Taking a complexity perspective.
The first paper in this series 17 outlines aspects of complexity associated with complex interventions and health systems that can potentially be explored by different types of evidence, including synthesis of quantitative and qualitative evidence. Petticrew et al 17 distinguish between a complex interventions perspective and a complex systems perspective. A complex interventions perspective defines interventions as having “implicit conceptual boundaries, representing a flexible, but common set of practices, often linked by an explicit or implicit theory about how they work”. A complex systems perspective differs in that “ complexity arises from the relationships and interactions between a system’s agents (eg, people, or groups that interact with each other and their environment), and its context. A system perspective conceives the intervention as being part of the system, and emphasises changes and interconnections within the system itself”. Aspects of complexity associated with implementation of complex interventions in health systems that could potentially be addressed with a synthesis of quantitative and qualitative evidence are summarised in table 2 . Another paper in the series outlines criteria used in a new evidence to decision framework for making decisions about complex interventions implemented in complex systems, against which the need for quantitative and qualitative evidence can be mapped. 16 A further paper 18 that explores how context is dealt with in guidelines and reviews taking a complexity perspective also recommends using both quantitative and qualitative evidence to better understand context as a source of complexity. Mixed-method syntheses of quantitative and qualitative evidence can also help with understanding of whether there has been theory failure and or implementation failure. The Cochrane Qualitative and Implementation Methods Group provide additional guidance on exploring implementation and theory failure that can be adapted to address aspects of complexity of complex interventions when implemented in health systems. 19
Health-system complexity-related questions that a synthesis of quantitative and qualitative evidence could address (derived from Petticrew et al 17 )
Aspect of complexity of interest | Examples of potential research question(s) that a synthesis of qualitative and quantitative evidence could address | Types of studies or data that could contribute to a review of qualitative and quantitative evidence |
What ‘is’ the system? How can it be described? | What are the main influences on the health problem? How are they created and maintained? How do these influences interconnect? Where might one intervene in the system? | Quantitative: previous systematic reviews of the causes of the problem); epidemiological studies (eg, cohort studies examining risk factors of obesity); network analysis studies showing the nature of social and other systems Qualitative data: theoretical papers; policy documents |
Interactions of interventions with context and adaptation | Qualitative: (1) eg, qualitative studies; case studies Quantitative: (2) trials or other effectiveness studies from different contexts; multicentre trials, with stratified reporting of findings; other quantitative studies that provide evidence of moderating effects of context | |
System adaptivity (how does the system change?) | (How) does the system change when the intervention is introduced? Which aspects of the system are affected? Does this potentiate or dampen its effects? | Quantitative: longitudinal data; possibly historical data; effectiveness studies providing evidence of differential effects across different contexts; system modelling (eg, agent-based modelling) Qualitative: qualitative studies; case studies |
Emergent properties | What are the effects (anticipated and unanticipated) which follow from this system change? | Quantitative: prospective quantitative evaluations; retrospective studies (eg, case–control studies, surveys) may also help identify less common effects; dose–response evaluations of impacts at aggregate level in individual studies or across studies included with systematic reviews (see suggested examples) Qualitative: qualitative studies |
Positive (reinforcing) and negative (balancing) feedback loops | What explains change in the effectiveness of the intervention over time? Are the effects of an intervention are damped/suppressed by other aspects of the system (eg, contextual influences?) | Quantitative: studies of moderators of effectiveness; long-term longitudinal studies Qualitative: studies of factors that enable or inhibit implementation of interventions |
Multiple (health and non-health) outcomes | What changes in processes and outcomes follow the introduction of this system change? At what levels in the system are they experienced? | Quantitative: studies tracking change in the system over time Qualitative: studies exploring effects of the change in individuals, families, communities (including equity considerations and factors that affect engagement and participation in change) |
It may not be apparent which aspects of complexity or which elements of the complex intervention or health system can be explored in a guideline process, or whether combining qualitative and quantitative evidence in a mixed-method synthesis will be useful, until the available evidence is scoped and mapped. 17 20 A more extensive lead in phase is typically required to scope the available evidence, engage with stakeholders and to refine the review parameters and questions that can then be mapped against potential review designs and methods of synthesis. 20 At the scoping stage, it is also common to decide on a theoretical perspective 21 or undertake further work to refine a theoretical perspective. 22 This is also the stage to begin articulating the programme theory of the complex intervention that may be further developed to refine an understanding of complexity and show how the intervention is implemented in and impacts on the wider health system. 17 23 24 In practice, this process can be lengthy, iterative and fluid with multiple revisions to the review scope, often developing and adapting a logic model 17 as the available evidence becomes known and the potential to incorporate different types of review designs and syntheses of quantitative and qualitative evidence becomes better understood. 25 Further questions, propositions or hypotheses may emerge as the reviews progress and therefore the protocols generally need to be developed iteratively over time rather than a priori.
Following a scoping exercise and definition of key questions, the next step in the guideline development process is to identify existing or commission new systematic reviews to locate and summarise the best available evidence in relation to each question. For example, case study 2, ‘Optimising health worker roles for maternal and newborn health through task shifting’, included quantitative reviews that did and did not take an additional complexity perspective, and qualitative evidence syntheses that were able to explain how specific elements of complexity impacted on intervention outcomes within the wider health system. Further understanding of health system complexity was facilitated through the conduct of additional country-level case studies that contributed to an overall understanding of what worked and what happened when lay health worker interventions were implemented. See table 1 online supplementary file 2 .
There are a few existing examples, which we draw on in this paper, but integrating quantitative and qualitative evidence in a mixed-method synthesis is relatively uncommon in a guideline process. Box 2 includes a set of key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in mixed-methods design might ask. Subsequent sections provide more information and signposting to further reading to help address these key questions.
Compound questions requiring both quantitative and qualitative evidence?
Questions requiring mixed-methods studies?
Separate quantitative and qualitative questions?
Separate quantitative and qualitative research studies?
Related quantitative and qualitative research studies?
Mixed-methods studies?
Quantitative unpublished data and/or qualitative unpublished data, eg, narrative survey data?
Throughout the review?
Following separate reviews?
At the question point?
At the synthesis point?
At the evidence to recommendations stage?
Or a combination?
Narrative synthesis or summary?
Quantitising approach, eg, frequency analysis?
Qualitising approach, eg, thematic synthesis?
Tabulation?
Logic model?
Conceptual model/framework?
Graphical approach?
Petticrew et al 17 define the different aspects of complexity and examples of complexity-related questions that can potentially be explored in guidelines and systematic reviews taking a complexity perspective. Relevant aspects of complexity outlined by Petticrew et al 17 are summarised in table 2 below, together with the corresponding questions that could be addressed in a synthesis combining qualitative and quantitative evidence. Importantly, the aspects of complexity and their associated concepts of interest have however yet to be translated fully in primary health research or systematic reviews. There are few known examples where selected complexity concepts have been used to analyse or reanalyse a primary intervention study. Most notable is Chandler et al 26 who specifically set out to identify and translate a set of relevant complexity theory concepts for application in health systems research. Chandler then reanalysed a trial process evaluation using selected complexity theory concepts to better understand the complex causal pathway in the health system that explains some aspects of complexity in table 2 .
Rehfeuss et al 16 also recommends upfront consideration of the WHO-INTEGRATE evidence to decision criteria when planning a guideline and formulating questions. The criteria reflect WHO norms and values and take account of a complexity perspective. The framework can be used by guideline development groups as a menu to decide which criteria to prioritise, and which study types and synthesis methods can be used to collect evidence for each criterion. Many of the criteria and their related questions can be addressed using a synthesis of quantitative and qualitative evidence: the balance of benefits and harms, human rights and sociocultural acceptability, health equity, societal implications and feasibility (see table 3 ). Similar aspects in the DECIDE framework 15 could also be addressed using synthesis of qualitative and quantitative evidence.
Integrate evidence to decision framework criteria, example questions and types of studies to potentially address these questions (derived from Rehfeuss et al 16 )
Domains of the WHO-INTEGRATE EtD framework | Examples of potential research question(s) that a synthesis of qualitative and/or quantitative evidence could address | Types of studies that could contribute to a review of qualitative and quantitative evidence |
Balance of benefits and harms | To what extent do patients/beneficiaries different health outcomes? | Qualitative: studies of views and experiences Quantitative: Questionnaire surveys |
Human rights and sociocultural acceptability | Is the intervention to patients/beneficiaries as well as to those implementing it? To what extent do patients/beneficiaries different non-health outcomes? How does the intervention affect an individual’s, population group’s or organisation’s , that is, their ability to make a competent, informed and voluntary decision? | Qualitative: discourse analysis, qualitative studies (ideally longitudinal to examine changes over time) Quantitative: pro et contra analysis, discrete choice experiments, longitudinal quantitative studies (to examine changes over time), cross-sectional studies Mixed-method studies; case studies |
Health equity, equality and non-discrimination | How is the intervention for individuals, households or communities? How —in terms of physical as well as informational access—is the intervention across different population groups? | Qualitative: studies of views and experiences Quantitative: cross-sectional or longitudinal observational studies, discrete choice experiments, health expenditure studies; health system barrier studies, cross-sectional or longitudinal observational studies, discrete choice experiments, ethical analysis, GIS-based studies |
Societal implications | What is the of the intervention: are there features of the intervention that increase or reduce stigma and that lead to social consequences? Does the intervention enhance or limit social goals, such as education, social cohesion and the attainment of various human rights beyond health? Does it change social norms at individual or population level? What is the of the intervention? Does it contribute to or limit the achievement of goals to protect the environment and efforts to mitigate or adapt to climate change? | Qualitative: studies of views and experiences Quantitative: RCTs, quasi-experimental studies, comparative observational studies, longitudinal implementation studies, case studies, power analyses, environmental impact assessments, modelling studies |
Feasibility and health system considerations | Are there any that impact on implementation of the intervention? How might , such as past decisions and strategic considerations, positively or negatively impact the implementation of the intervention? How does the intervention ? Is it likely to fit well or not, is it likely to impact on it in positive or negative ways? How does the intervention interact with the need for and usage of the existing , at national and subnational levels? How does the intervention interact with the need for and usage of the as well as other relevant infrastructure, at national and subnational levels? | Non-research: policy and regulatory frameworks Qualitative: studies of views and experiences Mixed-method: health systems research, situation analysis, case studies Quantitative: cross-sectional studies |
GIS, Geographical Information System; RCT, randomised controlled trial.
Questions can serve as an ‘anchor’ by articulating the specific aspects of complexity to be explored (eg, Is successful implementation of the intervention context dependent?). 27 Anchor questions such as “How does intervention x impact on socioeconomic inequalities in health behaviour/outcome x” are the kind of health system question that requires a synthesis of both quantitative and qualitative evidence and hence a mixed-method synthesis. Quantitative evidence can quantify the difference in effect, but does not answer the question of how . The ‘how’ question can be partly answered with quantitative and qualitative evidence. For example, quantitative evidence may reveal where socioeconomic status and inequality emerges in the health system (an emergent property) by exploring questions such as “ Does patterning emerge during uptake because fewer people from certain groups come into contact with an intervention in the first place? ” or “ are people from certain backgrounds more likely to drop out, or to maintain effects beyond an intervention differently? ” Qualitative evidence may help understand the reasons behind all of these mechanisms. Alternatively, questions can act as ‘compasses’ where a question sets out a starting point from which to explore further and to potentially ask further questions or develop propositions or hypotheses to explore through a complexity perspective (eg, What factors enhance or hinder implementation?). 27 Other papers in this series provide further guidance on developing questions for qualitative evidence syntheses and guidance on question formulation. 14 28
For anchor and compass questions, additional application of a theory (eg, complexity theory) can help focus evidence synthesis and presentation to explore and explain complexity issues. 17 21 Development of a review specific logic model(s) can help to further refine an initial understanding of any complexity-related issues of interest associated with a specific intervention, and if appropriate the health system or section of the health system within which to contextualise the review question and analyse data. 17 23–25 Specific tools are available to help clarify context and complex interventions. 17 18
If a complexity perspective, and certain criteria within evidence to decision frameworks, is deemed relevant and desirable by guideline developers, it is only possible to pursue a complexity perspective if the evidence is available. Careful scoping using knowledge maps or scoping reviews will help inform development of questions that are answerable with available evidence. 20 If evidence of effect is not available, then a different approach to develop questions leading to a more general narrative understanding of what happened when complex interventions were implemented in a health system will be required (such as in case study 3—risk communication guideline). This should not mean that the original questions developed for which no evidence was found when scoping the literature were not important. An important function of creating a knowledge map is also to identify gaps to inform a future research agenda.
Table 2 and online supplementary files 1–3 outline examples of questions in the three case studies, which were all ‘COMPASS’ questions for the qualitative evidence syntheses.
The shift towards integration of qualitative and quantitative evidence in primary research has, in recent years, begun to be mirrored within research synthesis. 29–31 The natural extension to undertaking quantitative or qualitative reviews has been the development of methods for integrating qualitative and quantitative evidence within reviews, and within the guideline process using evidence to decision-frameworks. Advocating the integration of quantitative and qualitative evidence assumes a complementarity between research methodologies, and a need for both types of evidence to inform policy and practice. Below, we briefly outline the current designs for integrating qualitative and quantitative evidence within a mixed-method review or synthesis.
One of the early approaches to integrating qualitative and quantitative evidence detailed by Sandelowski et al 32 advocated three basic review designs: segregated, integrated and contingent designs, which have been further developed by Heyvaert et al 33 ( box 3 ).
Segregated design.
Conventional separate distinction between quantitative and qualitative approaches based on the assumption they are different entities and should be treated separately; can be distinguished from each other; their findings warrant separate analyses and syntheses. Ultimately, the separate synthesis results can themselves be synthesised.
The methodological differences between qualitative and quantitative studies are minimised as both are viewed as producing findings that can be readily synthesised into one another because they address the same research purposed and questions. Transformation involves either turning qualitative data into quantitative (quantitising) or quantitative findings are turned into qualitative (qualitising) to facilitate their integration.
Takes a cyclical approach to synthesis, with the findings from one synthesis informing the focus of the next synthesis, until all the research objectives have been addressed. Studies are not necessarily grouped and categorised as qualitative or quantitative.
A recent review of more than 400 systematic reviews 34 combining quantitative and qualitative evidence identified two main synthesis designs—convergent and sequential. In a convergent design, qualitative and quantitative evidence is collated and analysed in a parallel or complementary manner, whereas in a sequential synthesis, the collation and analysis of quantitative and qualitative evidence takes place in a sequence with one synthesis informing the other ( box 4 ). 6 These designs can be seen to build on the work of Sandelowski et al , 32 35 particularly in relation to the transformation of data from qualitative to quantitative (and vice versa) and the sequential synthesis design, with a cyclical approach to reviewing that evokes Sandelowski’s contingent design.
Convergent synthesis design.
Qualitative and quantitative research is collected and analysed at the same time in a parallel or complementary manner. Integration can occur at three points:
a. Data-based convergent synthesis design
All included studies are analysed using the same methods and results presented together. As only one synthesis method is used, data transformation occurs (qualitised or quantised). Usually addressed one review question.
b. Results-based convergent synthesis design
Qualitative and quantitative data are analysed and presented separately but integrated using a further synthesis method; eg, narratively, tables, matrices or reanalysing evidence. The results of both syntheses are combined in a third synthesis. Usually addresses an overall review question with subquestions.
c. Parallel-results convergent synthesis design
Qualitative and quantitative data are analysed and presented separately with integration occurring in the interpretation of results in the discussion section. Usually addresses two or more complimentary review questions.
A two-phase approach, data collection and analysis of one type of evidence (eg, qualitative), occurs after and is informed by the collection and analysis of the other type (eg, quantitative). Usually addresses an overall question with subquestions with both syntheses complementing each other.
The three case studies ( table 1 , online supplementary files 1–3 ) illustrate the diverse combination of review designs and synthesis methods that were considered the most appropriate for specific guidelines.
In this section, we draw on examples where specific review designs and methods have been or can be used to explore selected aspects of complexity in guidelines or systematic reviews. We also identify other review methods that could potentially be used to explore aspects of complexity. Of particular note, we could not find any specific examples of systematic methods to synthesise highly diverse research designs as advocated by Petticrew et al 17 and summarised in tables 2 and 3 . For example, we could not find examples of methods to synthesise qualitative studies, case studies, quantitative longitudinal data, possibly historical data, effectiveness studies providing evidence of differential effects across different contexts, and system modelling studies (eg, agent-based modelling) to explore system adaptivity.
There are different ways that quantitative and qualitative evidence can be integrated into a review and then into a guideline development process. In practice, some methods enable integration of different types of evidence in a single synthesis, while in other methods, the single systematic review may include a series of stand-alone reviews or syntheses that are then combined in a cross-study synthesis. Table 1 provides an overview of the characteristics of different review designs and methods and guidance on their applicability for a guideline process. Designs and methods that have already been used in WHO guideline development are described in part A of the table. Part B outlines a design and method that can be used in a guideline process, and part C covers those that have the potential to integrate quantitative, qualitative and mixed-method evidence in a single review design (such as meta-narrative reviews and Bayesian syntheses), but their application in a guideline context has yet to be demonstrated.
Depending on the review design (see boxes 3 and 4 ), integration can potentially take place at a review team and design level, and more commonly at several key points of the review or guideline process. The following sections outline potential points of integration and associated practical considerations when integrating quantitative and qualitative evidence in guideline development.
In a guideline process, it is common for syntheses of quantitative and qualitative evidence to be done separately by different teams and then to integrate the evidence. A practical consideration relates to the organisation, composition and expertise of the review teams and ways of working. If the quantitative and qualitative reviews are being conducted separately and then brought together by the same team members, who are equally comfortable operating within both paradigms, then a consistent approach across both paradigms becomes possible. If, however, a team is being split between the quantitative and qualitative reviews, then the strengths of specialisation can be harnessed, for example, in quality assessment or synthesis. Optimally, at least one, if not more, of the team members should be involved in both quantitative and qualitative reviews to offer the possibility of making connexions throughout the review and not simply at re-agreed junctures. This mirrors O’Cathain’s conclusion that mixed-methods primary research tends to work only when there is a principal investigator who values and is able to oversee integration. 9 10 While the above decisions have been articulated in the context of two types of evidence, variously quantitative and qualitative, they equally apply when considering how to handle studies reporting a mixed-method study design, where data are usually disaggregated into quantitative and qualitative for the purposes of synthesis (see case study 3—risk communication in humanitarian disasters).
Clearly specified key question(s), derived from a scoping or consultation exercise, will make it clear if quantitative and qualitative evidence is required in a guideline development process and which aspects will be addressed by which types of evidence. For the remaining stages of the process, as documented below, a review team faces challenges as to whether to handle each type of evidence separately, regardless of whether sequentially or in parallel, with a view to joining the two products on completion or to attempt integration throughout the review process. In each case, the underlying choice is of efficiencies and potential comparability vs sensitivity to the underlying paradigm.
Once key questions are clearly defined, the guideline development group typically needs to consider whether to conduct a single sensitive search to address all potential subtopics (lumping) or whether to conduct specific searches for each subtopic (splitting). 36 A related consideration is whether to search separately for qualitative, quantitative and mixed-method evidence ‘streams’ or whether to conduct a single search and then identify specific study types at the subsequent sifting stage. These two considerations often mean a trade-off between a single search process involving very large numbers of records or a more protracted search process retrieving smaller numbers of records. Both approaches have advantages and choice may depend on the respective availability of resources for searching and sifting.
Closely related to decisions around searching are considerations relating to screening and selecting studies for inclusion in a systematic review. An important consideration here is whether the review team will screen records for all review types, regardless of their subsequent involvement (‘altruistic sifting’), or specialise in screening for the study type with which they are most familiar. The risk of missing relevant reports might be minimised by whole team screening for empirical reports in the first instance and then coding them for a specific quantitative, qualitative or mixed-methods report at a subsequent stage.
Within a guideline process, review teams may be more limited in their choice of instruments to assess methodological limitations of primary studies as there are mandatory requirements to use the Cochrane risk of bias tool 37 to feed into Grading of Recommendations Assessment, Development and Evaluation (GRADE) 38 or to select from a small pool of qualitative appraisal instruments in order to apply GRADE; Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) 39 to assess the overall certainty or confidence in findings. The Cochrane Qualitative and Implementation Methods Group has recently issued guidance on the selection of appraisal instruments and core assessment criteria. 40 The Mixed-Methods Appraisal Tool, which is currently undergoing further development, offers a single quality assessment instrument for quantitative, qualitative and mixed-methods studies. 41 Other options include using corresponding instruments from within the same ‘stable’, for example, using different Critical Appraisal Skills Programme instruments. 42 While using instruments developed by the same team or organisation may achieve a degree of epistemological consonance, benefits may come more from consistency of approach and reporting rather than from a shared view of quality. Alternatively, a more paradigm-sensitive approach would involve selecting the best instrument for each respective review while deferring challenges from later heterogeneity of reporting.
The way in which data and evidence are extracted from primary research studies for review will be influenced by the type of integrated synthesis being undertaken and the review purpose. Initially, decisions need to be made regarding the nature and type of data and evidence that are to be extracted from the included studies. Method-specific reporting guidelines 43 44 provide a good template as to what quantitative and qualitative data it is potentially possible to extract from different types of method-specific study reports, although in practice reporting quality varies. Online supplementary file 5 provides a hypothetical example of the different types of studies from which quantitative and qualitative evidence could potentially be extracted for synthesis.
The decisions around what data or evidence to extract will be guided by how ‘integrated’ the mixed-method review will be. For those reviews where the quantitative and qualitative findings of studies are synthesised separately and integrated at the point of findings (eg, segregated or contingent approaches or sequential synthesis design), separate data extraction approaches will likely be used.
Where integration occurs during the process of the review (eg, integrated approach or convergent synthesis design), an integrated approach to data extraction may be considered, depending on the purpose of the review. This may involve the use of a data extraction framework, the choice of which needs to be congruent with the approach to synthesis chosen for the review. 40 45 The integrative or theoretical framework may be decided on a priori if a pre-developed theoretical or conceptual framework is available in the literature. 27 The development of a framework may alternatively arise from the reading of the included studies, in relation to the purpose of the review, early in the process. The Cochrane Qualitative and Implementation Methods Group provide further guidance on extraction of qualitative data, including use of software. 40
Relatively few synthesis methods start off being integrated from the beginning, and these methods have generally been subject to less testing and evaluation particularly in a guideline context (see table 1 ). A review design that started off being integrated from the beginning may be suitable for some guideline contexts (such as in case study 3—risk communication in humanitarian disasters—where there was little evidence of effect), but in general if there are sufficient trials then a separate systematic review and meta-analysis will be required for a guideline. Other papers in this series offer guidance on methods for synthesising quantitative 46 and qualitative evidence 14 in reviews that take a complexity perspective. Further guidance on integrating quantitative and qualitative evidence in a systematic review is provided by the Cochrane Qualitative and Implementation Methods Group. 19 27 29 40 47
It is highly likely (unless there are well-designed process evaluations) that the primary studies may not themselves seek to address the complexity-related questions required for a guideline process. In which case, review authors will need to configure the available evidence and transform the evidence through the synthesis process to produce explanations, propositions and hypotheses (ie, findings) that were not obvious at primary study level. It is important that guideline commissioners, developers and review authors are aware that specific methods are intended to produce a type of finding with a specific purpose (such as developing new theory in the case of meta-ethnography). 48 Case study 1 (antenatal care guideline) provides an example of how a meta-ethnography was used to develop a new theory as an end product, 48 49 as well as framework synthesis which produced descriptive and explanatory findings that were more easily incorporated into the guideline process. 27 The definitions ( box 5 ) may be helpful when defining the different types of findings.
Descriptive findings —qualitative evidence-driven translated descriptive themes that do not move beyond the primary studies.
Explanatory findings —may either be at a descriptive or theoretical level. At the descriptive level, qualitative evidence is used to explain phenomena observed in quantitative results, such as why implementation failed in specific circumstances. At the theoretical level, the transformed and interpreted findings that go beyond the primary studies can be used to explain the descriptive findings. The latter description is generally the accepted definition in the wider qualitative community.
Hypothetical or theoretical finding —qualitative evidence-driven transformed themes (or lines of argument) that go beyond the primary studies. Although similar, Thomas and Harden 56 make a distinction in the purposes between two types of theoretical findings: analytical themes and the product of meta-ethnographies, third-order interpretations. 48
Analytical themes are a product of interrogating descriptive themes by placing the synthesis within an external theoretical framework (such as the review question and subquestions) and are considered more appropriate when a specific review question is being addressed (eg, in a guideline or to inform policy). 56
Third-order interpretations come from translating studies into one another while preserving the original context and are more appropriate when a body of literature is being explored in and of itself with broader or emergent review questions. 48
A critical element of guideline development is the formulation of recommendations by the Guideline Development Group, and EtD frameworks help to facilitate this process. 16 The EtD framework can also be used as a mechanism to integrate and display quantitative and qualitative evidence and findings mapped against the EtD framework domains with hyperlinks to more detailed evidence summaries from contributing reviews (see table 1 ). It is commonly the EtD framework that enables the findings of the separate quantitative and qualitative reviews to be brought together in a guideline process. Specific challenges when populating the DECIDE evidence to decision framework 15 were noted in case study 3 (risk communication in humanitarian disasters) as there was an absence of intervention effect data and the interventions to communicate public health risks were context specific and varied. These problems would not, however, have been addressed by substitution of the DECIDE framework with the new INTEGRATE 16 evidence to decision framework. A d ifferent type of EtD framework needs to be developed for reviews that do not include sufficient evidence of intervention effect.
Mixed-method review and synthesis methods are generally the least developed of all systematic review methods. It is acknowledged that methods for combining quantitative and qualitative evidence are generally poorly articulated. 29 50 There are however some fairly well-established methods for using qualitative evidence to explore aspects of complexity (such as contextual, implementation and outcome complexity), which can be combined with evidence of effect (see sections A and B of table 1 ). 14 There are good examples of systematic reviews that use these methods to combine quantitative and qualitative evidence, and examples of guideline recommendations that were informed by evidence from both quantitative and qualitative reviews (eg, case studies 1–3). With the exception of case study 3 (risk communication), the quantitative and qualitative reviews for these specific guidelines have been conducted separately, and the findings subsequently brought together in an EtD framework to inform recommendations.
Other mixed-method review designs have potential to contribute to understanding of complex interventions and to explore aspects of wider health systems complexity but have not been sufficiently developed and tested for this specific purpose, or used in a guideline process (section C of table 1 ). Some methods such as meta-narrative reviews also explore different questions to those usually asked in a guideline process. Methods for processing (eg, quality appraisal) and synthesising the highly diverse evidence suggested in tables 2 and 3 that are required to explore specific aspects of health systems complexity (such as system adaptivity) and to populate some sections of the INTEGRATE EtD framework remain underdeveloped or in need of development.
In addition to the required methodological development mentioned above, there is no GRADE approach 38 for assessing confidence in findings developed from combined quantitative and qualitative evidence. Another paper in this series outlines how to deal with complexity and grading different types of quantitative evidence, 51 and the GRADE CERQual approach for qualitative findings is described elsewhere, 39 but both these approaches are applied to method-specific and not mixed-method findings. An unofficial adaptation of GRADE was used in the risk communication guideline that reported mixed-method findings. Nor is there a reporting guideline for mixed-method reviews, 47 and for now reports will need to conform to the relevant reporting requirements of the respective method-specific guideline. There is a need to further adapt and test DECIDE, 15 WHO-INTEGRATE 16 and other types of evidence to decision frameworks to accommodate evidence from mixed-method syntheses which do not set out to determine the statistical effects of interventions and in circumstances where there are no trials.
When conducting quantitative and qualitative reviews that will subsequently be combined, there are specific considerations for managing and integrating the different types of evidence throughout the review process. We have summarised different options for combining qualitative and quantitative evidence in mixed-method syntheses that guideline developers and systematic reviewers can choose from, as well as outlining the opportunities to integrate evidence at different stages of the review and guideline development process.
Review commissioners, authors and guideline developers generally have less experience of combining qualitative and evidence in mixed-methods reviews. In particular, there is a relatively small group of reviewers who are skilled at undertaking fully integrated mixed-method reviews. Commissioning additional qualitative and mixed-method reviews creates an additional cost. Large complex mixed-method reviews generally take more time to complete. Careful consideration needs to be given as to which guidelines would benefit most from additional qualitative and mixed-method syntheses. More training is required to develop capacity and there is a need to develop processes for preparing the guideline panel to consider and use mixed-method evidence in their decision-making.
This paper has presented how qualitative and quantitative evidence, combined in mixed-method reviews, can help understand aspects of complex interventions and the systems within which they are implemented. There are further opportunities to use these methods, and to further develop the methods, to look more widely at additional aspects of complexity. There is a range of review designs and synthesis methods to choose from depending on the question being asked or the questions that may emerge during the conduct of the synthesis. Additional methods need to be developed (or existing methods further adapted) in order to synthesise the full range of diverse evidence that is desirable to explore the complexity-related questions when complex interventions are implemented into health systems. We encourage review commissioners and authors, and guideline developers to consider using mixed-methods reviews and synthesis in guidelines and to report on their usefulness in the guideline development process.
Handling editor: Soumyadeep Bhaumik
Contributors: JN, AB, GM, KF, ÖT and ES drafted the manuscript. All authors contributed to paper development and writing and agreed the final manuscript. Anayda Portela and Susan Norris from WHO managed the series. Helen Smith was series Editor. We thank all those who provided feedback on various iterations.
Funding: Funding provided by the World Health Organization Department of Maternal, Newborn, Child and Adolescent Health through grants received from the United States Agency for International Development and the Norwegian Agency for Development Cooperation.
Disclaimer: ÖT is a staff member of WHO. The author alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of WHO.
Competing interests: No financial interests declared. JN, AB and ÖT have an intellectual interest in GRADE CERQual; and JN has an intellectual interest in the iCAT_SR tool.
Patient consent: Not required.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data sharing statement: No additional data are available.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Home » Variables in Research – Definition, Types and Examples
Table of Contents
Definition:
In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest.
Types of Variables in Research are as follows:
This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.
This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.
This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.
This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.
This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.
This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.
This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.
This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.
This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.
This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.
This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.
This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.
This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.
There are different methods to analyze variables in research, including:
Variables are used in many different applications across various fields. Here are some examples:
Variables serve several purposes in research, including:
Characteristics of Variables are as follows:
Here are some of the advantages of using variables in research:
Here are some of the main disadvantages of using variables in research:
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Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.
Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.
After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.
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If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.
Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:
Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.
Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.
Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.
Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.
Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.
Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.
Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:
Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.
There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.
Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.
Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.
Now, let’s consider some descriptive research examples.
These examples showcase the versatility of descriptive research across diverse fields.
There are several advantages to this approach, which every researcher must be aware of. These are as follows:
On the other hand, this design has some drawbacks as well:
To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.
When should researchers conduct descriptive research.
Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.
Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.
Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.
No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.
The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.
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BMC Medical Education volume 24 , Article number: 928 ( 2024 ) Cite this article
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Educational settings in professional health education establishments significantly shape students' academic, social, and emotional experiences. These environments encompass physical, psychological, and social infrastructures of programs or institutions, which jointly influence learning and development. This study analyzed the educational environment at Prince Nora University in Saudi Arabia, a renowned institution in health education.
The primary aim of this study was to evaluate the perceptions of the educational environment among students at Prince Nora University using the Dundee Ready Education Environment Measure (DREEM) inventory. The DREEM inventory is a renowned and validated tool designed to gauge students' perceptions across various dimensions of their educational experience.
Employing a cross-sectional survey design, the research gathered data from a sample of 321 students enrolled in the College of Health and Rehabilitation Sciences at Prince Nord University. The DREEM inventory, which measures the academic, social, and emotional aspects of the learning environment from the student's perspective, was utilized to collect the information.
The findings from the study indicated a positive perception of the educational environment among the students, with an overall mean score of 113.84 out of 200 on the DREEM inventory. Analysis of the subscales revealed that the Student Perceptions of Atmosphere (SPoA) received the highest scores, indicating a favourable environment, while Student Social Self-Perceptions (SSSP) scored the lowest, suggesting areas that may require attention and improvement.
The study successfully showed the utility of the DREEM inventory in assessing the educational environment at Prince Nora University, highlighting its effectiveness as a tool for understanding student perceptions. The positive overall score suggests a conducive learning atmosphere, though the disparity in subscale scores points to potential areas for enhancement. Recommendation: The research suggests that Saudi Arabian universities should implement the DREEM inventory to assess and enhance their educational settings, ultimately delivering a comprehensive and nurturing learning experience for students .
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Professional health education classrooms are a hotbed of academic curiosity worldwide [ 1 ]. The programs' or institutions physical, social, psychological, and other infrastructures make up what they collectively refer to as the educational environment [ 2 ]. It also includes the mindset and actions of the teachers and strategies they employ to convey the course content to the students. It also includes the style of the curriculum employed and the instructional methods employed. The educational environment comprises everything that impacts instruction and study [ 3 ].
Many criteria recognize the traits and characteristics of a learning environment; program rules, governance structures, and other features may also be called educational environment elements [ 4 ]. It is agreed that the school climate significantly affects student achievement [ 5 ]. Thus, it has become clear that reviewing school settings is vital to ensuring that all students receive well-rounded education [ 6 ]. The National Commission for Accreditation and Assessment (NCAAA) in Saudi Arabia prioritizes the quality of the learning environment when conducting program evaluations. Thus, the NCAAA ensures the quality and standardization of higher education institutions and programs [ 7 ]. The establishment of the National Commission for Academic Accreditation and Assessment (NCAAA) in Saudi Arabia is aimed at setting standards, accrediting institutions, and improving the quality of higher education programs [ 8 ]. It strives to guarantee the effectiveness and comprehensiveness of educational and training programs while also assessing their impact on the national economy and development [ 8 ]. The NCAAA strives to establish trust in local and global communities regarding the outcomes of these institutions and programs [ 8 ]. While the NCAAA has only recently incorporated postgraduate programs into its plans, it now allows universities to apply for accreditation of these programs [ 9 ]. The NCAAA's efforts to accredit undergraduate programs have shown noteworthy progress, but its role in accrediting postgraduate programs is still in progress and may not yet provide the same level of assurance as it does for undergraduate programs.
Recent changes to health professional education (HPE) curricula, such as incorporating new teaching and learning practices and evaluation methods and the growing diversity of today's student body, have increased the urgency of reviewing the current state of health-related education [ 10 ]. There is a strong correlation between how students feel about their classrooms and their academic performance. Student data should inform decisions regarding the curriculum, instructional methods, and school infrastructure [ 11 ]. Educators believe that students' exposure to both classroom and clinical settings significantly impacts their development of attitudes, knowledge, abilities, and behaviors as they move through the medical school curriculum [ 12 ].
The quality of educational settings has been evaluated using several tools developed and deployed in recent years [ 13 , 14 , 15 , 16 , 17 , 18 ]. In 1997, a team of medical educators from Dundee University created a reliable instrument for assessing the setting and culture of HPE. Its official title is the Dundee Ready Environment Educational Measure (DREEM). It is widely accepted as a culturally neutral, credible, and accurate assessment tool for gauging the climate of undergraduate HPE programs. The quantitative and qualitative methodologies used to design this instrument are the basis for its widespread acceptance [ 1 ]. The current sample size employed in the assessment of students' perceptions of education using the DREEM inventory at the College of Education, Princess Nora University, Saudi Arabia, was deemed adequate and representative, ensuring the generalizability of the findings to the broader student population. This study's robust sampling methodology and consideration of diverse academic levels contribute to its representativeness and reliability.
Despite the potential value of applying DREEM to the analysis of HPE problems, this tool is not commonly used in health-related programs in Saudi Arabia. DREEM's application of DREEM to the investigation of HPE problems could be very beneficial, but it is not widely used in SA health-related activities. Thus, this study investigated how students in the health professions at Princess Noura University (PNU), an all-female institution in SA, felt about their classroom environment.
This cross-sectional study began on September 28, 2022, for 2 months at the PNU, SA. Faculty of Health and Rehabilitation Sciences. When it opened in 2008, they knew it as the "College of Physiotherapy." It offers only one major: physiotherapy. After then, in 2012, a reorganization led to the college's name changing from "College of Physiotherapy" to "College of Health and Rehabilitation Sciences." Department of Health Sciences, Department of Communication Sciences, Department of Rehabilitation Sciences, Department of Radiology. Thirteen different programs are available and spread across four different departments. These are only available for women.
Following Cochran [ 19 ], the optimal sample size was calculated using the following formula ( 1 ):
where n is the number of observations, is the fraction of expected levels (in this case, the DREEM response), Z is the standard deviation for a 95% confidence interval, and d is the intended margin of error. Given that the author could not know how numerous students would respond in each category on the DREEM, we had to use a cutoff of 50% of the possible total scores. π = 0.5, d = 0.05, and z = 1.96. With a 10% non-response rate and finite sample correction factor in mind, the minimum number of participants was 400. It then split the computed sample size across schools based on the relative student population.
In September 2021, the College of Education at Princess Nora University in Saudi Arabia was considered to be one of the world's largest educational institutions for women. This university is located in Riyadh, the bustling Saudi capital, and houses thousands of female students. It provides ample opportunities for practical learning experiences in real-world classroom settings in a rich academic and cultural environment. Through research and innovation, the institution aimed to prepare its students to make positive contributions to Saudi Arabia's educational system. Verifying the latest statistics and developments is advisable before relying on the provided information. The participants' responses were gathered using the standard, industry-wide-applied questionnaire, the DREEM. The DREEM inventory consists of 50 items divided into the following five scales:
Twelve components (1, 7, 13, 16, 20, 22, 24, 25, 38, 44, 47, and 48) with a maximum score of 48 make up students' perceptions of learning (SPoL), with scores being interpreted as follows: The results ranged from 0 to 48, with 0 being the worst, 13 to 24 being poor, 25 to 36 being negative, and 37 to 48 being very thought-out in the classroom.
There are 11 items on the Students' Perceptions of Teachers (SPoT) survey (No. 2, 6, 8, 9, 18, 29, 32, 37, 39, 40, and 50) and a maximum score of 44, with the results interpreted as follows: From lowest to highest, the lowest was teachers with a score of 0–11, the lowest was those with a score of 12–22, the middle was those with a score of 23–33, and the highest was those with a score of 34–44, who were deemed excellent educators.
There is a maximum score of 32 on students' academic self-perceptions (SASP), which consists of 8 items (Item No. Items: 2, 10, 22, 26, 27, 31, 41, and 45). Scores of 0–8 were regarded as a sense of absolute failure, 9–16 as a sense of many negative characteristics, 17–24 as a sense of learning more toward the positive, and 25–32 as a sense of confidence.
12 elements (items No. Items 11, 12, 17, 23, 30, 33, 34, 35, 36, 42, 43, and 50) make up the SPoA scale, which can be scored from 0 to 48. From 0 to 12, 13 to 24, 25 to 36, 37 to 48, and 48 and above, the environment was rated as poor, with many problems that needed to be fixed, a more positive atmosphere, and a nice feeling overall.
The Student Social Self-Perceptions (SSSP) contains 7 questions with a maximum score of 28 (questions 3, 4, 14, 15, 19, 28, and 46) and can be interpreted as follows : from 0 to 7, it was awful; from 8 to 14, it was not lovely; from 15 to 21, it was not too bad; and from 22 to 28, it was socially perfect.
After reading each item, students were to rate it on a five-point Likert scale from "strongly agree" to "strongly disagree." The items were rated as follows: a score of 4 indicated complete agreement, 3 indicated moderate agreement, 2 indicated uncertainty, 1 indicated disagreement, and 0 indicated significant disagreement. Altogether, the scale adds up to a total score of 200. In this study, the authors assessed 9 items initially rated negatively (4, 8, 9, 17, 25, 35, 39, 48, and 50). A perfect educational environment received a score of 200 on the original DREEM. A higher number reflects a more favorable rating. Authors have rated each item on a five-point Likert scale from "strongly agree" to "strongly disagree" and interpreted a score of 4 as a total score in the following format: A score between 0 and 50 suggests an impoverished educational environment, a score between 51 and 100 shows many problems, a score between 101 and 150 indicates more positive than negative, and a score between 151–200 indicates an excellent educational environment [ 1 ]. In this study, it was determined that areas with individual items with a mean score of 3.5 or higher are vital, areas with individual items with a mean score of 2.0 or lower require attention, and areas with individual items with mean scores between 2 and 3 are areas of the educational environment that have room for improvement [ 20 ]. Google Survey was used to conduct electronic surveys with students through a web-based program. The students received a link to the questionnaire via an airdrop.
We used the Statistical Package for the Social Sciences (SPSS) for data entry, verification, and analysis (SPSS) (version 25; SPSS Inc. Chicago, IL, USA). Descriptive statistics and inferential methods were used to analyze the data. Comparisons of group means were made using frequency analyses and basic tabulation. It also compares the meaning of the ‘subgroup. The scores were compared on a college-specific basis by using an independent Student’s t-test. To determine whether there was a statistically significant difference between the various cohorts of students defined by academic year and academic program, Kruskal–Wallis analysis was performed. Statistically, a significant result was defined as one with a probability level of less than 0.05.
In this study, approximately 321 students successfully completed the online survey, or 80.3% of the estimated sample size. The average age of these respondents was 22.2 ± 2.001. The majority were located in the Health Sciences Division ( n = 133/321) and Radiology Sciences Division ( n = 108/321) (41.4% and 33.6%, respectively). Table 1 shows the distribution of students by year in school. Participation rates were relatively consistent across all years except for the first, in which only 3.4% ( n = 11/321) of the children were present.
The overall DREEM score for this investigation was 113.84 35.187, which suggests that the educational environment is more favorable than unfavorable. Similarly, the SPoL, SPoT, SASP, SPoA, and SSSP are listed in Table 2 . This explains the items in each category in Table 3 .
Kruskal–Wallis was run with background characteristics as independent variables and DREAM domains as dependent variables to determine whether there was a correlation between the two data sets. Table 4 indicate no statistically significant differences between the five DREAM domains and the demographic information of the students who took the survey.
The quality of an educational institution is crucial for achieving its HPE program goals [ 21 ]. Therefore, this research aimed to assess the school climate perceived by PNU and SA women majoring in health sciences. The survey also sought to identify opinion discrepancies between departments and students of varying ages. The researchers employed the DREEM inventory, which is widely considered the best tool for measuring the educational environment of undergraduate HPE institutions [ 22 ].
According to Gruppen et al. [ 23 ], the quality of an educational institution is intimately tied to the success of any HPE programme. Consequently, this study aimed to investigate the perspectives of female students majoring in health Sciences at PNU in South Africa regarding the university environment [ 24 ]. This study aimed to determine several factors, including whether there were substantial disparities in opinions between various departments or among students of varying ages. The researchers used the DREEM inventory, which is generally regarded as the best approach for measuring the educational environment of undergraduate HPE institutions [ 22 ].
The mean DREEM score in this analysis was 113.84 ± 35.187, suggesting that participants were more likely to have a favorable impression of their school's environment than a negative one [ 10 ]. The results of several other Saudi academic institutions corroborate our findings. Global DREEM test results from King Khalid University [ 25 ], Qassim University [ 26 ], King Fahad Medical City [ 27 ], Tabuk University [ 28 ], Jazan University [ 29 ], and King Abdul Aziz University [ 30 ]were (102, 112.9, 112, 111.5, 105, 104.9, 102) respectively.
Comparing these scores, it can be observed that some institutions, including those in the present study (113.84), had DREEM scores higher than 100, indicating a generally positive perception of the educational environment. King Khalid University, King Fahad Medical City, Tabuk University, and Jazan University also had scores above 100, reflecting favorable perceptions among their students. On the other hand, Qassim University and King Abdul Aziz University had scores below 100, suggesting some areas for improvement in their educational environments as perceived by their students.
It is important to note that the DREEM inventory is a valuable tool for assessing various facets of the educational environment, and its application in multiple institutions helps to identify patterns and trends in students' perceptions. These scores can guide administrators and policymakers to understand the strengths and weaknesses of their educational settings and enable them to make informed decisions to enhance their overall learning experience. By benchmarking their scores against other institutions, universities can gain valuable insights and potentially implement best practices from those with higher DREEM scores to improve their educational landscape.
This was almost consistent with the results of a study conducted at a different university in the U.K.. (139) [ 31 ], Sudan (130) [ 32 ], Nepal (130) [ 20 ], Malaysia (125.3) [ 33 ], Nigeria (118) [ 20 ], Turkey (117.6) [ 34 ], India (117) [ 34 ], and Sri Lanka (108) [ 35 ]. The DREEM inventory subscale analysis is a valuable application. This reveals the benefits and drawbacks of the current school system. With scores of 27.2 on the SPoL scale, 23.1 on the SPoT scale, 18.3 on the SASP scale, 27.3 on the SPoA scale, and 15.7 on the SSSP scale, it is clear found that there were more positives than negatives on the educational environment. These results are consistent with those found in research performed at other SA universities such as Jazan University [ 29 ], Qassim University [ 26 ], and King Khalid University [ 25 ].
Among the DREEM scores, those from various universities in different countries, including the current study, had a DREEM score of 139. Moreover, the study provides DREEM scores from other countries ranging from 130 to 108, as well as the value of the subscale analysis of the DREEM inventory. In the U.K. study, students scored 139, indicating a very positive perception of the educational environment. In addition to Sudan and Nepal, both scored 130, indicating favorable impressions of their respective educational settings. There were also positive perceptions among students in Malaysia, Nigeria, Turkey, India, and Sri Lanka, with scores ranging from 108 to 125.3.
The DREEM score for the current study is "almost at the same level" as that in the U.K. study, even though this is not explicitly stated. The exact DREEM score for this study is not provided, but we can assume that it reflects a positive perception of the educational environment, similar to that in the U.K. Further discussion on the DREEM inventory subscale analysis is provided in this study.
The DREEM score is broken down into five specific subscales: SPoL (Perceptions of Learning), SpoT (Perceptions of Teaching), SASP (Academic Self-Perceptions), SpoA (Perceptions of Atmosphere), and SSSP (socials Selp-Perceptions). Based on the subscale scores discussed in this study, most participants positively perceived their educational environment. According to the SPoL, SpoT, SASP, SpoA, and SSSP scores, educational environment was more positive than negative (27.2, 23.1, 18.3, and 15.7, respectively). Clearly, students were optimistic about their learning experiences, teaching quality, academic self-confidence, and atmosphere within the institution. A brief comparison of the study's results with those of other Saudi Arabian universities, including Jazan University, Qassim University, and King Khalid University, is provided. This study does not provide specific DREEM scores for these universities, but suggests that the findings are consistent with those from other Saudi Arabian institutions. Based on this consistency, students generally perceive these universities as positive for their educational environment.
Focusing on the current study's comparison provides valuable insights into the DREEM scores from various universities worldwide. Students in the current study viewed their educational environment positively, highlighting the value of DREEM's subscale analysis in understanding specific aspects of the educational environment. More detailed information is required for comprehensive conclusions and understanding of the full implications of these findings, including the exact DREEM score from the current study.
Only 53% ( n = 170), with an average mean of 27.2 ± 9.444, showed a positive perception of learning, and 50% of them ( n = 160), with a mean of 23.01 ± 7.904, described that teachers were moving in the right direction, as shown in Tables 2 and 3 . This is mainly due to the continuous professional development program implemented by the college and university, which aimed to enhance the faculty's capacities in teaching and learning to include preparation and delivery of the teaching materials, development of a blueprint, and student assessment. The college also has a stringent recruitment process for selecting only the most qualified candidates with excellent teaching backgrounds and high GPAs. Peer assessment was used to evaluate the colleges’ teaching and learning methods to ensure that they performed as expected. The strengths and areas for improvement highlighted in the peer evaluation report were used to inform the continuing professional development goals for the following year. Annual faculty evaluation is also a tool to improve a college's educational atmosphere and pedagogy.
In this study, the authors examined how college and university students perceive their learning and teachers. It was found that 53% of the participants reported that education was positive, while 50% indicated that teachers were making progress. The average means of these scores were 27.2 ± 9.444 for SPoL and 23.01 ± 7.904 for SPoT. Indicators of students' experiences with the learning process and their perceptions of teachers' effectiveness were the DREEM scores for SPoL and SPoT. A SPoL score of 27.2 suggests that slightly more than half of the participants were satisfied with their learning experiences. However, a score of 23.01 for SPoT indicates that about half of the students are satisfied with the teaching methods and approaches, which suggests that they feel their teachers are moving in the right direction.
Positive perceptions of learning and teachers can be attributed to the continuous professional development programs in universities and colleges. The faculty's capacity for teaching and learning is enhanced by continuing professional development. To create a more dynamic and effective learning environment for students, institutions should provide faculty members with opportunities to improve their teaching skills and stay current with the latest teaching methodologies.
Through its stringent recruitment process, the college selects only qualified candidates with excellent teaching backgrounds and high GPAs, resulting in a higher quality faculty and a better educational experience for students. Selecting competent teachers is a critical component of ensuring high-quality instruction for students.
A positive aspect of the college approach is the use of peer assessment to evaluate teaching and learning methods. Experienced colleagues provide unbiased feedback in peer evaluations, highlighting strengths and improvement areas. Using the findings from the peer evaluation report, faculty members can set goals for professional development, ensuring that they address areas that need improvement. Annual faculty evaluation is a valuable tool for assessing and improving a college's educational environment and pedagogy. Using faculty evaluations can provide insights into how well instructors interact with their students, create a supportive learning environment, and adapt teaching methods to meet students' needs. As a result, the college can identify areas for improvement and make data-driven decisions that will enhance the educational experience of all students.
Due to the lack of explicit comparisons with results from other colleges, we cannot directly assess how the college's DREEM scores for SPoL and SPoT compare with those of other colleges. However, continuous professional development programs, peer evaluations, and annual faculty evaluations indicate that the college is taking proactive measures to ensure a positive educational environment for students. Such practices reflect this commitment to academic excellence and student success. The study concludes by emphasizing the importance of SPoLs and teachers. The college and university's continuous professional development programs, stringent recruitment processes, peer evaluations, and annual faculty evaluations were responsible for the positive perceptions of SPoL and SPoT. Emphasis on improving the quality of education and creating a conducive learning environment is evident in these practices. The study does not directly compare college's results with those of other institutions, but the practices mentioned suggest a proactive approach to fostering a positive educational environment.
Approximately 54% of participants ( n = 173) felt positive with the mean result of 18.29 ± 6.560 as shown in Table a mean result of 18.29 ± 6.560, as shown in Tables 2 and 3 . The mean result of 18.29 ± 6.560, as shown in Tables 2 and 3 closely relates to the ability of the 'students' to cope with the academic workload [ 26 ]. A well-designed and developed course timetable with more self-directed learning sessions allocated is a leading cause of this positive perception, as seen in the Australian DREEM study [ 36 ]. Many extracurricular activities aligned with program learning outcomes implemented within the course schedule gave the students free time to learn some non-technical skills in pressure-free time, supporting positive perception. The study examined students' perceptions of their ability to cope with the academic workload, with approximately 54% of participants reporting feeling positive. The mean result for this aspect was 18.29 ± 6.560.A significant finding was students' positive perception of their ability to cope with academic workloads, with a mean score of 18.29, above the midpoint of the DREEM scale, indicating that many participants felt confident in managing their academic responsibilities. This positive perception can positively impact student well-being and academic performance.When students feel capable of handling their workload, they are likely to experience less stress and anxiety, which can lead to improved learning outcomes.
According to this study, students' perceptions of their ability to cope with academic workloads are influenced by several factors. Course timetables are essential to students' perceptions of their academic workload. Having self-directed learning sessions in the timetable allows students to manage their time effectively and to control their learning pace. Students are empowered to take control of their learning journey using this approach, which is aligned with active learning and student-centered education principles. A positive perception of coping with academic workload is also supported by the implementation of extracurricular activities that align with program-learning outcomes. A well-rounded educational experience can be achieved by participating in extracurricular activities beyond the core academic curriculum. Students benefit from these activities regarding personal growth, skill development, and social interaction, all of which can reduce stress and improve their overall wellbeing.
Consequently, based on the study's lack of explicit comparisons, we cannot directly compare the current colleges’ scores for dealing with academic workload with those of other colleges or institutions. While a mean score of 18.29 is above the midpoint of the DREEM scale, it indicates a positive perception among participants. Students can manage their academic demands effectively, which is an encouraging sign of their commitment to creating a conducive learning environment. As a result, students' perceptions of their ability to cope with academic workloads were positive. This positive perception is partly attributed to well-designed course schedules with self-directed learning sessions and the implementation of extracurricular activities aligned with the learning outcomes. Students' well-being and academic performance can be enhanced if they positively perceive academic workload management. Further research and comparison could provide a better understanding of students' overall academic experiences compared with other colleges.
By “ learning resources”, authors mean things like the physical layout of classrooms and clinics and the attitude and demeanor of instructors during class and patient care. It comprises academic regulations and planning of the academic curriculum. Tables 2 and 3 show that 54% of the students ( n = 174) felt that the environment had improved. The mean score in this group was 27.339.342. The results are encouraging based on the study's findings at Taibah University's College of Medicine [ 37 ]. The positivity of student perception is based on well-designed timetables, a motivating environment, a wide range of extracurricular activities offered to students to enhance and encourage their interpersonal skills, and academic advisory services, such as academic, psychological, behavioral, and career counseling.
The study discusses the results of SPoL resources, which encompass various aspects, such as the physical layout of classrooms and clinics, instructor attitudes and demeanor during class and patient care, academic regulations, and curriculum planning. Based on a study conducted at Taibah University College of Medicine, 54% of students ( n = 174) felt that the learning environment had improved, with a mean score of 27.339.342. These results were encouraging. It is noteworthy that 54% of the students positively perceived improved learning resources. A mean score of 27.339.342 indicated that most students perceived positive changes. Positive perceptions suggest that students are satisfied with various aspects of their academic experience such as physical facilities, instructor attitudes, and academic structure.
A well-designed timetable plays an essential role in shaping students' academic experiences. This study identified several factors that influenced students' positive perceptions of learning resources. Optimizing class sessions, clinical rotations, and study time can help students to achieve a balanced and effective learning schedule. Students can better manage their academic workload when their timetables are organized, allowing for a smoother flow of learning activities.
Supportive and encouraging environments can inspire students to strive for academic excellence and to actively participate in their educational journey. Motivating environments foster enthusiasm and engagement among students, which can positively affect learning outcomes. It is beneficial for students' interpersonal skills to participate in various extracurricular activities.Essential life skills, such as teamwork, leadership, and communication, can be developed through such activities beyond the traditional academic setting. Student support can be provided in various ways, including academic, psychological, behavioral, and career counseling. AsAsThrough such advisory services, students can navigate academic challenges and make informed career decisions.
The results of a study conducted by Taibah University College of Medicine are described as encouraging. However, the Taibah University study did not provide specific details regarding the DREEM scores or the learning resources evaluated. As a result, direct comparison is difficult. Ultimately, students' positive perceptions of improved learning resources were encouraging.. Students expressed satisfaction with various aspects of their learning environments. In addition to well-designed timetables, a motivating environment, extracurricular activities, and academic advisory services, the college strives to enhance students' overall learning experiences. Despite the comparison with the Taibah University study being mentioned as encouraging, more comprehensive details and further research are needed to draw meaningful conclusions and understand the SPoL resources across colleges and institutions in a broader context.
In the current study, 50% of students with a mean of 15.66 ± 5.813 perceived social life as more positive, as shown in Tables 2 and 3 , as such various institutes in SA as Jazan University [ 29 ] Qassim University [ 26 ], and King Khalid University [ 25 ]. This is similar to studies conducted in Sudan 17/28 [ 38 ], Pakistan 15.4/28 [ 39 ], and Malaysia 16.7/28 [ 33 ]. It partially attributed the finding of a good social life in this study to the extracurricular activities offered by the college and the Deanship of Student Affairs at the university level. The curriculum is type-centered, with many active learning activities that increase student socialization with colleagues and tutors. In addition, academic advisory services play a significant role in determining social life, providing an excellent psychological support and feedback system for relevant students. In conclusion, this study showed that monitoring the educational environment could provide important information that medical educators should use to address the challenges that need attention and implement improvement changes.
According to the study, half of the students perceived social life more positively, as indicated by a mean score of 15.66 ± 5.813. In addition to comparing these findings with those of other Saudi Arabian universities, this study also compares them with those of Sudan, Pakistan, and Malaysia. According to the current study, extracurricular activities, type-centered curricula with active learning activities, and academic advisory services providing psychological support and feedback to students contributed to the positive perception of social life. Students perceived social life positively in 50% of cases, with a mean score of 15.66. The DREEM scale for social life ranges from 0 to 28, with higher scores indicating a more positive view. A mean score of 15.66 indicates that social life within the educational environment has room for improvement. Social life is viewed positively by several factors identified in this study.
Students' socialization is likely to be promoted by extracurricular activities offered by the college and by the university’s Deanship of Student Affairs. Such activities allow students to interact with their peers outside the classroom, thereby fostering social connections and a sense of belonging. Student engagement and tutor–student interaction can be increased through a type-centered curriculum with active learning activities. Students collaborate and build relationships with their instructors and each other through active learning approaches such as group discussions, team-based projects, and hands-on learning experiences.
Students can benefit from the psychological support and feedback offered by academic advisory services, which can enhance their social life. This can contribute to a more positive social experience for students who receive guidance and mentorship from advisors. The study results were compared with those from other institutes in Saudi Arabia, Sudan, Pakistan, and Malaysia regarding social life perception. Although these studies provide specific DREEM scores, it is evident that perceptions of social life across institutions are similar. Based on the comparable scores, social life deserves attention and improvement in various educational environments.
The study concluded that the educational environment and students' perceptions of social life should be monitored. According to 50% of the participants, extracurricular activities, a type-centered curriculum with active learning, and academic advisory services contribute to a positive social experience. This emphasizes the need for medical educators to address challenges and implement changes to enhance students' social experiences, even though there is still room for improvement. Educators can create a more supportive and enriched social environment for students by understanding their perceptions.
According to the DREEM inventory evaluations, the educational environment at Prince Nora University in Saudi Arabia has generally been positively perceived. The students' recognition of academic ambience appears exceptionally high, although there is scope for bolstering SocialSselP-perceptions. Notably, the DREEM inventory has emerged as a powerful and all-encompassing tool to gauge different facets of the educational environment, proving its irreplaceable value in this scenario. Consequently, it is suggested that other Saudi Arabian universities might find it beneficial to adopt this tool to pinpoint possible difficulties and opportunities for the enhancement of their educational landscapes. Other Saudi Arabian universities should consider adopting the DREEM inventory to identify areas for improvement and opportunities to enhance their own educational settings based on the positive perception of the educational environment at Prince Nora University and the effectiveness of the DREEM inventory.Declaration.
All authors have shared their raw data and attached them to the supplementary files.
Dundee Ready Environment Educational Measure
Health professional education
National Commission for Accreditation and Assessment
Princess Noura University
Students' Academic Self-Perceptions
Student Perceptions of Atmosphere
Students' perceptions of learning
Students' Perceptions of Teachers
Student Social Self-Perceptions
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The authors appreciate Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R283), Princess Nourah Bint Abdulrahman University, Riyadh, SA.
The authors thank Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R283), Princess Nourah Bint Abdulrahman University, Riyadh, SA.
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Latefa Hamad Al Fryan
Applied College, Education (Curriculum and Instruction), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
Mahasin Ibrahim Shomo
Department of Pathological Sciences, College of Medicine, Ajman University, Ajman, UAE
Ibrahim A. Bani
Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, USA
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Ibrahim Bani designed the research, conceived the idea, developed the theory, verified the analytical methods, supervised the findings of this work, and prepared and edited the manuscript. On the other hand, Latefa Hamad Al Fryan and Mahasin Ibrahim Shomo were responsible for data collection and analysis, which are commonly used to present complex information in a concise and understandable format.
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Al Fryan, L.H., Shomo, M.I. & Bani, I.A. Assessment of the ‘students’ perceptions of education using Dundee Ready Environment Educational Measure (DREEM) inventory at Princess Nora bint Abdulrahman University, Saudi Arabia. BMC Med Educ 24 , 928 (2024). https://doi.org/10.1186/s12909-024-05870-9
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Confidence in U.S. public opinion polling was shaken by errors in 2016 and 2020. In both years’ general elections, many polls underestimated the strength of Republican candidates, including Donald Trump. These errors laid bare some real limitations of polling.
In the midterms that followed those elections, polling performed better . But many Americans remain skeptical that it can paint an accurate portrait of the public’s political preferences.
Restoring people’s confidence in polling is an important goal, because robust and independent public polling has a critical role to play in a democratic society. It gathers and publishes information about the well-being of the public and about citizens’ views on major issues. And it provides an important counterweight to people in power, or those seeking power, when they make claims about “what the people want.”
The challenges facing polling are undeniable. In addition to the longstanding issues of rising nonresponse and cost, summer 2024 brought extraordinary events that transformed the presidential race . The good news is that people with deep knowledge of polling are working hard to fix the problems exposed in 2016 and 2020, experimenting with more data sources and interview approaches than ever before. Still, polls are more useful to the public if people have realistic expectations about what surveys can do well – and what they cannot.
With that in mind, here are some key points to know about polling heading into this year’s presidential election.
Probability sampling (or “random sampling”). This refers to a polling method in which survey participants are recruited using random sampling from a database or list that includes nearly everyone in the population. The pollster selects the sample. The survey is not open for anyone who wants to sign up.
Online opt-in polling (or “nonprobability sampling”). These polls are recruited using a variety of methods that are sometimes referred to as “convenience sampling.” Respondents come from a variety of online sources such as ads on social media or search engines, websites offering rewards in exchange for survey participation, or self-enrollment. Unlike surveys with probability samples, people can volunteer to participate in opt-in surveys.
Nonresponse and nonresponse bias. Nonresponse is when someone sampled for a survey does not participate. Nonresponse bias occurs when the pattern of nonresponse leads to error in a poll estimate. For example, college graduates are more likely than those without a degree to participate in surveys, leading to the potential that the share of college graduates in the resulting sample will be too high.
Mode of interview. This refers to the format in which respondents are presented with and respond to survey questions. The most common modes are online, live telephone, text message and paper. Some polls use more than one mode.
Weighting. This is a statistical procedure pollsters perform to make their survey align with the broader population on key characteristics like age, race, etc. For example, if a survey has too many college graduates compared with their share in the population, people without a college degree are “weighted up” to match the proper share.
Pollsters are making changes in response to the problems in previous elections. As a result, polling is different today than in 2016. Most U.S. polling organizations that conducted and publicly released national surveys in both 2016 and 2022 (61%) used methods in 2022 that differed from what they used in 2016 . And change has continued since 2022.
One change is that the number of active polling organizations has grown significantly, indicating that there are fewer barriers to entry into the polling field. The number of organizations that conduct national election polls more than doubled between 2000 and 2022.
This growth has been driven largely by pollsters using inexpensive opt-in sampling methods. But previous Pew Research Center analyses have demonstrated how surveys that use nonprobability sampling may have errors twice as large , on average, as those that use probability sampling.
The second change is that many of the more prominent polling organizations that use probability sampling – including Pew Research Center – have shifted from conducting polls primarily by telephone to using online methods, or some combination of online, mail and telephone. The result is that polling methodologies are far more diverse now than in the past.
(For more about how public opinion polling works, including a chapter on election polls, read our short online course on public opinion polling basics .)
All good polling relies on statistical adjustment called “weighting,” which makes sure that the survey sample aligns with the broader population on key characteristics. Historically, public opinion researchers have adjusted their data using a core set of demographic variables to correct imbalances between the survey sample and the population.
But there is a growing realization among survey researchers that weighting a poll on just a few variables like age, race and gender is insufficient for getting accurate results. Some groups of people – such as older adults and college graduates – are more likely to take surveys, which can lead to errors that are too sizable for a simple three- or four-variable adjustment to work well. Adjusting on more variables produces more accurate results, according to Center studies in 2016 and 2018 .
A number of pollsters have taken this lesson to heart. For example, recent high-quality polls by Gallup and The New York Times/Siena College adjusted on eight and 12 variables, respectively. Our own polls typically adjust on 12 variables . In a perfect world, it wouldn’t be necessary to have that much intervention by the pollster. But the real world of survey research is not perfect.
Predicting who will vote is critical – and difficult. Preelection polls face one crucial challenge that routine opinion polls do not: determining who of the people surveyed will actually cast a ballot.
Roughly a third of eligible Americans do not vote in presidential elections , despite the enormous attention paid to these contests. Determining who will abstain is difficult because people can’t perfectly predict their future behavior – and because many people feel social pressure to say they’ll vote even if it’s unlikely.
No one knows the profile of voters ahead of Election Day. We can’t know for sure whether young people will turn out in greater numbers than usual, or whether key racial or ethnic groups will do so. This means pollsters are left to make educated guesses about turnout, often using a mix of historical data and current measures of voting enthusiasm. This is very different from routine opinion polls, which mostly do not ask about people’s future intentions.
When major news breaks, a poll’s timing can matter. Public opinion on most issues is remarkably stable, so you don’t necessarily need a recent poll about an issue to get a sense of what people think about it. But dramatic events can and do change public opinion , especially when people are first learning about a new topic. For example, polls this summer saw notable changes in voter attitudes following Joe Biden’s withdrawal from the presidential race. Polls taken immediately after a major event may pick up a shift in public opinion, but those shifts are sometimes short-lived. Polls fielded weeks or months later are what allow us to see whether an event has had a long-term impact on the public’s psyche.
The answer to this question depends on what you want polls to do. Polls are used for all kinds of purposes in addition to showing who’s ahead and who’s behind in a campaign. Fair or not, however, the accuracy of election polling is usually judged by how closely the polls matched the outcome of the election.
By this standard, polling in 2016 and 2020 performed poorly. In both years, state polling was characterized by serious errors. National polling did reasonably well in 2016 but faltered in 2020.
In 2020, a post-election review of polling by the American Association for Public Opinion Research (AAPOR) found that “the 2020 polls featured polling error of an unusual magnitude: It was the highest in 40 years for the national popular vote and the highest in at least 20 years for state-level estimates of the vote in presidential, senatorial, and gubernatorial contests.”
How big were the errors? Polls conducted in the last two weeks before the election suggested that Biden’s margin over Trump was nearly twice as large as it ended up being in the final national vote tally.
Errors of this size make it difficult to be confident about who is leading if the election is closely contested, as many U.S. elections are .
Pollsters are rightly working to improve the accuracy of their polls. But even an error of 4 or 5 percentage points isn’t too concerning if the purpose of the poll is to describe whether the public has favorable or unfavorable opinions about candidates , or to show which issues matter to which voters. And on questions that gauge where people stand on issues, we usually want to know broadly where the public stands. We don’t necessarily need to know the precise share of Americans who say, for example, that climate change is mostly caused by human activity. Even judged by its performance in recent elections, polling can still provide a faithful picture of public sentiment on the important issues of the day.
The 2022 midterms saw generally accurate polling, despite a wave of partisan polls predicting a broad Republican victory. In fact, FiveThirtyEight found that “polls were more accurate in 2022 than in any cycle since at least 1998, with almost no bias toward either party.” Moreover, a handful of contrarian polls that predicted a 2022 “red wave” largely washed out when the votes were tallied. In sum, if we focus on polling in the most recent national election, there’s plenty of reason to be encouraged.
Compared with other elections in the past 20 years, polls have been less accurate when Donald Trump is on the ballot. Preelection surveys suffered from large errors – especially at the state level – in 2016 and 2020, when Trump was standing for election. But they performed reasonably well in the 2018 and 2022 midterms, when he was not.
During the 2016 campaign, observers speculated about the possibility that Trump supporters might be less willing to express their support to a pollster – a phenomenon sometimes described as the “shy Trump effect.” But a committee of polling experts evaluated five different tests of the “shy Trump” theory and turned up little to no evidence for each one . Later, Pew Research Center and, in a separate test, a researcher from Yale also found little to no evidence in support of the claim.
Instead, two other explanations are more likely. One is about the difficulty of estimating who will turn out to vote. Research has found that Trump is popular among people who tend to sit out midterms but turn out for him in presidential election years. Since pollsters often use past turnout to predict who will vote, it can be difficult to anticipate when irregular voters will actually show up.
The other explanation is that Republicans in the Trump era have become a little less likely than Democrats to participate in polls . Pollsters call this “partisan nonresponse bias.” Surprisingly, polls historically have not shown any particular pattern of favoring one side or the other. The errors that favored Democratic candidates in the past eight years may be a result of the growth of political polarization, along with declining trust among conservatives in news organizations and other institutions that conduct polls.
Whatever the cause, the fact that Trump is again the nominee of the Republican Party means that pollsters must be especially careful to make sure all segments of the population are properly represented in surveys.
The real margin of error is often about double the one reported. A typical election poll sample of about 1,000 people has a margin of sampling error that’s about plus or minus 3 percentage points. That number expresses the uncertainty that results from taking a sample of the population rather than interviewing everyone . Random samples are likely to differ a little from the population just by chance, in the same way that the quality of your hand in a card game varies from one deal to the next.
The problem is that sampling error is not the only kind of error that affects a poll. Those other kinds of error, in fact, can be as large or larger than sampling error. Consequently, the reported margin of error can lead people to think that polls are more accurate than they really are.
There are three other, equally important sources of error in polling: noncoverage error , where not all the target population has a chance of being sampled; nonresponse error, where certain groups of people may be less likely to participate; and measurement error, where people may not properly understand the questions or misreport their opinions. Not only does the margin of error fail to account for those other sources of potential error, putting a number only on sampling error implies to the public that other kinds of error do not exist.
Several recent studies show that the average total error in a poll estimate may be closer to twice as large as that implied by a typical margin of sampling error. This hidden error underscores the fact that polls may not be precise enough to call the winner in a close election.
Transparency in how a poll was conducted is associated with better accuracy . The polling industry has several platforms and initiatives aimed at promoting transparency in survey methodology. These include AAPOR’s transparency initiative and the Roper Center archive . Polling organizations that participate in these organizations have less error, on average, than those that don’t participate, an analysis by FiveThirtyEight found .
Participation in these transparency efforts does not guarantee that a poll is rigorous, but it is undoubtedly a positive signal. Transparency in polling means disclosing essential information, including the poll’s sponsor, the data collection firm, where and how participants were selected, modes of interview, field dates, sample size, question wording, and weighting procedures.
There is evidence that when the public is told that a candidate is extremely likely to win, some people may be less likely to vote . Following the 2016 election, many people wondered whether the pervasive forecasts that seemed to all but guarantee a Hillary Clinton victory – two modelers put her chances at 99% – led some would-be voters to conclude that the race was effectively over and that their vote would not make a difference. There is scientific research to back up that claim: A team of researchers found experimental evidence that when people have high confidence that one candidate will win, they are less likely to vote. This helps explain why some polling analysts say elections should be covered using traditional polling estimates and margins of error rather than speculative win probabilities (also known as “probabilistic forecasts”).
National polls tell us what the entire public thinks about the presidential candidates, but the outcome of the election is determined state by state in the Electoral College . The 2000 and 2016 presidential elections demonstrated a difficult truth: The candidate with the largest share of support among all voters in the United States sometimes loses the election. In those two elections, the national popular vote winners (Al Gore and Hillary Clinton) lost the election in the Electoral College (to George W. Bush and Donald Trump). In recent years, analysts have shown that Republican candidates do somewhat better in the Electoral College than in the popular vote because every state gets three electoral votes regardless of population – and many less-populated states are rural and more Republican.
For some, this raises the question: What is the use of national polls if they don’t tell us who is likely to win the presidency? In fact, national polls try to gauge the opinions of all Americans, regardless of whether they live in a battleground state like Pennsylvania, a reliably red state like Idaho or a reliably blue state like Rhode Island. In short, national polls tell us what the entire citizenry is thinking. Polls that focus only on the competitive states run the risk of giving too little attention to the needs and views of the vast majority of Americans who live in uncompetitive states – about 80%.
Fortunately, this is not how most pollsters view the world . As the noted political scientist Sidney Verba explained, “Surveys produce just what democracy is supposed to produce – equal representation of all citizens.”
Scott Keeter is a senior survey advisor at Pew Research Center .
Courtney Kennedy is Vice President of Methods and Innovation at Pew Research Center .
How public polling has changed in the 21st century, what 2020’s election poll errors tell us about the accuracy of issue polling, a field guide to polling: election 2020 edition, methods 101: how is polling done around the world, most popular.
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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .
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Research environments, or cultures, are thought to be the most influential predictors of research productivity. Although several narrative and systematic reviews have begun to identify the characteristics of research‐favourable environments, these reviews have ignored the contextual complexities and multiplicity of environmental characteristics.
Quantitative research is a pivotal aspect of academic inquiry, and understanding its fundamentals is crucial for anyone venturing into the realm of research. We'll explore the definitions and perspectives of quantitative research according to John Creswell, along with other notable scholars in the field. These insights are not only foundational for grasping the essence of quantitative ...
quantitative research are: Describing a problem statement by presenting the need for an explanation of a variable's relationship. Offering literature, a significant function by answering research ...
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative research is widely used in psychology, economics, demography, sociology, marketing, community health, health & human development, gender studies, and political science; and less frequently in anthropology and history. Research in mathematical sciences, such as physics, is also "quantitative" by definition, though this use of the term differs in context. In the social sciences, the ...
It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or ...
Quantitative research is an important part of market research that relies on hard facts and numerical data to gain as objective a picture of people's opinions as possible. It's different from qualitative research in a number of important ways and is a highly useful tool for researchers. Quantitative research is a systematic empirical ...
Quantitative research is a systematic data collection and analysis approach emphasizing quantifiable and numerical data. It employs statistical and computational techniques to measure, analyze, and interpret phenomena to uncover patterns, relationships, and trends. Unlike qualitative research, which focuses on subjective experiences and ...
What is Qualitative Research? Qualitative research differs from quantitative research in its objectives, techniques, and design. Qualitative research aims to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified using mathematics. Instead of seeking to uncover precise answers or statistics in a controlled environment like quantitative research ...
Quantitative research applies statisti cal tests, such as mean, median, and standard deviation, t-tests, multiple regression correlations (MRC), analysis of variances (ANOVAs), etc. Quantitative ...
Consideration is given to the opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence.
PDF | Successful research environment requires joint effort by individual researchers, research groups and the organization. This chapter describes the... | Find, read and cite all the research ...
Research setting is an important component of research design/methodology. If you have been asked to describe the setting of your study, note any aspects related to the environment in which your study is being conducted. You may want to refer to the author guidelines of your target journal to confirm which specific details the journal requires.
Here are some examples: Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
Descriptive research design is employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon. Read this comprehensive article to know what descriptive research is and the different methods, types and examples.
Organizational behavior or organisational behaviour (see spelling differences) is the "study of human behavior in organizational settings, the interface between human behavior and the organization, and the organization itself". [1] Organizational behavioral research can be categorized in at least three ways: [2] individuals in organizations (micro-level)
Deductive reasoning is the process of drawing valid inferences.An inference is valid if its conclusion follows logically from its premises, meaning that it is impossible for the premises to be true and the conclusion to be false. Deductive logic is the discipline studying the laws of deductive reasoning.. For example, the inference from the premises "all men are mortal" and "Socrates is a man ...
Quantitative research is regarded as the organized inquiry about phenomenon through collection. of numer ical data and execution of statistical, mathematical or computational techniques. The ...
The Competitive Landscape of the Anal Cancer MarketIn today's competitive business environment, the global Anal Cancer market stands as a critical battleground for businesses seeking to carve out a niche and drive growth. As industries grapple with the complexities of this market, understanding the competitive landscape becomes paramount for strategic decision-making and success.The global ...
Background Educational settings in professional health education establishments significantly shape students' academic, social, and emotional experiences. These environments encompass physical, psychological, and social infrastructures of programs or institutions, which jointly influence learning and development. This study analyzed the educational environment at Prince Nora University in ...
The real environment in which polls are conducted bears little resemblance to the idealized settings presented in textbooks. ... Research has found that Trump is popular among people who tend to sit out midterms but turn out for him in presidential election years. Since pollsters often use past turnout to predict who will vote, it can be ...