sample questionnaire for nursing research

Surveys and questionnaires in nursing research

Affiliation.

• 1 Trinity College Dublin, Dublin, Ireland.
• PMID: 26080989
• DOI: 10.7748/ns.29.42.42.e8904

Surveys and questionnaires are often used in nursing research to elicit the views of large groups of people to develop the nursing knowledge base. This article provides an overview of survey and questionnaire use in nursing research, clarifies the place of the questionnaire as a data collection tool in quantitative research design and provides information and advice about best practice in the development of quantitative surveys and questionnaires.

Keywords: Data collection; nursing research; qualitative research; quantitative research; questionnaire; research; survey; validity.

• Nursing Research / methods*
• Surveys and Questionnaires* / standards

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Survey Research: An Effective Design for Conducting Nursing Research

• Vicki A. Keough, PhD, RN-BC, ACNP Vicki A. Keough Affiliations Dean and Professor at Loyola University Chicago, Marcella Niehoff School of Nursing, Maywood, Illinois Search for articles by this author
• Paula Tanabe, PhD, MPH, RN Paula Tanabe Affiliations Research Assistant Professor in the Department of Emergency Medicine and the Institute for Healthcare Studies at Northwestern University, Feinberg School of Medicine, Chicago, Illinois Search for articles by this author

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• • Describe the steps of the survey research project.
• • Differentiate survey research methods.
• a. social desirability
• b. social status.
• c. validated practice.
• d. validated response.
• a. Web-based
• b. Face-to-face interviews
• c. U.S. mail
• a. They have the potential for researcher bias.
• b. They are time consuming.
• c. They reach too many participants.
• d. They have the potential for subject bias.
• a. A signed consent form from each participant is required.
• b. Approval from an institutional review board is not needed.
• c. Informed consent is implied when the survey is completed and returned.
• d. Respondents cannot be asked for information that would identify them.
• a. Purposive sample
• b. Population study
• c. Target survey
• d. Subset sample
• a. A questionnaire sent by registered mail
• b. A questionnaire that is at least 10 pages long
• c. Four contacts by mail followed by a "special" contact
• d. The addition of a form letter to the questionnaire
• a. outcome validity.
• b. inter-rater validity.
• c. face validity.
• d. construct validity.
• a. Outcome validity
• b. Inter-rater validity
• c. Face validity
• d. Construct validity
• a. inter-rater reliability.
• b. intra-rater reliability.
• c. concept validity.
• d. database validity.
• a. send the surveys out in waves.
• b. send all surveys out at one time.
• c. hold data entry until the end of data collection.
• d. hold data cleaning until the end of data collection.
• a. Statistical techniques should be independent of the design.
• b. Statistical techniques should match the design.
• c. Regression models should be used in the analysis.
• d. Pattern testing should be used in the analysis.
• c. Data analysis
• d. Discussion
• • Describe the steps of the survey research project. 1 2 3 4 5 ______________
• • Differentiate survey research methods. 1 2 3 4 5 ______________
• 2 Were the authors knowledgeable about the subject? 1 2 3 4 5 ______________
• 3 Were the methods of presentation (text, tables, figures, etc.) effective? 1 2 3 4 5 ______________
• 4 Was the content relevant to the objectives? 1 2 3 4 5 ______________
• 5 Was the article useful to you in your work? 1 2 3 4 5 ______________
• 6 Was there enough time allotted for this activity? 1 2 3 4 5 ______________ Comments: ______________ ______________ ______________ ______________ ______________ ______________
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DOI: https://doi.org/10.1016/S2155-8256(15)30315-X

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Narrowing a Clinical Question

Two types of clinical questions, what is pico anyway, picott alternatives and additions, pico process in action, using pico to form the research question, tips and tricks.

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To begin to develop and narrow a clinical research question it is advisable to craft an answerable question that begins and ends with a patient, population, or problem. These are the beginnings of not only developing an answerable EBP question, but also using the PICO process  for developing well-built searchable and answerable clinical questions. 

There are many elements to developing a good clinical question. Clinical questions can be further divided into two major areas: Background Questions and Foreground Questions .

Background Questions refer to general knowledge and facts. The majority of the information that can be used to inform answers to background questions are found in reference resources like Encyclopedias, Dictionaries, Textbooks, Atlases, Almanacs, Government Publications & Statistical Information, and Indexes.

Foreground Questions are generally more precise and usually revolve around patient/s, populations, or a specific problem. Crafting an appropriate EBP question will not only inform your search strategy which you will apply to the medical literature but will also create a framework for how to maintain and develop your investigative process.

What are some examples of P ?

• Diabetes mellitus, Type 2 (problem) Obese
• elderly (population)

What are some examples of I ?

• Chlorpropamide

What are some examples of C ?

What are some examples of O ?

• Management of glucose levels

Using the example from the bottom-center we can start forming a research question: 

Is Chlorpropamide (intevention) more efficient than Metformin (comparator) in managing Diabetes Mellitus Type 2 (problem) for obese elderly patients (population)?

*Note: It is not necessary to use every element in PICO or to have both a problem and population in your question. PICO is a tool that helps researchers frame an answerable EBP question. 

Synonyms can very helpful throughout your investigative and research process. Using synonyms with boolean operators can potentially expand your search. Databases with subject headings or controlled vocabularies like MeSH in PubMed often have a thesaurus that can match you with appropriate terms.

Boolean operators allow you to manipulate your search.

Use AND to narrow your search

  eg. elderly AND diabetes  

Use OR to broaden your search

  eg. myocardial infarction OR heart attack

Use NOT to exclude terms from your search

  eg. children NOT infants  

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• Volume 17, Issue 2
• Selecting the sample
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• Allison Shorten 1 ,
• Calvin Moorley 2
• 1 Yale University School of Nursing , West Haven, Connecticut , USA
• 2 University of East London, School of Health, Sport and Bioscience, Stratford , London , UK
• Correspondence to : Dr Allison Shorten , Yale University School of Nursing, PO Box 27399, West Haven, CT 06516-7399, USA; allison.shorten{at}yale.edu

https://doi.org/10.1136/eb-2014-101747

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Introduction

Sample selection is a key factor in research design and can determine whether research questions will be answered before the study has even begun. Good sample selection and appropriate sample size strengthen a study, protecting valuable time, money and resources. In the context of healthcare research, poor design could lead to use of harmful practices, delays in new treatment and lost opportunities for high quality care. Not every study can achieve design perfection and researchers often seek a balance between the ideal sample and one that is feasible or convenient, acknowledging the limitations of their design decisions. Sample selection is far from simple but here are some of the techniques to think about as you read research and make the most out of your research endeavours.

Sampling the population

Different sampling methods are used depending on the aim of the study and whether the research question seeks a confident answer about the population of interest. If it does then the sample/s should represent the population for inferences to be made. Not all research questions depend on making inferences and there are many examples in qualitative research where the aim is theory development or exploration of patient experiences and inferences are not the focus.

Sampling methods: quantitative research

Probability (representative) sampling includes techniques used to select a sample that clearly represents a specific population. Participants are randomly selected so that all members of the population have an equal chance of being selected in the sample, eliminating the possibility of sample selection bias.

Example: Let's say Hospital A is interested in the opinions of all 1000 of their patients who underwent knee surgery during the calendar year 2013 (the population). The Quality Assurance (QA) budget will not allow for all patients to be surveyed but they want to be sure the sample represents all patients. One way to do this is by selecting a random sample of a prespecified number of patients.

If simple random selection was used, the desired number of patients (eg, 200 or 20%) would be randomly selected from a complete list of 1000 patients using random numbers between 1 and 1000. Random numbers can be generated using a spreadsheet program or an online randomiser that can export a set of numbers into spreadsheet programs such as excel http://www.randomizer.org/form.htm 1

A stratified random sample could be selected for a more targeted approach. If the hospital was aware that there were only 400 women who had knee surgery in that year and wanted the sample to represent known gender proportions for all patients having knee surgery they could stratify the populations first. In this case, the patient population would be divided (stratified) according to gender before simple random selection was used to identify 40% (n=80) female and 60% (n=120) male patients for the sample.

Non-probability sampling (non-representative) although usually systematic and capable of answering research questions, can suffer from issues associated with sample selection bias. Let's say the QA nurse at Hospital A is interested in patient opinions about the new hospital admission and discharge process for knee surgery patients. Owing to budget limitations and the fact that the QA nurse only works 1 day/week, the nurse uses a convenient purposive sample to survey patients who experience knee surgery every Friday when she is in the hospital. This particular convenience sample may be representative of patients who experienced knee surgery on Fridays (the sample); however, it would not necessarily represent all patients who experienced knee surgery in that hospital (the population). There may be differences in the characteristics of patients having surgery on Fridays. Perhaps the surgeons who operate on Fridays mostly see sports-injured male patients (age range 25–35 years) with private health insurance compared with the surgeons who operate during the remainder of the week serving male and female publicly insured patients (age range 35–55 years). Potential differences in sample characteristics such as age, gender, level of presurgery fitness, income, education and employment could be unique to the sample and fail to answer the question for all patients in the hospital.

Sample size

Selecting the right sample size is about predicting in advance that the sample size will be large enough to give adequate ‘power’ to the study. The ‘power’ of a study can be defined as the probability of correctly identifying that the intervention produces a treatment effect if one actually exists. Statisticians state that we correctly reject the null hypothesis of no treatment effect, or alternatively as the probability of avoiding a type II error.

There are many sample size power calculators available online such as http://www.stat.ubc.ca/~rollin/stats/ssize/index.html 2

To calculate a sample size the researcher needs to understand the type of variables being compared (eg, mean values vs proportions or percentages); be able to specify the variables of interest for each sample; estimate the likely size of the treatment effect: estimate the SD of the combined samples (if comparing means); specify whether the calculation should be based on a one-sided or two-sided test (eg, if it is an intervention study and it is thought that the treatment may have the potential to either improve or harm patient outcomes, then a two-sided test should be used); and finally to specify values for both ‘α’ and ‘power’. The ‘α’ reflects our level of willingness to tolerate a type I error—incorrectly rejecting a null hypothesis (a false positive). Conventionally, researchers set α at 0.05 (5%) when conducting sample size power calculations. The ‘power’ indicates the probability that the samples selected will detect an intervention effect if one exists. There is a conventional value of 0.8 (or 80%).

Sample size calculation is not an exact science and sometimes decisions are made without prior research to inform the estimates. Pilot studies are often needed to provide data to guide sample size predictions. Watch out for studies where sample size is small with unrealistic or unsubstantiated estimates for large intervention effects. This can be a sign that the smaller sample size has been decided first with post hoc power calculations to justify this.

Sampling methods: qualitative research

Similar to quantitative studies, qualitative research questions determine the sample and sampling strategies used. When we ask ‘why’ and ‘how’ questions to illuminate, unravel and understand complex human psychosocial issues, the focus is not on predetermined hypotheses or generalisable results. Samples may be sought for focus groups, personal interviews (structured, semistructured or unstructured) or for participatory, observational and ethnographic work. 3 This type of research often involves a small number of participants to explore in-depth feelings, experiences and held beliefs. A sample can be as small as one person (a case study) and sampling ends when saturation of emerging themes has been reached. Data analysis begins at data collection, requiring a design that is flexible and iterative, as the researcher may move backward to move forward in response to emerging themes.

There are three broad qualitative sampling approaches; convenience, judgement and theoretical sampling.

Convenience samples are recruited based on accessibility to the researcher. An example is snowball sampling where participants guide the researcher to the next participant, via acquaintances or social networks. Snowballing is useful when studying hard to reach groups but can result in a sample bias. Convenience sampling is the least rigorous technique used and can result in poor-quality data, which should be balanced against resource savings (time and money) or convenience.

Judgement sampling , commonly referred to as purposeful or selective sampling, relies on the judgement and practical knowledge of the researcher to identify and select participants. A framework for selection can be developed from variables identified in the literature, combined with practical knowledge of the phenomena. This is a more systematic strategy and can increase sample credibility using a wide range of participants, for example, those with in-depth experience or special knowledge of the research topic.

Theoretical sampling is a hallmark of the grounded theory approach. 4 Here, the sample is theory driven and the researcher continues data collection and analysis until theoretical saturation is reached. The samples are used to build and produce an interpretative theory. This is useful when the aim is theory and concept development, grounded in or emergent from real-world life events and circumstances.

Sample selection is far from simple so check your research design and sample selection plan with an expert statistician or research consultant early, and before the data collection begins. Designing a study or writing research questions to suit a sample that is merely convenient to reach is dangerous and readers should always be alert to signs of poor practice in sample selection. Great samples are worth the effort.

• Urbaniak GC ,
• ↵ UCSF Department of Epidemiology and Biostatistics . Power and Sample Size Calculations . http://www.stat.ubc.ca/~rollin/stats/ssize/index.html
• Thorogood N

Competing interests None.

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Establishing appropriate sample size for developing and validating a questionnaire in nursing research

2021, Belitung Nursing Journal

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National Sample Survey of Registered Nurses (NSSRN)

New findings on the state of the nursing workforce.

In March 2024, HRSA released new data, survey results, and workforce projections on the U.S. nursing workforce.

Key findings of the National Sample Survey of Registered Nurses (NSSRN) show that the nursing workforce is becoming more diverse, more highly educated, but less satisfied with their job. The survey data also show the effects of COVID on the profession, while workforce projections show shortages increasing in nursing occupations through 2036.

Read the factsheet (PDF - 83 KB) for more information.

In 2022–2023, the National Center for Health Workforce Analysis, in collaboration with the U.S. Census Bureau, surveyed registered nurses in the United States. Nearly 50,000 registered nurses provided data. We compiled the results, produced several reports on the data, and updated an easy-to-use dashboard to display the latest information.

From its inaugural assessment in 1977, the National Sample Survey of Registered Nurses (NSSRN) represents the longest running survey of registered nurses (RNs) in the United States. The survey examines the characteristics of registered nurses and their experiences in nursing.

How can I review findings on the nursing workforce?

We provide summary reports as well as a dashboard to present our findings on the nursing workforce, based on data from the 2022 NSSRN.

Nursing reports and briefs

• Nursing Education and Training: Data from the 2022 NSSRN (PDF - 425 KB)
• Experiences of Nurses Working During the COVID-19 Pandemic: Data from the 2022 NSSRN (PDF - 288 KB)
• Job Satisfaction Among Registered Nurses: Data from the 2022 NSSRN (PDF - 389 KB)

Nursing Workforce Dashboard

The Nursing Workforce Dashboard visualizes data from both the 2022 and 2018 NSSRN, which includes detailed information on the nursing workforce in the United States. This dashboard provides insights on the nursing profession by showing their work environment, education, demographics, hours, earnings, and more. It also helps us predict what nurses will need in the future.

The dashboard enables you to access the 400,000 unique data points from the survey and visualize this data on the nursing workforce landscape, including demographics, employment, education, earnings, and hours for various categories of nurses (RNs, NPs, and APRNs).

The dashboard serves as a benchmark for providing educators, health workforce leaders, and policymakers with key details and developments of the nursing workforce supply.

Because these data were based on the nursing workforce in December 2021, the impact of the COVID-19 pandemic is now reflected in the dashboard.

Training videos on the Nursing Dashboard

We developed these videos in Zoom to demonstrate how to use the features of the dashboard.

Part 1: Introduction, the Demographics Tab, the Education/Licenses Tab (21:34 min) Part 2: The Employment and Work Environment Tabs (8:32 min) Part 3: The Earnings and Hours Tabs  (8:16 min)

Where can I find more data from the NSSRN?

Visit the Data Warehouse for  Nursing Workforce Survey Data . Find data on registered nurses from 1977-2022 and nurse practitioners from our 2012 survey.

You can also download the survey questionnaire, 2022 National Sample Survey of Registered Nurses (PDF - 1 MB) .

Where can I find information on the 2018 NSSRN?

We maintain the following information on the 2018 survey and findings from the data.

• Brief Summary of Results: 2018 NSSRN (PDF - 848 KB)
• Technical Documentation for the 2018 NSSRN (PDF - 7 MB) *
• Nursing Education and Training in the United States (PDF - 524 KB)
• Brief: Job Satisfaction Among Registered Nurses – Pre-COVID
• 2018 National Sample Survey of Registered Nurses (PDF - 906 KB) *

Who do I contact with questions?

Email us at [email protected] .

* If you use assistive technology, you may not be able to access information in this file. For help, call 1-800-221-9393 (TTY: 877-897-9910), 8 a.m.-8 p.m. ET weekdays (except federal holidays).

Nursing Research Nursing Test Bank and Practice Questions (60 Items)

Welcome to your nursing test bank and practice questions for nursing research.

Nursing Research Test Bank

Nursing research has a great significance on the contemporary and future professional nursing practice, thus rendering it an essential component of the educational process. Research is typically not among the traditional responsibilities of an entry-level  nurse . Many nurses are involved in either direct patient care or administrative aspects of health care. However, nursing research is a growing field in which individuals within the profession can contribute a variety of skills and experiences to the science of nursing care. Nursing research is critical to the nursing profession and is necessary for continuing advancements that promote optimal nursing care. Test your knowledge about nursing research in this 60-item nursing test bank.

Quiz Guidelines

Before you start, here are some examination guidelines and reminders you must read:

• Practice Exams : Engage with our Practice Exams to hone your skills in a supportive, low-pressure environment. These exams provide immediate feedback and explanations, helping you grasp core concepts, identify improvement areas, and build confidence in your knowledge and abilities.
• You’re given 2 minutes per item.
• For Challenge Exams, click on the “Start Quiz” button to start the quiz.
• Complete the quiz : Ensure that you answer the entire quiz. Only after you’ve answered every item will the score and rationales be shown.
• Learn from the rationales : After each quiz, click on the “View Questions” button to understand the explanation for each answer.
• Free access : Guess what? Our test banks are 100% FREE. Skip the hassle – no sign-ups or registrations here. A sincere promise from Nurseslabs: we have not and won’t ever request your credit card details or personal info for our practice questions. We’re dedicated to keeping this service accessible and cost-free, especially for our amazing students and nurses. So, take the leap and elevate your career hassle-free!
• Share your thoughts : We’d love your feedback, scores, and questions! Please share them in the comments below.

Quizzes included in this guide are:

Recommended Resources

Recommended books and resources for your NCLEX success:

Disclosure: Included below are affiliate links from Amazon at no additional cost from you. We may earn a small commission from your purchase. For more information, check out our privacy policy .

Saunders Comprehensive Review for the NCLEX-RN Saunders Comprehensive Review for the NCLEX-RN Examination is often referred to as the best nursing exam review book ever. More than 5,700 practice questions are available in the text. Detailed test-taking strategies are provided for each question, with hints for analyzing and uncovering the correct answer option.

Strategies for Student Success on the Next Generation NCLEX® (NGN) Test Items Next Generation NCLEX®-style practice questions of all types are illustrated through stand-alone case studies and unfolding case studies. NCSBN Clinical Judgment Measurement Model (NCJMM) is included throughout with case scenarios that integrate the six clinical judgment cognitive skills.

Saunders Q & A Review for the NCLEX-RN® Examination This edition contains over 6,000 practice questions with each question containing a test-taking strategy and justifications for correct and incorrect answers to enhance review. Questions are organized according to the most recent NCLEX-RN test blueprint Client Needs and Integrated Processes. Questions are written at higher cognitive levels (applying, analyzing, synthesizing, evaluating, and creating) than those on the test itself.

NCLEX-RN Prep Plus by Kaplan The NCLEX-RN Prep Plus from Kaplan employs expert critical thinking techniques and targeted sample questions. This edition identifies seven types of NGN questions and explains in detail how to approach and answer each type. In addition, it provides 10 critical thinking pathways for analyzing exam questions.

Illustrated Study Guide for the NCLEX-RN® Exam The 10th edition of the Illustrated Study Guide for the NCLEX-RN Exam, 10th Edition. This study guide gives you a robust, visual, less-intimidating way to remember key facts. 2,500 review questions are now included on the Evolve companion website. 25 additional illustrations and mnemonics make the book more appealing than ever.

NCLEX RN Examination Prep Flashcards (2023 Edition) NCLEX RN Exam Review FlashCards Study Guide with Practice Test Questions [Full-Color Cards] from Test Prep Books. These flashcards are ready for use, allowing you to begin studying immediately. Each flash card is color-coded for easy subject identification.

Recommended Links

If you need more information or practice quizzes, please do visit the following links:

An investment in knowledge pays the best interest. Keep up the pace and continue learning with these practice quizzes:

• Nursing Test Bank: Free Practice Questions UPDATED ! Our most comprehenisve and updated nursing test bank that includes over 3,500 practice questions covering a wide range of nursing topics that are absolutely free!
• NCLEX Questions Nursing Test Bank and Review UPDATED! Over 1,000+ comprehensive NCLEX practice questions covering different nursing topics. We’ve made a significant effort to provide you with the most challenging questions along with insightful rationales for each question to reinforce learning.

3 thoughts on “Nursing Research Nursing Test Bank and Practice Questions (60 Items)”

Thanks for the well prepared questions and answers. It will be of a great help for those who look up your contributions.

Hi Zac, we’re having some performance issues with the quizzes so we’re forced to change their settings in the meantime. We are working on a solution and will revert the changes once we’re sure that the problem is resolved. Thanks for the understanding!

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• Open access
• Published: 17 April 2024

Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study

• Rawhia Salah Dogham 1 ,
• Heba Fakieh Mansy Ali 1 ,
• Asmaa Saber Ghaly 3 ,
• Nermine M. Elcokany 2 ,
• Mohamed Mahmoud Seweid 4 &
• Ayman Mohamed El-Ashry   ORCID: orcid.org/0000-0001-7718-4942 5  

BMC Nursing volume  23 , Article number:  249 ( 2024 ) Cite this article

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Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being.

This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students.

Design and Method

A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2 F).

Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches.

Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being.

Nursing implication

Understanding academic stress’s impact on nursing students’ learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.

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Introduction

Nursing education is a demanding field that requires students to acquire extensive knowledge and skills to provide competent and compassionate care. Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [ 1 , 2 ]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information. Surface learning approaches focus on memorization and reproducing information without a more profound understanding. Strategic learning approaches aim to achieve high grades by adopting specific strategies, such as memorization techniques or time management skills [ 3 , 4 , 5 ].

Nursing education stands out due to its focus on practical training, where the blend of academic and clinical coursework becomes a significant stressor for students, despite academic stress being shared among all university students [ 6 , 7 , 8 ]. Consequently, nursing students are recognized as prone to high-stress levels. Stress is the physiological and psychological response that occurs when a biological control system identifies a deviation between the desired (target) state and the actual state of a fitness-critical variable, whether that discrepancy arises internally or externally to the human [ 9 ]. Stress levels can vary from objective threats to subjective appraisals, making it a highly personalized response to circumstances. Failure to manage these demands leads to stress imbalance [ 10 ].

Nursing students face three primary stressors during their education: academic, clinical, and personal/social stress. Academic stress is caused by the fear of failure in exams, assessments, and training, as well as workload concerns [ 11 ]. Clinical stress, on the other hand, arises from work-related difficulties such as coping with death, fear of failure, and interpersonal dynamics within the organization. Personal and social stressors are caused by an imbalance between home and school, financial hardships, and other factors. Throughout their education, nursing students have to deal with heavy workloads, time constraints, clinical placements, and high academic expectations. Multiple studies have shown that nursing students experience higher stress levels compared to students in other fields [ 12 , 13 , 14 ].

Research has examined the relationship between academic stress and coping strategies among nursing students, but no studies focus specifically on the learning approach and academic stress. However, existing literature suggests that students interested in nursing tend to experience lower levels of academic stress [ 7 ]. Therefore, interest in nursing can lead to deep learning approaches, which promote a comprehensive understanding of the subject matter, allowing students to feel more confident and less overwhelmed by coursework and exams. Conversely, students employing surface learning approaches may experience higher stress levels due to the reliance on memorization [ 3 ].

Understanding the interplay between academic stress and learning approaches among nursing students is essential for designing effective educational interventions. Nursing educators can foster deep learning approaches by incorporating active learning strategies, critical thinking exercises, and reflection activities into the curriculum [ 15 ]. Creating supportive learning environments encouraging collaboration, self-care, and stress management techniques can help alleviate academic stress. Additionally, providing mentorship and counselling services tailored to nursing students’ unique challenges can contribute to their overall well-being and academic success [ 16 , 17 , 18 ].

Despite the scarcity of research focusing on the link between academic stress and learning methods in nursing students, it’s crucial to identify the unique stressors they encounter. The intensity of these stressors can be connected to the learning strategies employed by these students. Academic stress and learning approach are intertwined aspects of the student experience. While academic stress can influence learning approaches, the choice of learning approach can also impact the level of academic stress experienced. By understanding this relationship and implementing strategies to promote healthy learning approaches and manage academic stress, educators and institutions can foster an environment conducive to deep learning and student well-being.

Hence, this study aims to investigate the correlation between academic stress and learning approaches experienced by nursing students.

Study objectives

Assess the levels of academic stress among nursing students.

Assess the learning approaches among nursing students.

Identify the relationship between academic stress and learning approach among nursing students.

Identify the effect of academic stress and related factors on learning approach and among nursing students.

Materials and methods

Research design.

A cross-sectional descriptive correlation research design adhering to the STROBE guidelines was used for this study.

A research project was conducted at Alexandria Nursing College, situated in Egypt. The college adheres to the national standards for nursing education and functions under the jurisdiction of the Egyptian Ministry of Higher Education. Alexandria Nursing College comprises nine specialized nursing departments that offer various nursing specializations. These departments include Nursing Administration, Community Health Nursing, Gerontological Nursing, Medical-Surgical Nursing, Critical Care Nursing, Pediatric Nursing, Obstetric and Gynecological Nursing, Nursing Education, and Psychiatric Nursing and Mental Health. The credit hour system is the fundamental basis of both undergraduate and graduate programs. This framework guarantees a thorough evaluation of academic outcomes by providing an organized structure for tracking academic progress and conducting analyses.

Participants and sample size calculation

The researchers used the Epi Info 7 program to calculate the sample size. The calculations were based on specific parameters such as a population size of 9886 students for the academic year 2022–2023, an expected frequency of 50%, a maximum margin of error of 5%, and a confidence coefficient of 99.9%. Based on these parameters, the program indicated that a minimum sample size of 976 students was required. As a result, the researchers recruited a convenient sample of 1010 nursing students from different academic levels during the 2022–2023 academic year [ 19 ]. This sample size was larger than the minimum required, which could help to increase the accuracy and reliability of the study results. Participation in the study required enrollment in a nursing program and voluntary agreement to take part. The exclusion criteria included individuals with mental illnesses based on their response and those who failed to complete the questionnaires.

socio-demographic data that include students’ age, sex, educational level, hours of sleep at night, hours spent studying, and GPA from the previous semester.

Tool two: the perceived stress scale (PSS)

It was initially created by Sheu et al. (1997) to gauge the level and nature of stress perceived by nursing students attending Taiwanese universities [ 20 ]. It comprises 29 items rated on a 5-point Likert scale, where (0 = never, 1 = rarely, 2 = sometimes, 3 = reasonably often, and 4 = very often), with a total score ranging from 0 to 116. The cut-off points of levels of perceived stress scale according to score percentage were low < 33.33%, moderate 33.33–66.66%, and high more than 66.66%. Higher scores indicate higher stress levels. The items are categorized into six subscales reflecting different sources of stress. The first subscale assesses “stress stemming from lack of professional knowledge and skills” and includes 3 items. The second subscale evaluates “stress from caring for patients” with 8 items. The third subscale measures “stress from assignments and workload” with 5 items. The fourth subscale focuses on “stress from interactions with teachers and nursing staff” with 6 items. The fifth subscale gauges “stress from the clinical environment” with 3 items. The sixth subscale addresses “stress from peers and daily life” with 4 items. El-Ashry et al. (2022) reported an excellent internal consistency reliability of 0.83 [ 21 ]. Two bilingual translators translated the English version of the scale into Arabic and then back-translated it into English by two other independent translators to verify its accuracy. The suitability of the translated version was confirmed through a confirmatory factor analysis (CFA), which yielded goodness-of-fit indices such as a comparative fit index (CFI) of 0.712, a Tucker-Lewis index (TLI) of 0.812, and a root mean square error of approximation (RMSEA) of 0.100.

Tool three: revised study process questionnaire (R-SPQ-2 F)

It was developed by Biggs et al. (2001). It examines deep and surface learning approaches using only 20 questions; each subscale contains 10 questions [ 22 ]. On a 5-point Likert scale ranging from 0 (never or only rarely true of me) to 4 (always or almost always accurate of me). The total score ranged from 0 to 80, with a higher score reflecting more deep or surface learning approaches. The cut-off points of levels of revised study process questionnaire according to score percentage were low < 33%, moderate 33–66%, and high more than 66%. Biggs et al. (2001) found that Cronbach alpha value was 0.73 for deep learning approach and 0.64 for the surface learning approach, which was considered acceptable. Two translators fluent in English and Arabic initially translated a scale from English to Arabic. To ensure the accuracy of the translation, they translated it back into English. The translated version’s appropriateness was evaluated using a confirmatory factor analysis (CFA). The CFA produced several goodness-of-fit indices, including a Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100. Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100.

Ethical considerations

The Alexandria University College of Nursing’s Research Ethics Committee provided ethical permission before the study’s implementation. Furthermore, pertinent authorities acquired ethical approval at participating nursing institutions. The vice deans of the participating institutions provided written informed consent attesting to institutional support and authority. By giving written informed consent, participants confirmed they were taking part voluntarily. Strict protocols were followed to protect participants’ privacy during the whole investigation. The obtained personal data was kept private and available only to the study team. Ensuring participants’ privacy and anonymity was of utmost importance.

Tools validity

The researchers created tool one after reviewing pertinent literature. Two bilingual translators independently translated the English version into Arabic to evaluate the applicability of the academic stress and learning approach tools for Arabic-speaking populations. To assure accuracy, two additional impartial translators back-translated the translation into English. They were also assessed by a five-person jury of professionals from the education and psychiatric nursing departments. The scales were found to have sufficiently evaluated the intended structures by the jury.

Pilot study

A preliminary investigation involved 100 nursing student applicants, distinct from the final sample, to gauge the efficacy, clarity, and potential obstacles in utilizing the research instruments. The pilot findings indicated that the instruments were accurate, comprehensible, and suitable for the target demographic. Additionally, Cronbach’s Alpha was utilized to further assess the instruments’ reliability, demonstrating internal solid consistency for both the learning approaches and academic stress tools, with values of 0.91 and 0.85, respectively.

Data collection

The researchers convened with each qualified student in a relaxed, unoccupied classroom in their respective college settings. Following a briefing on the study’s objectives, the students filled out the datasheet. The interviews typically lasted 15 to 20 min.

Data analysis

The data collected were analyzed using IBM SPSS software version 26.0. Following data entry, a thorough examination and verification were undertaken to ensure accuracy. The normality of quantitative data distributions was assessed using Kolmogorov-Smirnov tests. Cronbach’s Alpha was employed to evaluate the reliability and internal consistency of the study instruments. Descriptive statistics, including means (M), standard deviations (SD), and frequencies/percentages, were computed to summarize academic stress and learning approaches for categorical data. Student’s t-tests compared scores between two groups for normally distributed variables, while One-way ANOVA compared scores across more than two categories of a categorical variable. Pearson’s correlation coefficient determined the strength and direction of associations between customarily distributed quantitative variables. Hierarchical regression analysis identified the primary independent factors influencing learning approaches. Statistical significance was determined at the 5% (p < 0.05).

Table  1 presents socio-demographic data for a group of 1010 nursing students. The age distribution shows that 38.8% of the students were between 18 and 21 years old, 32.9% were between 21 and 24 years old, and 28.3% were between 24 and 28 years old, with an average age of approximately 22.79. Regarding gender, most of the students were female (77%), while 23% were male. The students were distributed across different educational years, a majority of 34.4% in the second year, followed by 29.4% in the fourth year. The students’ hours spent studying were found to be approximately two-thirds (67%) of the students who studied between 3 and 6 h. Similarly, sleep patterns differ among the students; more than three-quarters (77.3%) of students sleep between 5- to more than 7 h, and only 2.4% sleep less than 2 h per night. Finally, the student’s Grade Point Average (GPA) from the previous semester was also provided. 21% of the students had a GPA between 2 and 2.5, 40.9% had a GPA between 2.5 and 3, and 38.1% had a GPA between 3 and 3.5.

Figure  1 provides the learning approach level among nursing students. In terms of learning approach, most students (55.0%) exhibited a moderate level of deep learning approach, followed by 25.9% with a high level and 19.1% with a low level. The surface learning approach was more prevalent, with 47.8% of students showing a moderate level, 41.7% showing a low level, and only 10.5% exhibiting a high level.

Nursing students? levels of learning approach (N=1010)

Figure  2 provides the types of academic stress levels among nursing students. Among nursing students, various stressors significantly impact their academic experiences. Foremost among these stressors are the pressure and demands associated with academic assignments and workload, with 30.8% of students attributing their high stress levels to these factors. Challenges within the clinical environment are closely behind, contributing significantly to high stress levels among 25.7% of nursing students. Interactions with peers and daily life stressors also weigh heavily on students, ranking third among sources of high stress, with 21.5% of students citing this as a significant factor. Similarly, interaction with teachers and nursing staff closely follow, contributing to high-stress levels for 20.3% of nursing students. While still significant, stress from taking care of patients ranks slightly lower, with 16.7% of students reporting it as a significant factor contributing to their academic stress. At the lowest end of the ranking, but still notable, is stress from a perceived lack of professional knowledge and skills, with 15.9% of students experiencing high stress in this area.

Nursing students? levels of academic stress subtypes (N=1010)

Figure  3 provides the total levels of academic stress among nursing students. The majority of students experienced moderate academic stress (56.3%), followed by those experiencing low academic stress (29.9%), and a minority experienced high academic stress (13.8%).

Nursing students? levels of total academic stress (N=1010)

Table  2 displays the correlation between academic stress subscales and deep and surface learning approaches among 1010 nursing students. All stress subscales exhibited a negative correlation regarding the deep learning approach, indicating that the inclination toward deep learning decreases with increasing stress levels. The most significant negative correlation was observed with stress stemming from the lack of professional knowledge and skills (r=-0.392, p < 0.001), followed by stress from the clinical environment (r=-0.109, p = 0.001), stress from assignments and workload (r=-0.103, p = 0.001), stress from peers and daily life (r=-0.095, p = 0.002), and stress from patient care responsibilities (r=-0.093, p = 0.003). The weakest negative correlation was found with stress from interactions with teachers and nursing staff (r=-0.083, p = 0.009). Conversely, concerning the surface learning approach, all stress subscales displayed a positive correlation, indicating that heightened stress levels corresponded with an increased tendency toward superficial learning. The most substantial positive correlation was observed with stress related to the lack of professional knowledge and skills (r = 0.365, p < 0.001), followed by stress from patient care responsibilities (r = 0.334, p < 0.001), overall stress (r = 0.355, p < 0.001), stress from interactions with teachers and nursing staff (r = 0.262, p < 0.001), stress from assignments and workload (r = 0.262, p < 0.001), and stress from the clinical environment (r = 0.254, p < 0.001). The weakest positive correlation was noted with stress stemming from peers and daily life (r = 0.186, p < 0.001).

Table  3 outlines the association between the socio-demographic characteristics of nursing students and their deep and surface learning approaches. Concerning age, statistically significant differences were observed in deep and surface learning approaches (F = 3.661, p = 0.003 and F = 7.983, p < 0.001, respectively). Gender also demonstrated significant differences in deep and surface learning approaches (t = 3.290, p = 0.001 and t = 8.638, p < 0.001, respectively). Female students exhibited higher scores in the deep learning approach (31.59 ± 8.28) compared to male students (29.59 ± 7.73), while male students had higher scores in the surface learning approach (29.97 ± 7.36) compared to female students (24.90 ± 7.97). Educational level exhibited statistically significant differences in deep and surface learning approaches (F = 5.599, p = 0.001 and F = 17.284, p < 0.001, respectively). Both deep and surface learning approach scores increased with higher educational levels. The duration of study hours demonstrated significant differences only in the surface learning approach (F = 3.550, p = 0.014), with scores increasing as study hours increased. However, no significant difference was observed in the deep learning approach (F = 0.861, p = 0.461). Hours of sleep per night and GPA from the previous semester did not exhibit statistically significant differences in deep or surface learning approaches.

Table  4 presents a multivariate linear regression analysis examining the factors influencing the learning approach among 1110 nursing students. The deep learning approach was positively influenced by age, gender (being female), educational year level, and stress from teachers and nursing staff, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by stress from a lack of professional knowledge and skills. The other factors do not significantly influence the deep learning approach. On the other hand, the surface learning approach was positively influenced by gender (being female), educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by gender (being male). The other factors do not significantly influence the surface learning approach. The adjusted R-squared values indicated that the variables in the model explain 17.8% of the variance in the deep learning approach and 25.5% in the surface learning approach. Both models were statistically significant (p < 0.001).

Nursing students’ academic stress and learning approaches are essential to planning for effective and efficient learning. Nursing education also aims to develop knowledgeable and competent students with problem-solving and critical-thinking skills.

The study’s findings highlight the significant presence of stress among nursing students, with a majority experiencing moderate to severe levels of academic stress. This aligns with previous research indicating that academic stress is prevalent among nursing students. For instance, Zheng et al. (2022) observed moderated stress levels in nursing students during clinical placements [ 23 ], while El-Ashry et al. (2022) found that nearly all first-year nursing students in Egypt experienced severe academic stress [ 21 ]. Conversely, Ali and El-Sherbini (2018) reported that over three-quarters of nursing students faced high academic stress. The complexity of the nursing program likely contributes to these stress levels [ 24 ].

The current study revealed that nursing students identified the highest sources of academic stress as workload from assignments and the stress of caring for patients. This aligns with Banu et al.‘s (2015) findings, where academic demands, assignments, examinations, high workload, and combining clinical work with patient interaction were cited as everyday stressors [ 25 ]. Additionally, Anaman-Torgbor et al. (2021) identified lectures, assignments, and examinations as predictors of academic stress through logistic regression analysis. These stressors may stem from nursing programs emphasizing the development of highly qualified graduates who acquire knowledge, values, and skills through classroom and clinical experiences [ 26 ].

The results regarding learning approaches indicate that most nursing students predominantly employed the deep learning approach. Despite acknowledging a surface learning approach among the participants in the present study, the prevalence of deep learning was higher. This inclination toward the deep learning approach is anticipated in nursing students due to their engagement with advanced courses, requiring retention, integration, and transfer of information at elevated levels. The deep learning approach correlates with a gratifying learning experience and contributes to higher academic achievements [ 3 ]. Moreover, the nursing program’s emphasis on active learning strategies fosters critical thinking, problem-solving, and decision-making skills. These findings align with Mahmoud et al.‘s (2019) study, reporting a significant presence (83.31%) of the deep learning approach among undergraduate nursing students at King Khalid University’s Faculty of Nursing [ 27 ]. Additionally, Mohamed &Morsi (2019) found that most nursing students at Benha University’s Faculty of Nursing embraced the deep learning approach (65.4%) compared to the surface learning approach [ 28 ].

The study observed a negative correlation between the deep learning approach and the overall mean stress score, contrasting with a positive correlation between surface learning approaches and overall stress levels. Elevated academic stress levels may diminish motivation and engagement in the learning process, potentially leading students to feel overwhelmed, disinterested, or burned out, prompting a shift toward a surface learning approach. This finding resonates with previous research indicating that nursing students who actively seek positive academic support strategies during academic stress have better prospects for success than those who do not [ 29 ]. Nebhinani et al. (2020) identified interface concerns and academic workload as significant stress-related factors. Notably, only an interest in nursing demonstrated a significant association with stress levels, with participants interested in nursing primarily employing adaptive coping strategies compared to non-interested students.

The current research reveals a statistically significant inverse relationship between different dimensions of academic stress and adopting the deep learning approach. The most substantial negative correlation was observed with stress arising from a lack of professional knowledge and skills, succeeded by stress associated with the clinical environment, assignments, and workload. Nursing students encounter diverse stressors, including delivering patient care, handling assignments and workloads, navigating challenging interactions with staff and faculty, perceived inadequacies in clinical proficiency, and facing examinations [ 30 ].

In the current study, the multivariate linear regression analysis reveals that various factors positively influence the deep learning approach, including age, female gender, educational year level, and stress from teachers and nursing staff. In contrast, stress from a lack of professional knowledge and skills exert a negative influence. Conversely, the surface learning approach is positively influenced by female gender, educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, but negatively affected by male gender. The models explain 17.8% and 25.5% of the variance in the deep and surface learning approaches, respectively, and both are statistically significant. These findings underscore the intricate interplay of demographic and stress-related factors in shaping nursing students’ learning approaches. High workloads and patient care responsibilities may compel students to prioritize completing tasks over deep comprehension. This pressure could lead to a surface learning approach as students focus on meeting immediate demands rather than engaging deeply with course material. This observation aligns with the findings of Alsayed et al. (2021), who identified age, gender, and study year as significant factors influencing students’ learning approaches.

Deep learners often demonstrate better self-regulation skills, such as effective time management, goal setting, and seeking support when needed. These skills can help manage academic stress and maintain a balanced learning approach. These are supported by studies that studied the effect of coping strategies on stress levels [ 6 , 31 , 32 ]. On the contrary, Pacheco-Castillo et al. study (2021) found a strong significant relationship between academic stressors and students’ level of performance. That study also proved that the more academic stress a student faces, the lower their academic achievement.

Strengths and limitations of the study

This study has lots of advantages. It provides insightful information about the educational experiences of Egyptian nursing students, a demographic that has yet to receive much research. The study’s limited generalizability to other people or nations stems from its concentration on this particular group. This might be addressed in future studies by using a more varied sample. Another drawback is the dependence on self-reported metrics, which may contain biases and mistakes. Although the cross-sectional design offers a moment-in-time view of the problem, it cannot determine causation or evaluate changes over time. To address this, longitudinal research may be carried out.

Notwithstanding these drawbacks, the study substantially contributes to the expanding knowledge of academic stress and nursing students’ learning styles. Additional research is needed to determine teaching strategies that improve deep-learning approaches among nursing students. A qualitative study is required to analyze learning approaches and factors that may influence nursing students’ selection of learning approaches.

According to the present study’s findings, nursing students encounter considerable academic stress, primarily stemming from heavy assignments and workload, as well as interactions with teachers and nursing staff. Additionally, it was observed that students who experience lower levels of academic stress typically adopt a deep learning approach, whereas those facing higher stress levels tend to resort to a surface learning approach. Demographic factors such as age, gender, and educational level influence nursing students’ choice of learning approach. Specifically, female students are more inclined towards deep learning, whereas male students prefer surface learning. Moreover, deep and surface learning approach scores show an upward trend with increasing educational levels and study hours. Academic stress emerges as a significant determinant shaping the adoption of learning approaches among nursing students.

Implications in nursing practice

Nursing programs should consider integrating stress management techniques into their curriculum. Providing students with resources and skills to cope with academic stress can improve their well-being and academic performance. Educators can incorporate teaching strategies that promote deep learning approaches, such as problem-based learning, critical thinking exercises, and active learning methods. These approaches help students engage more deeply with course material and reduce reliance on surface learning techniques. Recognizing the gender differences in learning approaches, nursing programs can offer gender-specific support services and resources. For example, providing targeted workshops or counseling services that address male and female nursing students’ unique stressors and learning needs. Implementing mentorship programs and peer support groups can create a supportive environment where students can share experiences, seek advice, and receive encouragement from their peers and faculty members. Encouraging students to reflect on their learning processes and identify effective study strategies can help them develop metacognitive skills and become more self-directed learners. Faculty members can facilitate this process by incorporating reflective exercises into the curriculum. Nursing faculty and staff should receive training on recognizing signs of academic stress among students and providing appropriate support and resources. Additionally, professional development opportunities can help educators stay updated on evidence-based teaching strategies and practical interventions for addressing student stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions imposed by the institutional review board to protect participant confidentiality, but are available from the corresponding author on reasonable request.

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Acknowledgements

Our sincere thanks go to all the nursing students in the study. We also want to thank Dr/ Rasha Badry for their statistical analysis help and contribution to this study.

The research was not funded by public, commercial, or non-profit organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Nursing Education, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Rawhia Salah Dogham & Heba Fakieh Mansy Ali

Critical Care & Emergency Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Nermine M. Elcokany

Obstetrics and Gynecology Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Asmaa Saber Ghaly

Faculty of Nursing, Beni-Suef University, Beni-Suef, Egypt

Mohamed Mahmoud Seweid

Psychiatric and Mental Health Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Ayman Mohamed El-Ashry

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Contributions

Ayman M. El-Ashry & Rawhia S. Dogham: conceptualization, preparation, and data collection; methodology; investigation; formal analysis; data analysis; writing-original draft; writing-manuscript; and editing. Heba F. Mansy Ali & Asmaa S. Ghaly: conceptualization, preparation, methodology, investigation, writing-original draft, writing-review, and editing. Nermine M. Elcokany & Mohamed M. Seweid: Methodology, investigation, formal analysis, data collection, writing-manuscript & editing. All authors reviewed the manuscript and accept for publication.

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Correspondence to Ayman Mohamed El-Ashry .

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The research adhered to the guidelines and regulations outlined in the Declaration of Helsinki (DoH-Oct2008). The Faculty of Nursing’s Research Ethical Committee (REC) at Alexandria University approved data collection in this study (IRB00013620/95/9/2022). Participants were required to sign an informed written consent form, which included an explanation of the research and an assessment of their understanding.

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Dogham, R.S., Ali, H.F.M., Ghaly, A.S. et al. Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study. BMC Nurs 23 , 249 (2024). https://doi.org/10.1186/s12912-024-01885-1

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Received : 31 January 2024

Accepted : 21 March 2024

Published : 17 April 2024

DOI : https://doi.org/10.1186/s12912-024-01885-1

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