’s consider an example. Say you’re a company developing a new fitness app.
In most cases, the best strategy involves using a combination of both primary and secondary research. Secondary research can provide a solid foundation and help you identify areas for further investigation through primary research. This two-pronged approach ensures you have a comprehensive understanding of the market while addressing your specific business needs.
Within the realm of primary market research, two main approaches exist for gathering data: qualitative and quantitative research. Each offers a unique perspective on your target market, and understanding their strengths is crucial for choosing the right method for your research goals.
Imagine researching the appeal of a new eco-friendly cleaning product. Qualitative interviews with potential customers might reveal concerns about product effectiveness alongside a desire to support sustainable practices. This can help you tailor your marketing message to address both aspects.
For example, imagine a survey distributed to a representative sample of your target market could ask about their cleaning product preferences, frequency of purchase, and willingness to pay a premium for eco-friendly options. This data can be statistically analyzed to understand overall market trends and price sensitivity.
The best approach depends on your research objectives. Here’s a general guideline:
Often, the most robust research strategy involves using both methods sequentially. Qualitative research can help you develop targeted questions for your quantitative survey, while quantitative data can add validation and broader context to qualitative findings.
Primary market research equips you with the power to directly tap into the minds of your target audience. By choosing the right method and tools, you can gather invaluable insights that inform critical business decisions. Here’s a breakdown of the most common types of primary research, along with their corresponding tools and how they can be used.
Interviews are in-depth conversations with individual target audience members, allowing for probing questions and exploration of nuanced thoughts and feelings.
Tools: Online interview platforms (e.g., Zoom, Skype), offline face-to-face meetings, phone interviews.
Use Cases: Understanding customer needs and pain points, gathering detailed feedback on product concepts or prototypes, and exploring purchase motivations.
Pro tip: Prelaunch is a comprehensive concept validation equipped to help you compile both quantitative and qualitative data. One of its most sought-after features is the customer insights gained from in-depth interviews that can be coordinated, scheduled, hosted, and analyzed all in one place.
Surveys are questionnaires distributed to a larger sample size, enabling efficient data collection from a broader audience.
Tools: Online survey platforms (e.g., SurveyMonkey , Google Forms ), email surveys, paper-based surveys.
Use Cases: Gauging market sentiment towards a new product or service, measuring brand awareness, gathering customer satisfaction feedback.
Did you know: Prelaunch seamlessly integrates sightful yet brief surveys into the customer journey. When a potential buyer lands on your page they will be asked to answer a few questions about your product, either when they reserve the product or when they confirm their purchase intent. To learn more about the process, check out our concept validation tool .
These are moderated discussions with a small group (typically 6-10 participants) to generate ideas, share opinions, and explore product concepts in a group setting.
Tools : Focus group facilities with recording equipment, online video conferencing platforms.
Use Cases: Identifying user needs and preferences, brainstorming new product ideas, testing prototypes or marketing messages.
Pro tip: If you’re considering organizing a focus group to test your product, Prelaunch can help recruit participants based on specific demographics or interests. Most importantly, any group you gather via Prelaunch will be vetted to include only those who have already confirmed their purchase intent, ensuring higher quality feedback compared to arbitrary focus groups where participants are paid to give answers.
Observing target audience behavior in their natural environment to understand their needs and pain points in context.
Tools: One-way observation mirrors, ethnographic note-taking tools, screen recording software (for website behavior) .
Use Cases: Understanding customer journeys and touchpoints, evaluating product usability, and uncovering unmet customer needs.
Experiment with different versions of a product, marketing message, or website design to see which one performs better.
Tools: Website A/B testing platforms, marketing automation software with A/B testing features.
Use Cases: Optimizing website conversion rates, testing different marketing copy variations, and identifying the most effective product features.
By leveraging these primary market research methods and tools, you can unlock a treasure trove of customer insights. Remember, the most effective approach often involves a combination of these techniques tailored to your specific research goals.
Primary market research involves a series of methodical steps that ensure you gather accurate and actionable data to inform your business decisions.
Before diving into data collection, take a step back and ask yourself: What do you want to learn? Clearly defined research objectives will guide your entire process. Here are some questions to consider:
Once you have a clear understanding of your goals, you can choose the most appropriate primary research method(s).
As we explored earlier, there are various primary research methods, each with its strengths and weaknesses. Consider your research objectives, target audience size, and budget when making your selection. Here’s a quick recap:
Once you’ve chosen your method(s), it’s time to develop the tools you’ll use to collect data. This may involve:
The quality of your data hinges on recruiting the right participants. Consider demographics, interests, and behaviors that align with your target audience. Here are some recruitment strategies:
With your tools and participants in place, it’s time to conduct your research. Here are some pointers for successful data collection:
Once you’ve collected your data, it’s time to transform it into meaningful insights. The analysis approach will vary depending on your chosen method:
Based on your analysis, translate your findings into actionable recommendations. This may involve:
Clearly document your research process, methodology, results, and recommendations. This report can be shared with stakeholders to inform business decisions and future market research efforts.
By following these steps and leveraging the power of primary market research, you can gain a deeper understanding of your target audience and make informed decisions that drive success.
Primary market research empowers you to go beyond assumptions and gather firsthand information directly from your potential customers. This offers a multitude of benefits that can propel your business forward.
Primary research delves into the “why” behind customer behavior, providing insights that traditional data sources might miss. This allows you to make data-driven decisions about product development, marketing strategies, and pricing.
By uncovering unique customer needs and preferences, you can differentiate your offerings from competitors. This valuable intelligence can help you develop innovative products and position yourself for success in a crowded market.
Primary research reduces reliance on guesswork and assumptions. With concrete data about your target market, you can make strategic decisions with greater confidence, leading to a higher return on investment.
By directly engaging with your target audience, you gain a deeper understanding of their needs and wants. This fosters a customer-centric approach, allowing you to tailor your offerings and messaging for maximum impact.
Unlike secondary market research, which relies on existing data, primary research provides fresh, up-to-date insights that reflect current market trends and customer preferences. This ensures you’re basing your decisions on the most relevant information.
By proactively identifying potential challenges or roadblocks through primary research, you can mitigate risks associated with product launches or marketing campaigns.
Primary market research isn’t just a theoretical concept; it’s a driving force behind the success of many renowned brands. Here are a few real-life examples of how companies leveraged primary research to gain valuable customer insights:
The iconic beverage company understands the importance of staying ahead of the curve. Coca-Cola routinely conducts taste tests, a form of primary research, to gauge consumer preferences for new and existing flavors. This allows them to refine their offerings and maintain their market dominance.
Apple is known for its innovative product design. They achieve this partly through focus groups, a form of primary research, where potential users interact with prototypes and offer feedback. This allows Apple to identify usability issues, assess design preferences, and ultimately create products that resonate with their target audience .
The streaming giant personalizes the user experience to keep viewers engaged. Netflix utilizes A/B testing, a form of primary research, to compare different website layouts, recommendation algorithms, and even video thumbnails. By analyzing user behavior, they can determine which variations lead to higher engagement and optimize the overall user experience.
The building block company recognizes the power of its community. LEGO actively analyzes user-generated content, a form of observation research, on social media platforms. This allows them to identify popular building techniques, understand user preferences for specific themes, and develop new product lines that cater to their audience’s passions.
Even Hollywood heavyweights rely on primary research. Director Steven Spielberg famously conducted test screenings of his movies, a form of observation research, before their official release. By gauging audience reactions, he could identify confusing plot points, ineffective scenes, and make adjustments to ensure the final product resonated with viewers.
These examples showcase the diverse ways companies leverage primary market research. By directly engaging with their target audience, these brands gain invaluable insights that translate into successful products, services, and marketing campaigns.
Primary market research is a powerful tool for gaining exclusive, tailored insights that can drive your business forward. By understanding the various methods and their benefits, you can make informed decisions that set you apart from the competition.
Ready to take the next step? Start implementing these strategies today and experience the advantages of primary market research firsthand!
By following these guidelines, you can effectively utilize primary market research to gain a competitive edge and ensure your business decisions are backed by reliable data.
The relationship between religiosity, spirituality and health has received increasing attention in the academic literature. Studies involving quantitative measurement of religiosity and/or spirituality (R/S) and health have reported many positive associations between these constructs. The quality of various measures, however, is very important in this field, given concerns that some measures of R/S have been contaminated with indicators of mental health. When this occurs, that is when R/S is defined and measured a priori, this subsequently guarantees a positive association between R/S and health (especially mental health). Such associations are called tautological, which involves correlating a construct with itself, thus producing associations that are uninterpretable and misleading. In this article, concerns about the measurement of R/S are discussed, examples of contaminated and potentially probelmatic measures of R/S are noted, and recommendations are made regarding uncontaminated measures of R/S that should be used in future studies of R/S and health.
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Harold G. Koenig
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Department of Psychiatry, Shiraz University of Medical Sciences, Shiraz, Iran
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Lindsay B. Carey
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Institute for Ethics and Society, University of Notre Dame, Sydney, New South Wales, 2007, Australia
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Koenig, H.G., Carey, L.B. Religion, Spirituality and Health Research: Warning of Contaminated Scales. J Relig Health (2024). https://doi.org/10.1007/s10943-024-02112-6
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Nearly three in four U.S. medical students graduate with debt in six-figure dollar amounts which impairs students emotionally and academically and impacts their career choices and lives long after graduation. Schools have yet to develop systems-level solutions to address the impact of debt on students’ well-being. The objectives of this study were to identify students at highest risk for debt-related stress, define the impact on medical students’ well-being, and to identify opportunities for intervention.
This was a mixed methods, cross-sectional study that used quantitative survey analysis and human-centered design (HCD). We performed a secondary analysis on a national multi-institutional survey on medical student wellbeing, including univariate and multivariate logistic regression, a comparison of logistic regression models with interaction terms, and analysis of free text responses. We also conducted semi-structured interviews with a sample of medical student respondents and non-student stakeholders to develop insights and design opportunities.
Independent risk factors for high debt-related stress included pre-clinical year (OR 1.75), underrepresented minority (OR 1.40), debt $20–100 K (OR 4.85), debt >$100K (OR 13.22), private school (OR 1.45), West Coast region (OR 1.57), and consideration of a leave of absence for wellbeing (OR 1.48). Mental health resource utilization ( p = 0.968) and counselors ( p = 0.640) were not protective factors against debt-related stress. HCD analysis produced 6 key insights providing additional context to the quantitative findings, and associated opportunities for intervention.
We used an innovative combination of quantitative survey analysis and in-depth HCD exploration to develop a multi-dimensional understanding of debt-related stress among medical students. This approach allowed us to identify significant risk factors impacting medical students experiencing debt-related stress, while providing context through stakeholder voices to identify opportunities for system-level solutions.
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Over the past few decades, it has become increasingly costly for aspiring physicians to attend medical school and pursue a career in medicine. Most recent data shows that 73% of medical students graduate with debt often amounting to six-Fig [ 1 ]. – an amount that is steadily increasing every year [ 2 ]. In 2020, the median cost of a four-year medical education in the United States (U.S.) was $250,222 for public and $330,180 for private school students [ 1 ] – a price that excludes collateral costs such as living, food, and lifestyle expenses. To meet these varied costs, students typically rely on financial support from their families, personal means, scholarships, or loans. Students are thereby graduating with more debt than ever before and staying indebted for longer, taking 10 to 20 years to repay their student loans regardless of specialty choice or residency length [ 1 ].
Unsurprisingly, higher debt burden has been negatively correlated with generalized severe distress among medical students [ 3 , 4 ], in turn jeopardizing their academic performance and potentially impacting their career choices [ 5 ]. Studies have found that medical students with higher debt relative to their peers were more likely to choose a specialty with a higher average annual income [ 5 ], less likely to plan to practice in underserved locations, and less likely to choose primary care specialties [ 4 ]. However, a survey of 2019 graduating medical students from 142 medical schools found that, when asked to rank factors that influenced their specialty choice, students ranked economic factors, including debt and income, at the bottom of the list. With this inconsistency in the literature, authors Youngclaus and Fresne declare that further studies and analysis are required to better understand this important relationship [ 1 ].
Unfortunately, debt and its negative effects disproportionately impact underrepresented minority (URM) students, including African Americans, Hispanic Americans, American Indian, Native Hawaiian, and Alaska Native [ 6 ], who generally have more debt than students who are White or Asian American [ 1 ]. In 2019, among medical school graduates who identified as Black, 91% reported having education debt, in comparison to the 73% reported by all graduates [ 1 ]. Additionally, Black medical school graduates experience a higher median education debt amount relative to other groups of students, with a median debt of $230,000 [ 1 ]. This inequitable distribution of debt disproportionately places financial-related stress on URM students [ 7 ], discouraging students from pursuing a medical education [ 8 ]. These deterring factors can lead to a physician workforce that lacks diversity and compromises health equity outcomes [ 9 ].
Limited literature exists to identify the impact of moderating variables on the relationship between debt and debt-related stress. Financial knowledge is found to be a strong predictor of self-efficacy and confidence in students’ financial management, leading to financial optimism and potentially alleviating debt stress [ 10 , 11 , 12 ]. Numerous studies list mindfulness practices, exercise, and connecting with loved ones as activities that promote well-being and reduce generalized stress among students [ 13 , 14 , 15 ]. However, to date, no studies have examined whether these types of stress-reducing activities, by alleviating generalized stress, reduce debt-related stress. Studies have not examined whether resources such as physician role models may act as a protective factor against debt-related stress.
Despite the growing recognition that debt burdens medical students emotionally and academically, we have yet to develop systemic solutions that target students’ unmet needs in this space. We performed the first multi-institutional national study on generalized stress among medical students, and found that debt burden was one of several risk factors for generalized stress among medical students [ 3 ]. The goal of this study is to build upon our findings by using a mixed methods approach combining rigorous survey analysis and human-centered design to develop an in-depth understanding of the impact that education debt has on medical students’ emotional and academic well-being and to identify opportunities for intervention.
We conducted a mixed methods, cross-sectional study that explored the impact of debt-related stress on US medical students’ well-being and professional development. This study was conducted at the University of California, San Francisco (UCSF). All activities were approved by the UCSF institutional review board, and informed consent was obtained verbally from participants prior to interviews. We performed a secondary analysis of the quantitative and qualitative results of the Medical Student Wellbeing Survey (MSWS), a national multi-institutional survey on medical student wellbeing administered between 2019 and 2020, to determine risk factors and moderating variables of debt-related stress. To further explore these variables, we used human-centered design (HCD), an approach to problem-solving that places users at the center of the research process in order to determine key pain points and unmet needs, and co-design solutions tailored to their unique context [ 16 ]. In this study, we performed in-depth, semi-structured interviews with a purposefully sampled cohort of medical students and a convenience sample of non-student stakeholders to determine key insights representing students’ unmet needs, and identified opportunities to ameliorate the impact of debt-related stress on medical students.
The MSWS is a survey to assess medical student wellbeing that was administered from September 2019 to February 2020 to medical students actively enrolled in accredited US or Caribbean medical schools [ 3 ]. Respondents of the MSWS represent a national cohort of > 3,000 medical students from > 100 unique medical school programs. The MSWS utilizes a combination of validated survey questions, such as the Medical Student Wellbeing Index (MS-WBI), and questions based on foundations established from previously validated wellbeing survey methods [ 3 ]. Questions generally focused on student demographics, sources of stress during medical school, specialty consideration, and frequency in activities that promote wellbeing. Some questions ask students to rate physical, emotional, and social domains of wellbeing using a five-point Likert scale. Questions of interest from the MSWS included debt-related stress, generalized stress, intended specialty choice, and utilization of well-being resources and counselors. An additional variable investigated was average school tuition, which was determined by a review of publicly available data for each student’s listed medical school [ 17 ]. All data from the MSWS was de-identified for research purposes.
Debt stress was assessed by the question, “How does financial debt affect your stress level?” Students responded using a five-point Likert scale from − 2 to 2: significant increase in stress (-2), mild increase (-1), no change (0), mild decrease (1), or significant decrease (2). Responses for this question were evaluated as a binary index of ‘high debt stress,’ defined as a response of − 2, versus ‘low debt stress,’ defined as a response of − 1 or 0. In addition, generalized stress from the MSWS was assessed by questions from the embedded MS-WBI, which produced a score. Previous studies have shown that the score can be used to create a binary index of distress: a score ≥ 4 has been associated with severe distress, and a score < 4 has been associated with no severe distress [ 18 ].
We categorized students’ responses to intended specialty choice by competitiveness, using the 2018 National Resident Match Program data [ 19 ]. ‘High’ and ‘low’ competitiveness were defined as an average United States Medical Licensing Examination (USMLE) Step 1 score of > 240 or ≤ 230, respectively, or if > 18% or < 4% of applicants were unmatched, respectively. ‘Moderate’ competition was defined as any specialty not meeting criteria for either ‘high’ or ‘low’ competitiveness.
The MSWS assessed the utilization of well-being resources by the question, “At your institution, which of the following well-being resources have you utilized? (Select all that apply)” Students responded by selecting each of the resource(s) they used: Mental Health and Counseling Services, Peer Mentorship, Self-Care Education, Mindfulness/Meditation Classes, Community Building Events, and Other. The number of choices that the student selected was calculated, allowing for placement into a category depending on the amount of resource utilization: 0–20%, 20–40%, 40–60%, 60–80%, 80–100%. Responses for this question were evaluated as a binary index of ‘high resource utilization,’ defined as a response of 80–100% resource utilization, versus ‘low resource utilization,’ defined as a response of < 80% resource utilization. The co-authors collaboratively decided upon this “top-box score approach,” [ 20 ] which is the sum of percentages for the most favorable top one, two or three highest categories on a scale, to assess if the most extreme users (80–100%) of these supportive resources experienced a decrease in debt-related stress. Additionally, use of a counselor for mental health support was assessed by the question, “Which of the following activities do you use to cope with difficult situations (or a difficult day on clinical rotation)? (Select all that apply).” Students responded by selecting the activities that they use from a list (e.g., listen to music, mindfulness practice, meet with a counselor, exercise). Responses for this question were evaluated as a binary index of ‘Meeting with a Counselor,’ defined by selection of that option, versus ‘Not Meeting with a Counselor,’ defined as not selecting that option.
We performed a secondary analysis of quantitative data from the MSWS to calculate frequencies and odds ratios for the five quantitative variables described above (debt-related stress, generalized stress, intended specialty, resource utilization, and school tuition). Tests performed are summarized in Table 1 (“Secondary Analysis Tests Performed”). Univariate analysis and multivariate logistic regression were performed among students in the high debt stress (-2) and low debt stress (0 or − 1) for select variables, such as clinical phase, URM, debt burden, specialty competitiveness, and average school tuition, to identify risk factors for high debt stress. To determine if ‘high resource utilization’ or ‘meeting with a counselor’ were moderating variables on the relationship between debt burden and debt stress, we applied the logistic regression with the interaction terms of ‘debt’ and ‘resource utilization’ (high vs. low). Then, we performed a similar analysis but replaced the interaction term with ‘debt’ and ‘meeting with a counselor’ (yes vs. no). We also performed Chi-squared tests to determine the degree to which severe distress increases as debt burden increases, if specialty competitiveness varied by debt stress, and if the proportion of students who identified as URM, in comparison to non-URM, differed by debt level. All statistical tests were two-sided and p < 0.05 was considered significant. Statistical analyses were performed using SAS version 9.4 and R version 4.0.5.
Free-text entries.
At the conclusion of the 2019–2020 MSWS, respondents had unlimited text space to provide comments to two prompts. The first prompt read, “What well-being resource(s), if offered at your school, do you feel would be most useful?” The second prompt read “If you have any further comments to share, please write them below.” Answers to either prompt that pertained to debt, cost of medical school, or finances were extracted for the purpose of this study and analyzed with the other qualitative data subsequently described.
Interview participants were identified from a repository of respondents to the MSWS who had attached their email address and expressed willingness at the time of the survey to be contacted for an interview [ 3 ]. Our recruitment period was between April 19, 2021 to July 2, 2021. The recruitment process involved sending invitations to all of the email addresses in the list to participate in a 45-minute interview on the topic of student debt and wellbeing. The invitation included a brief screening questionnaire asking students to report updates to questions that were previously asked in the MSWS (i.e.: clinical training year, marital status, dependents). Additional novel questions included primary financial support system, estimate of financial support systems’ household income in the last year, estimate of educational financial debt at conclusion of medical school, student’s plan for paying off debt, and degree of stress (using a Likert scale from 0 to 10) over current and future education debt.
Purposeful sampling of medical student stakeholders for interviews allowed us to maximize heterogeneity. We utilized the students’ responses to the brief screening questionnaire with their corresponding responses to demographic questions from the MSWS to select interviewees that varied by gender, race, presence of severe distress, type of medical school (public vs. private), region of school, and tuition level of school. The sampling ensured a diverse representation, in accordance with HCD methodology [ 21 ]. Brief descriptions of participant experiences are listed in Table 2 (“Interviewee Descriptors”). Students who were selected for interviews were sent a confirmation email to participate. Interviews were to be conducted until thematic saturation was reached. In addition, to include representation from the entire ecosystem, we interviewed a financial aid counselor at a medical school and a pre-medical student, chosen through convenience sampling. We directly contacted those two individuals for interviews.
All interviews were conducted between April 2021 and July 2021 over Zoom. A single researcher conducted interviews over an average of 45 min. Informed consent was obtained verbally from participants prior to interviews; interviews and their recordings only proceeded following verbal consent. The interview guide (S1 File) included open-ended questions about students’ experience of debt-related stress and their reflections on its consequences. The audio recordings were transcribed using Otter.ai, a secure online transcription service that converts audio files to searchable text files. Interview responses were redacted to preserve anonymity of respondent identity.
Interview data was analyzed using a general inductive approach to thematic analysis. Specifically, two researchers (SL and AY) independently inductively analyzed transcripts from the first three semi-structured interviews to come up with themes relating to the experiences and consequences of debt-related stress. They reconciled discrepancies in themes through discussion to create the codebook (S2 File), which included 18 themes. SL and AY independently coded each subsequent interview transcript as well as the free text responses from the survey, meeting to reach a consensus on representative quotes for applicable themes.
Following the HCD methodology, two researchers met with the core team to discuss the themes from the interviews and translate them into “insight statements”, which reflect key tensions and challenges experienced by stakeholders. Insight statements carefully articulate stakeholders’ unique perspectives and motivations in a way that is actionable for solution development [ 22 ]. As such, these insight statements are reframed into design opportunities, which suggest that multiple solutions are possible [ 23 , 24 ]. For example, discussion about themes 1a and 1b (“Questionable Job Security” and “Disappointing MD salary and Satisfaction Payoff”) revealed that they were related in the way that they led students to wonder whether the investment in medical school would be offset by the salary payoff. This led to the identification of the tension for low-income students in particular, who have to weigh this tradeoff earlier in their medical school journey than other students who are less financially-constrained (insight: “Medical school is a risky investment for low-income students”.) The design opportunity logically translates into a call to action for brainstorming and solution development: “Support low-income students to make values-based tradeoffs when considering a career in medicine.”
A total of 3,162 students responded to the MSWS and their sociodemographic characteristics have been described previously [ 3 ]. A total of 2,771 respondents (87.6%) responded to our study’s variables of interest, including a response for ‘high debt stress’ (–2) or ‘low debt stress’ (–1 or 0). Table 3 lists the distribution of debt-related stress across different variables for all respondents.
Factors that were independently associated with higher debt-related stress included being in pre-clinical year (OR 1.75, 95% CI 1.30–2.36, p < 0.001), identifying as URM (OR 1.40, 95% CI 1.03–1.88), p = 0.029), having debt $20–100 K (OR 4.85, 95% CI 3.32–7.30, p < 0.001), debt > 100 K (OR 13.22, 95% CI 9.05–19.90, p < 0.001), attending a private medical school (OR 1.45, 95% CI 1.06–1.98, p = 0.019), attending medical school on the West Coast (OR 1.57, 95% CI 1.17–2.13, p = 0.003), and having considered taking a leave of absence for wellbeing (OR 1.48, 95% CI 1.13–1.93, p = 0.004) (Table 4 , S1 Table).
Levels of generalized severe distress differed across debt burden groups. As debt level increased, the percentage of individuals with “severe” distress increased ( p < 0.001).
There were significant differences between the high debt stress versus low debt stress groups and plans to pursue highly vs. moderately vs. minimally competitive specialties ( p = 0.027) (Fig. 1 ) A greater percentage of low debt stress students were pursuing a highly competitive specialty or a minimally competitive specialty. A greater percentage of high debt stress students were pursuing a moderately competitive specialty. As shown in Table 4 , there were no differences in debt-associated stress between students who choose different specialties, such as medical versus surgical versus mixed (medical/surgical).
Debt stress by specialty competitiveness
URM identity was an independent risk factor for higher debt-related stress (Table 4 ) In addition, debt levels varied between those who identify as URM versus non-URM ( p < 0.001). Students identifying as URM tended to have higher debt than those who did not. Although the percentage of non-URM students was higher than that of URM students within the lowest debt burden category (<$20k), among all higher debt burden categories, including $20–100 K, $100–300 K, and >$300K, the percentage of URM students was higher than the percentage of non-URM students.
Protective factors such as high degree of mental health resource utilization and meeting with a counselor did not reduce the impact of debt burden on debt stress. Among students who reported a high degree of mental health resource utilization, there was no impact on the relationship between debt and debt stress ( p = 0.968). Similarly, meeting with a counselor had no impact on the relationship between debt and debt stress ( p = 0.640).
We conducted in-depth, semi-structured interviews with 11 medical students, who are briefly described in Table 2 . We reached thematic saturation with 11 interviews, a point at which we found recurring themes. Therefore, no further interviews were needed. Among the medical student interviewees, there was representation from all regions, including the Northeast ( n = 3), West Coast ( n = 5), Midwest ( n = 2), and South ( n = 1). Students were also from all clinical phases, including pre-clinical ( n = 3), clinical ( n = 4), gap year/other ( n = 2), and post-clinical ( n = 2). Most interviewees were female ( n = 8) and 5 of the interviewees identified as URM. Financial support systems were diverse, including self ( n = 3), spouse/partner ( n = 3), and parents/other ( n = 5). Most interviewees reported low debt stress ( n = 8), as opposed to high debt stress ( n = 3). 55% of interviewees planned to pursue specialties that pay <$300K ( n = 6), with some pursuing specialties that pay $300–400 K ( n = 2) and >$400K ( n = 3).
Among the MSWS free-text responses, to the prompt, “What well-being resource(s), if offered at your school, do you feel would be most useful?” 20 of 118 respondents (16.9%) provided free-text responses that pertained to debt, cost of medical school, or finances. To the prompt “If you have any further comments to share, please write them below” 11 of 342 students (3.2%) provided relevant free-text responses. Analysis of the free-text responses and semi-structured interviews revealed 6 distinct insights (Table 5 ), with each insight translated into an actionable design opportunity.
Medical school is a risky investment for low-income students.
The personal and financial sacrifices required for low-income students to attend medical school and pursue a career in medicine outweigh the benefits of becoming a physician. When considering a career in medicine, students feel discouraged by questionable job security (theme 1a) and reduced financial compensation (theme 1b) – a combination that jeopardizes immediate and long-term job satisfaction. Some students feel hopeful that their decision to pursue medicine will be personally rewarding (1b.6) and their salaries will stabilize (1a.1, 1a.5), but many low-income students experience doubt about whether they made the right career choice (1b.2, 1b.4, 1b.6), and feel stressed that they will be in debt for longer than they expected (1a.3, 1a.4, 1b.1, 1b.5). Support low-income students to make values-based tradeoffs when considering a career in medicine.
Support low-income students to make values-based tradeoffs when considering a career in medicine.
Medical schools lack the adaptive infrastructure to be welcoming to low-income students.
Students face financial challenges from the moment they apply to medical school (theme 2a), a costly process that limits admissions options for low-income students due to their inability to pay for numerous application fees (2a.1) and expensive test preparation courses (2a.2, 2a.3). Once students begin medical school, they feel unsupported in their varied responsibilities towards their families (theme 2b) and additional financial needs (theme 2c), requiring them to make tradeoffs with their education and personal lives (2b.2, 2c.1).
Develop flexible systems that can recognize and accommodate students’ complex financial needs during medical school.
Students worry about the impact that their medical school debt has on their present and future families, which compounds feelings of guilt and anxiety.
For students who need to take loans, the decision to pursue a career in medicine is a collective investment with their families. Students feel guilty about the sacrifices their families have to make for the sake of their career (theme 3a) and feel pressure to continue to provide financially for their family while having debt (theme 3b). Students are stressed about acquiring more debt throughout their training (3a.1) and the impact that has on loved ones who are dependent on them (3a.4, 3a.5, 3b.2), especially with respect to ensuring their financial security in the future (3b.4).
Create an environment that acknowledges and accounts for the burden of responsibility that students face towards their families.
Without the appropriate education about loans, the stress of debt is exponentially worse.
Students feel the greatest fear around loans when they do not understand them, including the process of securing loans and paying off debt (theme 4a). Students are overwhelmed by their loan amounts (4a.5) and lack the knowledge or resources to manage their debt (4a.1, 4a.2), making them uncertain about how they will become debt-free in the future (4a.3, 4a.4). Students reported that various resources helped to alleviate those burgeoning fears (theme 4b), including financial aid counselors (4b.2, 4b.3) and physician role models (4b.5, 4b.6) that generally increase knowledge and skills related to debt management (4b.1).
Empower students to become experts in managing their debt by making loan-related resources more available and accessible.
The small, daily expenses are the most burdensome and cause the greatest amount of stress.
Students with educational debt are mentally unprepared for the burden of managing their daily living expenses (theme 5a), causing them to make significant lifestyle adjustments in the hopes to ease their resulting anxiety (theme 5b). These costs are immediate and tangible, compared to tuition costs which are more distant and require less frequent management (5a.3) Students learn to temper their expectations for living beyond a bare minimum during medical school (5a.1, 5b.2, 5b.4) and develop strategies to ensure that their necessary expenses are as low as possible (5b.1, 5b.2, 5b.3, 5b.4).
Develop and distribute resources to support both short- and long-term financial costs for medical students.
Students view debt as a dark cloud that constrains their mental health and dictates their career trajectory.
The constant burden of educational debt constrains students’ abilities to control their mental health (theme 6a) and pursue their desired career path in medicine (themes 6b & 6c). Students feel controlled by their debt (6a.3) and concerned that it will impact their [ability] to live a personally fulfilling life (6a.1, 6a.2, 6c.6), especially with respect to pursuing their desired medical specialties (6b.1, 6c.3, 6c.5, 6c.6). Students with scholarships, as opposed to loans, felt more able to choose specialties that prioritized their values rather than their finances (6c.1, 6c.2), an affordance that impacts long-term career growth and satisfaction.
Create a culture of confidence for managing debt and debt-stress among medical students.
This is the first multi-institutional national study to explore the impact of debt-related stress on medical students’ well-being in the United States. We used an innovative, mixed methods approach to better understand the factors that significantly affect debt-related stress, and propose opportunities for improving medical student well-being.
Analysis of survey results found that students who identify as URM are more likely to experience higher levels of debt-related stress than non-URM students. Our study also found that among all higher debt burden categories, debt levels were higher for URM students, findings consistent with studies that have shown the disproportionate burden of debt among URM students [ 1 ]. Our semi-structured interviews illuminated that students from low-income backgrounds feel unsupported by their medical schools in these varied financial stressors that extend beyond tuition costs (insight 2), leaving their needs unmet and increasing financial stress over time: “We don’t have different socio-economic classes in medicine because there’s constantly a cost that [isn’t] even factored into tuition cost [and] that we can’t take student loans for.” Many URM students feel especially stressed by their financial obligations towards their families (insight 3), and describe the decision to enter into medicine as one that is collective ( “the family’s going to school” ) rather than individual, placing additional pressure on themselves to succeed in their career: “ Being of low SES , the most significant stressor for me is the financing of medical school and the pull of responsibility for my family.” Several other studies from the literature confirm that students who identify as URM and first generation college or medical students are at higher risk for financial stress compared to their counterparts [ 7 ], and report that they feel as though it is their responsibility to honor their families through their educational and career pursuits [ 25 ]. Our study demonstrates and describes how low-income and URM students face numerous financial barriers in medical school, resulting in medical trainees that are less diverse than the patient populations they are serving [ 1 , 8 ].
Our quantitative analysis found that students with debt amounts over $100,000 are at much higher risk for experiencing severe stress than students with debt less than that amount. Although this finding may seem intuitive, it is important to highlight the degree to which this risk differs between these two cohorts. Students with debt amounts between $20,000 and $100,000 are approximately 5 times more likely to experience high stress than students with debt less than $20,000, while students with debt amounts over $100,000 are approximately 13 times more likely to experience severe stress when compared to the same cohort. Interview participants describe that the more debt they have, the less hopeful they feel towards achieving financial security (insight 1): “There are other healthcare professionals that will not accrue the same amount of loans that we will , and then may or may not have the same salary or privileges […] makes me question , did I do the right thing?” Students internalize this rising stress so as not to shift the feelings of guilt onto their families (insight 3), thereby compounding the psychological burden associated with large amounts of debt (insight 6): “As long as you’re in debt , you’re owned by someone or something and the sooner you can get out of it , the better; the sooner I can get started with my life.”
According to our survey analysis, students who are in their pre-clinical years are at higher risk for stress than students in their clinical years. Our interview findings from insight 4 suggest that students feel initially overwhelmed and unsure about what questions to ask ( “One of my fears is that I don’t know what I don’t know”) or how to manage their loans so that it doesn’t have a permanent impact on their lives: “The biggest worry is , what if [the debt] becomes so large that I am never able to pay it off and it ends up ruining me financially.” Pre-clinical students may therefore feel unsure or ill-equipped to manage their loans, making them feel overwhelmed by the initial stimulus of debt. By the time students reach their clinical years, they may have had time to develop strategies for managing stress, acquire more financial knowledge, and/or normalize the idea of having debt.
Our survey analysis found several risk factors related to medical school characteristics. First, we found that students who attended a private school were at higher risk for debt-related stress than students who attended a public school. Not only is the median 4-year cost of attendance in 2023 almost $100,000 higher in private compared to public medical schools [ 26 ], but it is also the case that financial aid packages are more liberally available for public schools due to state government funding [ 27 ]. This not only relieves students from having higher amounts of debt, but it also creates a more inclusive cohort of medical students. Insight 2 from our interviews suggests that private medical schools without the infrastructure to meet students’ varying financial needs force low-income students to make tradeoffs between their education and personal lives.
Another characteristic that was found to be a risk factor for debt stress was attending a medical school on the West Coast (compared to a non-coastal school.) This was a surprising finding given that tuition rates for both private and public schools on the West Coast are no higher than those in other regions [ 17 ]. The distribution of survey respondents did not vary significantly across regional categories, so no bias in sample size is suspected. While these interviews were not designed to address the reasoning behind students’ choice of medical school matriculation, there is a potential explanation for this finding. Historically, students match for residency programs that are in their home state or not far from their home state; [ 28 , 29 ] therefore, we speculate that students may prefer to settle on the West Coast, and may be willing to take on more financial debt in pursuit of their long-term practice and lifestyle goals.
Our quantitative analysis found that students who reported having considered taking a leave of absence for well-being purposes were at higher risk for debt-related stress. This cohort of students likely experience higher levels of stress as they are conscious of the negative impact it has on their life, and have already ruminated on leaving medical school. A study by Fallar et al. found that the period leading up to a leave of absence is particularly stressful for students because they are unfamiliar with the logistics of taking time off, and don’t feel as though leaving medical school is encouraged or normalized for students [ 30 ]. An interview with a student who did a joint MD and PhD program expressed having more time for herself during her PhD program, and described using money for activities that could alleviate stress (“I took figure skating during my PhD”) rather than create more stress by compromising on their lifestyle during medical school (insight 5). More research may be needed to better understand and support students considering taking a leave of absence from medical school.
Our study found that students with high debt stress pursue moderately competitive specialties compared to students with low debt stress. This may be explained by the fact that low debt stress gives students the freedom to pursue minimally competitive specialties, which may be more fulfilling to them but typically have lower salaries. Insight 6 further elaborates upon this finding that students with high debt stress deprioritize specialties for which they are passionate in favor of higher paying specialties that might alleviate their debt: “I love working with kids…but being an outpatient pediatrician just wasn’t going to be enough to justify the [private school] price tag.” Students with lower debt stress describe having the freedom to choose specialties that align with their values, regardless of anticipated salary: “Scholarships give me the freedom to do [specialties] that maybe are a little bit less well-paying in medicine.” Interestingly, certain studies examining the relationship between specialty choice and debt stress have found that high debt stress is associated with a higher likelihood of pursuing a more competitive, and presumably higher paying, specialty [ 5 ]. More research investigating the relationship between debt stress and specialty choice could illuminate opportunities for increasing a sense of agency and overall satisfaction among students for their career choices.
In our exploration of potential protective factors against the effects of debt-related stress, our survey analysis found that the two variables measured (high mental health resource utilization and meeting with a counselor) did not have any impact on reducing debt-related stress. This finding is inconsistent with the literature, which considers these activities to promote general well-being among students but has never been studied in the context of debt-related stress [ 13 , 14 , 15 ]. A potential explanation is that the survey questions that assessed these activities were imperfect. For example, the question of meeting with a counselor was not a standalone question, but instead, was at the bottom of a list of other wellbeing activities; therefore, students may have been fatigued by the time they got to the bottom of the list and not selected it. Additionally, our definition of “high” mental health resource utilization may have been perceived as too strict (i.e.: 80–100%) and perhaps we would have seen effects at lower percentages of utilization (i.e.: 40–60%). Despite this finding, students describe in their interviews that having access to certain resources such as financial knowledge and physician role models can help to alleviate stress by helping them feel confident in managing their loans in the immediate and more distant future (insight 4): “I’ve had explicit discussions with physicians who went to med school , had debt , paid it off [.] the debt hasn’t hindered their life in any way. I think that just makes me feel a lot calmer.” This finding aligns with previous studies that suggest that financial knowledge, such as knowledge about loans and a payoff plan, confers confidence in students’ financial management [ 11 , 12 ]. These factors are also aligned with previous studies that suggest financial optimism, such as with a physician role model who successfully paid off loans, is associated with less financial stress [ 10 ].
Our quantitative analysis of risk factors helped us to identify which areas might significantly impact debt-related stress among medical students, while our qualitative analysis provided more in-depth insight into those risk factors for more human-centered intervention design. The HCD process not only provides additional context from the perspective of medical students, but also proposes distinct design opportunities upon which interventions may be designed and tested. Drawing from the six design opportunities outlined in this paper, we propose a solution on a national scale: lowering the cost of the MCAT and medical school applications to reduce the financial barrier to applying to medical school [ 31 ]. We also propose the following solutions that can be implemented at the level of medical schools to better support medical students facing debt-related stress: (1) providing adequate financial aid that prevents low-income students from needing to work while being in medical school [ 32 ], (2) providing targeted financial planning classes and counseling for first-year medical students who have taken loans [ 33 ], and (3) creating mentorship programs that pair medical students with debt with physician role models who had also had debt but successfully paid it off [ 34 ]. We encourage medical schools to consider these suggestions, choosing the ideas from the list that make sense and tailoring them as necessary for their students and their unique needs. Additionally, given that our quantitative portion of the study was a secondary analysis of a survey focused on general medical student well-being, a nationwide study is needed that is specifically designed to explore the topic of debt-related stress among medical students. Furthermore, more research is needed that assesses the impact of activities that promote well-being (e.g., access to therapy, mindfulness practices, exercise) on debt-related stress among medical students.
Our study had some notable limitations. One potential limitation is that our data collection occurred between 2019 and 2021 for this publication in 2023. Additionally, as described in the original study [ 3 ], a limitation of the MSWS is the inability to determine a response rate of students due to the survey distribution by medical student liaisons from each medical school; under the reasonable assumption that the survey was distributed to every US allopathic medical student, the response rate was estimated to have been 8.7%. 3 An additional limitation is the potential for response bias [ 3 ]. A limitation of the qualitative interviews is the potential for response bias among the interviewees. Although we purposely sampled, the students who accepted the invitation to interview may have been students with extreme views, either very negative views of debt or very neutral views of debt. Additionally, the interviewees were not representative of all possible financial situations, given that most students were from private schools, which typically have higher tuition rates. Also, all students had debt amounts in the middle and high categories, with none in the low category. Finally, our model of risk factors for debt-related stress suggested the presence of negative confounding factors, which exerted effects on specific variables (i.e.: pre-clinical year, West Coast) for which univariate analysis found no significant associations but multivariate analysis did. We did not perform further analysis to identify which variables served as the negative confounding variables.
In conclusion, our mixed methods, cross-sectional study exploring debt-related stress and its impact on US medical students’ wellbeing and professional development revealed a set of risk factors and design opportunities for intervention. By using a combined quantitative and qualitative HCD approach, we were able to develop a broad, in-depth understanding of the challenges and opportunities facing medical students with education debt. With these efforts to support the well-being and academic success of students at higher risk of debt-related stress, medical education institutions can develop and nurture a more diverse medical field that can best support the needs of future patients.
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We thank the members of The Better Lab, including Devika Patel, Christiana Von Hippel, and Marianna Salvatori, for their support. We appreciate Pamela Derish (UCSF) for assistance in manuscript editing and the UCSF Clinical and Translational Science Institute (CTSI) for assistance in statistical analysis. This publication was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR001872. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Funding was not obtained for this project.
Adrienne Yang, Simone Langness and Lara Chehab contributed equally to this work.
Department of Surgery, University of California, San Francisco, CA, USA
Adrienne Yang, Lara Chehab & Amanda Sammann
Department of Trauma Surgery, Sharp HealthCare, San Diego, CA, USA
Simone Langness
Department of Pediatrics, Stanford University, Stanford, CA, USA
Nikhil Rajapuram
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
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A.Y. and L.C. wrote the main manuscript text and prepared the figures. S.L. created the study design. All authors reviewed the manuscript.
Correspondence to Adrienne Yang .
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The authors declare no competing interests.
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Yang, A., Langness, S., Chehab, L. et al. Medical students in distress: a mixed methods approach to understanding the impact of debt on well-being. BMC Med Educ 24 , 947 (2024). https://doi.org/10.1186/s12909-024-05927-9
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Background Nearly three in four U.S. medical students graduate with debt in six-figure dollar amounts which impairs students emotionally and academically and impacts their career choices and lives long after graduation. Schools have yet to develop systems-level solutions to address the impact of debt on students' well-being. The objectives of this study were to identify students at highest ...