quantitative research importance in nursing

Nursing Research (NURS 3321/4325/5366)

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• Understand What Quantitative Research Is
• Understand What Qualitative Research Is
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What is Quantitative Research?

Quantitative methodology is the dominant research framework in the social sciences. it refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . quantitative research gathers a range of numeric data. some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘on a scale from 1 to 10, how depressed did you feel last week’). the collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the usa has a higher gross domestic product than spain). quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies..

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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• Volume 18, Issue 3
• Validity and reliability in quantitative studies
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• Roberta Heale 1 ,
• Alison Twycross 2
• 1 School of Nursing, Laurentian University , Sudbury, Ontario , Canada
• 2 Faculty of Health and Social Care , London South Bank University , London , UK
• Correspondence to : Dr Roberta Heale, School of Nursing, Laurentian University, Ramsey Lake Road, Sudbury, Ontario, Canada P3E2C6; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2015-102129

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Evidence-based practice includes, in part, implementation of the findings of well-conducted quality research studies. So being able to critique quantitative research is an important skill for nurses. Consideration must be given not only to the results of the study but also the rigour of the research. Rigour refers to the extent to which the researchers worked to enhance the quality of the studies. In quantitative research, this is achieved through measurement of the validity and reliability. 1

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Types of validity

The first category is content validity . This category looks at whether the instrument adequately covers all the content that it should with respect to the variable. In other words, does the instrument cover the entire domain related to the variable, or construct it was designed to measure? In an undergraduate nursing course with instruction about public health, an examination with content validity would cover all the content in the course with greater emphasis on the topics that had received greater coverage or more depth. A subset of content validity is face validity , where experts are asked their opinion about whether an instrument measures the concept intended.

Construct validity refers to whether you can draw inferences about test scores related to the concept being studied. For example, if a person has a high score on a survey that measures anxiety, does this person truly have a high degree of anxiety? In another example, a test of knowledge of medications that requires dosage calculations may instead be testing maths knowledge.

There are three types of evidence that can be used to demonstrate a research instrument has construct validity:

Homogeneity—meaning that the instrument measures one construct.

Convergence—this occurs when the instrument measures concepts similar to that of other instruments. Although if there are no similar instruments available this will not be possible to do.

Theory evidence—this is evident when behaviour is similar to theoretical propositions of the construct measured in the instrument. For example, when an instrument measures anxiety, one would expect to see that participants who score high on the instrument for anxiety also demonstrate symptoms of anxiety in their day-to-day lives. 2

The final measure of validity is criterion validity . A criterion is any other instrument that measures the same variable. Correlations can be conducted to determine the extent to which the different instruments measure the same variable. Criterion validity is measured in three ways:

Convergent validity—shows that an instrument is highly correlated with instruments measuring similar variables.

Divergent validity—shows that an instrument is poorly correlated to instruments that measure different variables. In this case, for example, there should be a low correlation between an instrument that measures motivation and one that measures self-efficacy.

Predictive validity—means that the instrument should have high correlations with future criterions. 2 For example, a score of high self-efficacy related to performing a task should predict the likelihood a participant completing the task.

Reliability

Reliability relates to the consistency of a measure. A participant completing an instrument meant to measure motivation should have approximately the same responses each time the test is completed. Although it is not possible to give an exact calculation of reliability, an estimate of reliability can be achieved through different measures. The three attributes of reliability are outlined in table 2 . How each attribute is tested for is described below.

Attributes of reliability

Homogeneity (internal consistency) is assessed using item-to-total correlation, split-half reliability, Kuder-Richardson coefficient and Cronbach's α. In split-half reliability, the results of a test, or instrument, are divided in half. Correlations are calculated comparing both halves. Strong correlations indicate high reliability, while weak correlations indicate the instrument may not be reliable. The Kuder-Richardson test is a more complicated version of the split-half test. In this process the average of all possible split half combinations is determined and a correlation between 0–1 is generated. This test is more accurate than the split-half test, but can only be completed on questions with two answers (eg, yes or no, 0 or 1). 3

Cronbach's α is the most commonly used test to determine the internal consistency of an instrument. In this test, the average of all correlations in every combination of split-halves is determined. Instruments with questions that have more than two responses can be used in this test. The Cronbach's α result is a number between 0 and 1. An acceptable reliability score is one that is 0.7 and higher. 1 , 3

Stability is tested using test–retest and parallel or alternate-form reliability testing. Test–retest reliability is assessed when an instrument is given to the same participants more than once under similar circumstances. A statistical comparison is made between participant's test scores for each of the times they have completed it. This provides an indication of the reliability of the instrument. Parallel-form reliability (or alternate-form reliability) is similar to test–retest reliability except that a different form of the original instrument is given to participants in subsequent tests. The domain, or concepts being tested are the same in both versions of the instrument but the wording of items is different. 2 For an instrument to demonstrate stability there should be a high correlation between the scores each time a participant completes the test. Generally speaking, a correlation coefficient of less than 0.3 signifies a weak correlation, 0.3–0.5 is moderate and greater than 0.5 is strong. 4

Equivalence is assessed through inter-rater reliability. This test includes a process for qualitatively determining the level of agreement between two or more observers. A good example of the process used in assessing inter-rater reliability is the scores of judges for a skating competition. The level of consistency across all judges in the scores given to skating participants is the measure of inter-rater reliability. An example in research is when researchers are asked to give a score for the relevancy of each item on an instrument. Consistency in their scores relates to the level of inter-rater reliability of the instrument.

Determining how rigorously the issues of reliability and validity have been addressed in a study is an essential component in the critique of research as well as influencing the decision about whether to implement of the study findings into nursing practice. In quantitative studies, rigour is determined through an evaluation of the validity and reliability of the tools or instruments utilised in the study. A good quality research study will provide evidence of how all these factors have been addressed. This will help you to assess the validity and reliability of the research and help you decide whether or not you should apply the findings in your area of clinical practice.

• Lobiondo-Wood G ,
• Shuttleworth M
• ↵ Laerd Statistics . Determining the correlation coefficient . 2013 . https://statistics.laerd.com/premium/pc/pearson-correlation-in-spss-8.php

Twitter Follow Roberta Heale at @robertaheale and Alison Twycross at @alitwy

Competing interests None declared.

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Increasing Quantitative Literacy in Nursing: A Joint Nursing-Statistician Perspective

Krista schroeder.

a Assistant Professor of Nursing, Temple University College of Public Health, 3307 North Broad Street, Philadelphia PA 19140, USA

Levent DUMENCI

b Professor of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia PA 19122, USA

David B. SARWER

c Associate Dean for Research, Director - Center for Obesity Research and Education, Temple University College of Public Health, Philadelphia PA 11940, USA

David C. WHEELER

d Associate Professor, Virginia Commonwealth University School of Medicine, Department of Biostatistics, Richmond VA 23298, USA

Matthew J. HAYAT

e Professor of Biostatistics, School of Public Health & Byrdine F. Lewis College of Nursing and Health Professions at Georgia State University, Atlanta GA 30302, USA

Strong quantitative literacy is necessary to fulfill nurses’ professional responsibilities across education levels, roles, and settings. Evidence-based practice and systems improvement are not possible if nurses do not understand the statistics employed in generating evidence. Statistics is the language of science and rigorous nursing science cannot exist without a clear understanding of statistical methods. Increasing availability and complexity of medical and public health data and a growing focus on population health necessitate increasingly sophisticated quantitative literacy in nursing practice, leadership, and science ( Hayat, Kim, Schwartz, & Jiroutek, 2021 ; Hayat, Schmiege, & Cook, 2014 ). Without strong quantitative knowledge, the nursing profession will lose opportunities to lead evidence-driven, population-focused efforts for health improvement.

Serious limitations in both knowledge and application of statistics have been documented in nursing pedagogy, scholarship, and research for decades ( Anthony, 1996 ; Gaskin & Happell, 2014 ; Hayat, Higgins, Schwartz, & Staggs, 2015 ; Hayat et al., 2021 ; Hayat et al., 2014 ). Prior work calling for greater quantitative literacy in nursing has been solely or primarily statistician-led, leaving an opportunity and responsibility for nurses to contribute. Without the voice of nursing, efforts to improve quantitative literacy within the profession will lack nursing insight and perspective. In this commentary we provide guidance for nurses’ engagement with quantitative methods and offer suggestions to increase quantitative literacy in nursing across education levels, roles, and settings.

Be Aware of What You Do and Don’t Know

For nurses, awareness of one’s level of statistical knowledge can foster more effective communication with statisticians and consumers of nursing scholarship and avoid analytic errors ground in lack of statistical knowledge. Statistics is a discipline – not a toolbox; statistics is not simply about choosing the right analytic approach, but about a start-to-finish approach to project planning, data collection, appropriate analysis (including confirming underlying statistical assumptions and conducting sensitivity analyses), and accurately and comprehensively understanding and presenting results to a range of stakeholders with varying levels of statistical knowledge. Importantly, levels of required statistical knowledge varies by nursing role – BSN-prepared clinicians focus on evidence-based practice, MSN- and DNP-prepared nurse practitioners focuses on quality improvement and research translation to systems and practice, and PhD-prepared nurse scientists focus on creation of generalizable knowledge ( Hayat et al., 2014 ). Thus, while BSN-, MSN-, and DNP-prepared nurses may focus on quantitative literacy, PhD-prepared nurses may recognize larger gaps in their required knowledge and focus on gaining statistical expertise required to conduct high quality nursing research.

Know How to Find Statistical Help

Many nurses – particularly those working primarily in clinical practice, in a small organization, or in a setting without formal academic-practice partnerships – may be unsure how to find statistical assistance. Often, nurses are not taught how to collaborate with statisticians ( Hayat et al., 2015 ). A first step entails deciding from whom statistical help is needed. A graduate student in statistics, MS-prepared statistician, and PhD-prepared statistician will bring different expertise, but all may be appropriate collaborators depending on need. A nurse scientist conducting research to develop new knowledge likely requires different expertise than a hospital nursing unit manager planning a quality improvement project. Statistical collaborators are often available via academic institutions (schools of nursing but also schools of public health or medicine). Nursing-statistician collaboration typically necessitates deeper partnership than simply confirming which statistical test should be used, as statisticians can help with the entire project planning process ( Hayat et al., 2015 ). Nursing collaborators should be aware that statistical collaboration may entail cost such as hourly fees or coverage of salary/effort. Alternatively, statistical support services may be provided by a nursing school or college, such as via consulting labs, that do not require funding or provide short term consultation as needed.

When seeking a statistical collaborator, it is important to be aware that statisticians have different focuses and areas of expertise. Simply finding “a statistician” may be too broadly defined and thus an ineffective approach. For example, the statistician who helps with instrument development may not be the same statistician who helps plan a randomized controlled trial nor the same statistician who helps analyze large, multi-level data from an electronic medical record. Attention to statisticians’ areas of expertise is important to finding the right collaborator.

For PhD-prepared Nurses, Seek Challenging Learning Opportunities

Nurses should embrace challenge when learning about statistics. For PhD-prepared nurses, gaining statistical expertise through summer intensive or short courses, formal university coursework, or career development awards is often beneficial. Particularly for nurse scientists whose work entails advanced quantitative approaches, such additional training is likely a necessity. PhD-prepared nurses should also consider challenging themselves to learn analytic tools beyond menu-driven commercial software (e.g., SPSS) ( Hayat et al., 2014 ). Code-driven and free-of-charge statistical computing tools allow for replicability, transparency, and documentation of analytic work. R is a tool that may be of particular interest, given its open structure, large and active user-driven community, and availability of numerous flexible user-provided packages. R is also a useful tool for working with spatial data, which is relevant for nurses who are interested in social or environmental determinants of health, such as neighborhood poverty or greenspace access. Thus, while learning tools beyond menu-driven software may initially feel challenging, doing so can contribute meaningfully to one’s statistical skillset.

Focus on Quantitative Literacy Rather Than Statistical Expertise

For most nurses, the goal should be quantitative literacy ( Hayat et al., 2015 ; Hayat et al., 2014 ). Nurses bring important content expertise coupled with a wealth of relevant clinical experience that can bring data analytic strategies alive for a multidisciplinary audience in a presentation or manuscript. A statistician would not take one or two courses in “nursing” and expect to care for patients. Similarly, a nurse should not take one or two biostatistics courses and aim to plan, execute, and interpret one’s own data analysis. Nurses can approach statistical collaboration with respect for the content knowledge they bring as a nurse, rather than an insecurity about the statistical expertise they lack. Through effective collaboration and a focus on strong quantitative literacy, nurses can dispel negative stereotypes about nurses not being “good at” statistics.

Advance Efforts to Increase Quantitative Literacy in Nursing

Given documented gaps in nursing knowledge, strategies for increasing quantitative literacy must be considered. Quantitative and qualitative assessments of nurses’ statistical learning needs could inform translation of the robust statistics education literature into nursing training. A nursing-focused addendum to the Guidelines for Assessment and Instruction in Statistics Education could inform nursing education, as could increased guidance on nursing education statistical competencies from accrediting bodies. In addition, formalizing processes and enforcing rigorous guidelines for manuscripts’ statistical methods sections in nursing journals could increase rigor within the nursing literature ( Hayat et al., 2015 ; Hayat et al., 2021 ). Further, efforts to increase the pipeline of individuals well-prepared to serve as statisticians in schools of nursing could benefit academic nursing. Faculty who hold joint appointment in nursing and statistics and have formal education and training in both fields may be optimal, and they would have both scholarly authority in nursing and statistics and the ability to communicate effectively with nursing students. Joint graduate degree programs or minors – approaches used by many other disciplines at large research universities – can increase the pipeline of statisticians prepared in this manner and well-suited to serve in schools of nursing.

Conclusions

There is a ripe opportunity for increased nursing leadership to improve quantitative literacy in nursing. An active collaboration of nursing and statistical thought leaders can chart the path forward. When armed with appropriate statistical knowledge, nurses can play a unique role in using data to promote health and prevent disease among individuals, communities, and populations.

This research was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K23 HD101554; PI: Schroeder) of the National Institutes of Health (NIH). Dr. Sarwer’s work was supported by grant funding from the National Institutes of Health (National Institute for Diabetes, Digestive, and Kidney Disease R01 DK108628 and National Institute of Dental and Craniofacial Research R01 DE026603) as well as PA CURE Funds from the Commonwealth of Pennsylvania. The content is solely the responsibility of the authors and does not necessarily represent the views of the funder. The funder had no role in the development or preparation of this manuscript.

Conflict of Interest Statement: Krista Schroeder, Levent Dumenci, David C. Wheeler, and Matthew J. Hayat declare that they have no conflict of interest. David Sarwer discloses consulting relationships with Ethicon and NovoNordisk.

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Measurement in Nursing Research

Curtis, Alexa Colgrove PhD, MPH, FNP, PMHNP; Keeler, Courtney PhD

Alexa Colgrove Curtis is assistant dean and professor of graduate nursing and director of the MPH–DNP dual degree program and Courtney Keeler is an associate professor, both at the University of San Francisco School of Nursing and Health Professions. Contact author: Alexa Colgrove Curtis, [email protected] . Nursing Research, Step by Step is coordinated by Bernadette Capili, PhD, NP-C: [email protected] . The authors have disclosed no potential conflicts of interest, financial or otherwise. A podcast with the authors is available at www.ajnonline.com .

Editor's note: This is the fourth article in a series on clinical research by nurses. The series is designed to give nurses the knowledge and skills they need to participate in research, step by step. Each column will present the concepts that underpin evidence-based practice—from research design to data interpretation. The articles will be accompanied by a podcast offering more insight and context from the authors. To see all the articles in the series, go to https://links.lww.com/AJN/A204 .

Quantitative research examines associations between research variables as measured through numerical analysis, where study effects (outcomes) are analyzed using statistical techniques. Such techniques include descriptive statistics (for example, sample mean and standard deviation) and inferential statistics, which uses the laws of probability to evaluate for statistically significant differences between sample groups (for example, t test, ANOVA, and regression analysis). Qualitative research explores research questions through an analysis of nonnumerical data sources (for example, text sources collected directly or indirectly by the researcher) and reports outcomes as themes or concepts that describe a phenomenon or experience.

As described in the first installment of this series, “a common goal of clinical research is to understand health and illness and to discover novel methods to detect, diagnose, treat, and prevent disease”; with this in mind, research questions must “focus on clear approaches to measuring or quantifying change or outcome,” the research outcome being the “planned measure to determine the effect of an intervention on the population under study.” 1

In this article, we explore measurement in quantitative research. We will also consider the concepts of validity and reliability as they relate to quantitative research measurement. Qualitative analysis will be considered separately in a future article in this series, as this methodology does not typically use a prescribed mechanism for measurement of research variables.

DEFINING THE VARIABLE OF INTEREST

Measurement in research begins with defining the variables of interest. Often, researchers are interested in exploring how variation in one factor or phenomenon influences variation in another. The dependent variables (outcome variables) in a study reflect the primary phenomenon of interest and the independent variables (or explanatory variables) reflect the factors that are hypothesized to have an impact on the primary phenomenon of interest (the dependent variable). 2 For example, a researcher might rightly hypothesize that body mass index (BMI) influences blood pressure, further hypothesizing that increases in BMI are associated with increases in blood pressure. In a study testing this hypothesis, blood pressure is the dependent variable and BMI is an independent variable.

In identifying the variables of interest in a study, researchers are likely to have ideas of concepts they would like to explore. For instance, among other things the researcher is interested in in the above example is weight. A conceptual definition of a research variable provides a general theoretical understanding of that variable; regarding weight, a person might be considered “thin” or “overweight.” Nevertheless, in moving from theory to practice, the researcher must consider how to operationalize this theoretical definition—that is, the researcher needs to select specific mechanisms for measuring the proscribed variables conceptualized in the study. Thus, an operational definition provides a measurable definition of a variable. Continuing with the above example, BMI would be a means of operationalizing the weight variable, where a person with a BMI of 25 or above is categorized by the Centers for Disease Control and Prevention as overweight. 3 In operationalizing variables, first look in existing evidence-based literature, practices, and professional guidelines. For instance, the researcher might consider measuring depression using the validated and widely utilized Patient Health Questionnaire-9 (PHQ-9) depression assessment scale or assessing longitudinal hyperglycemic risk by using the accepted measurement of glycated hemoglobin (HbA 1c ) level.

MEASUREMENT TOOLS

Researchers rely on measurement tools and instruments to create quantitative assessments of the variables studied. In some cases, direct measurements can be made using biometric measurement instruments to collect physiologic data such as weight, blood pressure, oxygen saturation level, and serum laboratory values. These biometric assessments are considered direct measures . 4 To quantify more abstract concepts, such as mood states, attitudes, and theoretical concepts like “caring,” researchers must consider less obvious proxy measures. Proxy measures constructed to quantify more abstract concepts are considered indirect measures . 4 For instance, Hughes developed an instrument to assess peer group caring during informal peer interactions among undergraduate nursing students. 5 While unable to directly measure the theoretical concept of caring, Hughes was able to construct an indirect proxy assessment using a survey tool.

Indirect, and even direct, measures can be operationalized in several ways. For instance, a researcher may consider operationalizing the concept of depression using the PHQ-9 depression assessment scale, using the Center for Epidemiological Studies Depression scale, or by applying Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition , diagnostic criteria. The study findings may be affected by how a variable is operationalized and which measurement tools are utilized; therefore, researchers should give serious thought to study objectives, sample/target populations, and other important considerations when operationalizing a variable. More specifics on measurement formats and methods of administration will be explored in the next installment of this series.

LEVELS OF MEASUREMENT

Levels of measurement describe the structure of a variable (see Table 1 ).

Nominal level . The lowest form of measurement, the nominal level groups data into distinct and exclusive categories that cannot be rank ordered. 2 Gender identity, race/ethnicity, occupation, geographic location, and clinical diagnoses are all examples of categories that contain nominal level data. This type of variable may also be referred to as a categorical variable. 6

Ordinal level data can also be categorized into distinct and exclusive groups; however, unlike nominal data, ordinal data can be ordered by rank. Likert-type scale variables reflect a classic example of ordinal level data, where responses can be rank ordered by “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree.” The 0-to-10-point pain scale is another example of the ordinal level of measurement. Using this scale, a patient provides a subjective determination of the experience of pain, where 0 reflects no pain and 10 reflects the highest pain threshold. As with all ordinal data, the precise quantitative distance between the descriptor data points is impossible to assess—the differences between a pain determination of 3 and one of 4 and a pain determination of 7 and one of 8 cannot be precisely calculated. Further, the distance between each pain level (for example, jumping from a pain level of 3 to 4 or from a pain level of 7 to 8) is not assumed to be incrementally or objectively equal. 2 Despite these drawbacks, ordinal level data are frequently translated into a numerical expression so they can be analyzed as interval or ratio data. For example, a Likert scale can be translated into a scale ranging from “strongly disagree = 1” to “strongly agree = 5,” allowing for the calculation of a numerical mean satisfaction score.

Interval level data reflect a chronological sequencing of data points with distances that are assumed to be quantifiable and equal in magnitude, such as ambient temperature. As with ambient temperature measured in degrees Fahrenheit, the magnitude of the chronological difference between each data point is assumed to be equal along a continuum of continuous values. Of note, interval data do not include a true and meaningful zero, the total absence of the characteristic being measured. 2 For example, there is no such thing as the absence of temperature.

Ratio level data provide the final and most robust level of measurement. Ratio level data are measured continuously, with equal spacing between intervals and with a true zero. Examples include height, weight, heart rate, and serum laboratory values. A zero value is interpreted as the absence of the characteristic. Once again, researchers should be cautious in defining how a variable is operationalized because the level of measurement will influence the types of statistical analyses that can be performed in the evaluation of study outcomes. Interval and ratio levels of measurement result in the most robust statistical analyses and research results. Statistical analysis techniques will be discussed in more detail later in this series.

MEASUREMENT ERROR

For variables to provide a meaningful and appropriate representation of the underlying concept being measured, data measurement needs to be accurate and precise. Measurement error reflects the difference between the measured and true value of the underlying concept. The value of an individual measurement can be described as follows 7 :

Chance error (or random error) changes from measurement to measurement, while bias (or systematic error) influences “all measurements the same way, pushing them in the same direction.” 7 Chance errors are individually unpredictable and inconsistent and in the long run should cancel each other out. If there is no bias in one's measurement, the individual and exact values should ultimately be equal. Bias, however, is inherent in all models and causes a systematic deviation from the true, underlying value.

Weight offers an excellent example of measurement error. Suppose some patients are weighed in the morning, some in the afternoon; some wear coats while others do not; some have eaten while others have fasted, and so forth. This variation reflects random error—we'd expect this positive and negative, over- and underestimation, to average out once enough patients have been sampled. Further, suppose the scale is incorrectly calibrated, such that it reports that every person weighs five pounds more than her or his actual weight. This result reflects a positive bias in the estimates and will not be corrected no matter how many patients are sampled.

The potential for bias resulting from measurement error falls broadly under the category of information bias —are researchers measuring what they think they are measuring? Information bias is present if the study data collected are somehow incorrect. 8 This can occur because of faulty measurement practices that systematically result in the under- or overvaluation of a measure, as described in the scale example above, or because of systematic misreporting by respondents. There are many forms of information bias, including recall bias, interviewer bias, and misclassification, as well as systematic differences in soliciting, recording, and interpreting information. 8 For instance, consider a study of adolescent sexual behavior in which adolescents are interviewed in the home with parents or guardians present. One might assume that adolescents in these circumstances would underreport the number of sexual partners they have had; as a result, one might expect this systematic underreporting to represent a downward bias in the data collected.

Other forms of bias exist, such as selection bias (if study participants are systematically different from the target population, or population of interest). Selection bias, for instance, does not necessarily affect the internal validity of the study (the ability to collect valid data) but may affect the external validity of the study (can researchers truly generalize findings to the population of interest?). These forms of bias will be described in further detail elsewhere in this series.

Measurement error is study and model specific; it comes in many forms and the type of error affects the level or form of bias. In interpreting results and designing research, researchers need to be aware of potential measurement error, do what they can to minimize bias, and provide a thorough assessment of bias in presenting the limitations of their work.

VALIDITY AND RELIABILITY

Validity refers to the degree to which a measurement accurately represents the underlying concept being measured. Basically, does the test operate as designed? Researchers need to consider the validity of use of the measurement instrument within the context of specific populations. For instance, in Hughes's study of caring among peer groups in undergraduate nursing populations, the author not only had to ensure that her instrument accurately measured caring among peer groups but also needed to verify that this measurement was accurate in nursing undergraduate populations. 5 In this instance, Hughes developed the survey explicitly with undergraduates in mind, making the second point easier to achieve.

Suppose, however, that a researcher wanted to use a version of the survey to gauge peer caring among nursing faculty. Would this be appropriate? Not without first assessing the validity of the survey within the new sample. The validity of an instrument can be assessed in several ways: by having the instrument reviewed by a content expert, comparing the instrument's results with those from an alternative assessment metric, assessing how well the instrument predicts current or future performance for the concept under consideration, and running a factor analysis (a statistical procedure that compares items or subscales within an instrument with each other and with the overall instrument outcome).

Reliability and validity go hand in hand. Reliability reflects the consistency of a measurement tool in reporting variable data. An instrument must be reliable to be valid. The reliability of an instrument can be gauged in three ways:

• stability (the consistency of outcomes with repeated implementation)
• interrater reliability (the consistency between different evaluators)
• internal consistency (the homogeneity of items within a scale as they relate to the measurement of the concept under investigation)

Cronbach α is a statistical procedure to assess instrument reliability by determining the internal consistency of items on a multi-item scale. 4, 9 Internal consistency evaluations examine how closely items on a scale represent the outcome concept under evaluation. Cronbach α scores range from 0.00 to 1.00—the higher the score the better the internal consistency. An acceptable Cronbach α as an evaluation of instrument reliability is often considered to be 0.70; however, a score of 0.80 or higher is preferable.

When choosing a measurement instrument for quantitative research, it is best to select one that has documented validity and reliability; alternatively, the researcher may independently complete and describe an assessment of the instrument's validity and reliability. Evaluation of a research study prior to practice implementation should also include assessment of the validity and reliability of the measurement instrument employed, which should be described within the research article.

This installment of AJN 's nursing research series explores how to measure both research outcomes and factors that are hypothesized to influence outcomes. Careful selection of measurement instruments will enhance the accuracy of research and maximize the ability of the research findings to meaningfully inform nursing practice and improve the well-being of patient populations. The next article in this series will further explore the selection and utilization of measurement instruments in the design and execution of nursing research.

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Sampling design in nursing research, selection of the study participants, interpretive methodologies in qualitative nursing research, introduction to statistical hypothesis testing in nursing research, cross-sectional studies.

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The value of quantitative research in nursing

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• PMID: 9782989

Quantitative research is an objective process used to obtain numerical data. The form of quantitative research used is influenced by current knowledge of the problem. Careful planning in the design stage is essential when undertaking quantitative research.

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• Essential skills in research. Nelson AE. Nelson AE. Prof Nurse. 1998 Aug;13(11):741. Prof Nurse. 1998. PMID: 9782984 No abstract available.

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Understanding quantitative research: part 1, juanita hoe senior clinical research associate, research department of mental health sciences, university college london, london, zoë hoare clinical trials statistician, bangor university, bangor.

This article, which is the first in a two-part series, provides an introduction to understanding quantitative research, basic statistics and terminology used in research articles. Critical appraisal of research articles is essential to ensure that nurses remain up to date with evidence-based practice to provide consistent and high-quality nursing care. This article focuses on developing critical appraisal skills and understanding the use and implications of different quantitative approaches to research. Part two of this article will focus on explaining common statistical terms and the presentation of statistical data in quantitative research.

Nursing Standard . 27, 17, 52-58. doi: 10.7748/ns.27.17.52.s65

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Nursing Research - Undergraduate: Quantitative vs. Qualitative

• Primary vs. Secondary
• Peer-Reviewed
• Quantitative vs. Qualitative
• Evidence-Based Practice
• Hiearchy of Evidence

What is the difference?

Quantitative research and qualitative research are two types of original research that you will come across when you are researching for original nursing research. 

While both contain useful data to use, you may be required to use one particular type of data for a paper or presentation. 

Definitions

Quantitative research : A traditional approach to research in which variables are identified and measured in a reliable and valid way (Houser, 2018, p. 34)

Qualitative research : A naturalistic approach to research in which the focus is on understanding the meaning of an experience from the individual's perspective (Houser, 2018, p. 35)

Houser, J. (2018). Nursing research: Reading, using, and creating evidence  (4th ed.).  Jones & Bartlett Learning. 

Quantitative Research

Think of quantitative research as a scientific experiment. You have your hypothesis, an item you want to change, an item you are comparing the change against, and then the results of your experiment.

At its core, quantitative research involves a control variable and an independent variable. Typically with nursing research, the independent variable will be the proposed change or intervention that you are looking to implement in your practice . Results of a quantitative research study should be something that can be replicated. Data results for quantitative research typically involve hard data, such as blood pressure, temperature, oxygen levels, etc. 

Types of Research Used for Evidence-Based Practice

Ovid. [OvidWoltersKluwer]. (2015, October 6). Types of research used for evidence- based practice [Video file]. YouTube.  https://youtu.be/jwOu24btBVk

Qualitative Research

Qualitative research focuses more on soft data, meaning it observes an individual's experience and cannot be replicated (Houser, 2018). Types of research studies that are qualitative include: "observations, in-depth interviews or focus-groups, case studies, and social interaction studies" (Houser, 2018). 

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Differences between Qualitative & Quantitative Research

" Quantitative research ," also called " empirical research ," refers to any research based on something that can be accurately and precisely measured.  For example, it is possible to discover exactly how many times per second a hummingbird's wings beat and measure the corresponding effects on its physiology (heart rate, temperature, etc.).

" Qualitative research " refers to any research based on something that is impossible to accurately and precisely measure.  For example, although you certainly can conduct a survey on job satisfaction and afterwards say that such-and-such percent of your respondents were very satisfied with their jobs, it is not possible to come up with an accurate, standard numerical scale to measure the level of job satisfaction precisely.

It is so easy to confuse the words "quantitative" and "qualitative," it's best to use "empirical" and "qualitative" instead.

Hint: An excellent clue that a scholarly journal article contains empirical research is the presence of some sort of statistical analysis

See "Examples of Qualitative and Quantitative" page under "Nursing Research" for more information.

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

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Difference between Qualitative vs. Quantitative Research

Qualitative research allows you to discover the why and how of people and activities. The abstract from this dissertation shows  the study: 

• Asks open-ended questions
• Examines people's activities
• Gives a narrative conclusion 
• Observations and Interviews 
• Understanding and interpreting social interactions and behaviors 
• Uses a small group of subjects 
• Uses words 

Quantitative research is based on things that can be accurately and precisely measured. The abstract shows that this dissertation:

• Describes with statistics
• Gives a statistical result 
• Makes predictions 
• Objective 
• Studies a randomly selected group 
• Tests a theory 
• Uses numbers
• Uses a survey 

Identifying Quantitative Research- Example

This abstract has several indications that this is a  quantitative  study:

• the goal of the study was examining relationships between several variables
• the researchers used statistical methods (logistic regression models)
• subjects completed questionnaires
• the study included a large number of subjects

Based on Eastern Michigan University Library Libguide Quantitative and Qualitative Research 

Identifying Qualitative Research-Example

This abstract has several indications that this is a qualitative study: 

• the goal of the study was to explore the subjects' experiences. 
• the researchers conducted open-ended interviews. 
• the researchers used thematic analysis when reviewing the interviews. 

Based on Eastern Michigan University Library Libguide  Quantitative and Qualitative Research 

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Understanding Qualitative and Quantitative Research in Nursing: Quantiative Research Information

• Qualitative Research Information
• Quantiative Research Information

About Quantiative Research

Quantitative research consists of information expressed in numbers, variables, and percentages. It seeks to confirm that all problems, dilemmas, or hypotheses have clear, concrete, and objective solutions that can be expressed in a numerical format. This type of research focuses on specific, narrow questions in a double-blind study, usually with a large random group and variables. The data collected can be analyzed with the help of statistics in an unbiased manner with the objective to explain, describe, or predict.

Helpful Website Links on Quantitative Research in Nursing

• A nurses’ guide to Quantitative Research
• Understanding Quantiative Research (Article)
• Quantitative research ... This practice profile
• Quantitative vs qualitative research methods... 'What direction for mental
• Implications for quantitative and qualitative reviews.

Quantitative research consist of following elements

• a hypothesis
•  a random or specific study group with a common similarity
•  variables (any element or behavior that can affect or change the outcomes of a study, such as a medication, treatment, or nursing intervention)
•  outcomes.

Quantitative research is usually conducted in a controlled environment, such as a lab or healthcare unit. It can be categorized as follows.

3 Types of Quantitative Research

Correlational research is the methodical investigation of relationships or interactions between two or more variables without determining the cause-and-effect relationship the variables may have on each other. An example is studying two chemotherapy medications for compatibility without studying how the medications can have adverse interactions with food or other common medications.

Quasi-experimental research explores a cause-and-effect relationship among variables. It also evaluates the underlying cause of a problem and studies the effects of variables (such as a nursing intervention) to evaluate their effect on the problem.

Descriptive research offers an accurate representation of the characteristics of a particular individual, situation, or group. Descriptive research is a way of discovering new meaning, describing numerically something that currently exists, determining the frequency with which something occurs, and categorizing information.

To find articles in ERIC   click on the  advanced search  tab. Use the phrase "quantitative research"  as one of your search terms.

Related terms that may be searched:  

Bayesian statistics

Correlation

Effect size

Error of measurement

Factor analysis

Goodness of fit

Hypothesis testing

Item analysis

Least squares

Monte Carlo Methods

Maximum likelihood

Multivariate analysis

Regression (statistics)

Robustness (statistics)

Statistical analysis

Statistical inference

Statistical significance

Markov processes

Also the following may be use, but not restricted to Subject Terms

Experimental design, design of experiments, statistical design, or research design

Quantitative Research eBooks

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Understanding Nursing Research

• Primary Research

What is Quantitative Research?

How do i tell if my article has quantitative research, qualitative research.

• Experimental Design
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There are two kinds of research: Quantitative and Qualitative

Quantitative is research that generates numerical data. If it helps, think of the root of the word "Quantitative." The word "Quantity" is at its core, and quantity just means "amount" or "how many." Heart rates, blood cell counts, how many people fainted at the jazz festival-- these are all examples of quantitative measures.

Qualitative , on the other hand, is a more subjective measurement. Think of the root of the word again, this time it's "Quality." If someone is called a quality person or someone's selling a high quality product, they're being measured in subjective terms, rather than concrete, objective terms (like numbers.) Qualitative research includes things like interviews or focus groups.

Just like when we examine whether or not our article is an example of Primary Research, the best way to examine what kind of data your article uses is by reading the article's Abstract, Methodologies, and Results sections. That will tell you how the research was conducted and what kind of data (qualitative or quantitative) was collected.

An example of what to look for in the Abstract can be seen here:

You can see that data was evaluated (66% of students were in compliance with school immunization requirements), a strategy was implemented (letters and emails were sent to student's parents/guardians), and at the end of the study, new quantitative data is reported (99.6% of students were in compliance with vaccination requirements).

Finding qualitative research can be trickier, since it can often take more time to collect. Examples of qualitative data include things like interview transcripts, focus group feedback, and journal entries detailing people's experiences and feelings. The easiest way to search for a qualitative study is to include the word "qualitative" as a keyword in your database search along with the search terms about the topic you're interested in.

Check out the video below to see an example of searching for qualitative research in CINAHL.

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The Advantages of Using Quantitative Methods in Nursing Research

How to Teach Lab Values to Nursing Students

When doing any kind of research, it is important to decide whether you will use qualitative data, quantitative data or a mixed methods approach. A lot of research that is done in the medical field is quantitative.

The function of quantitative data is to use an accurate approach to collect and analyze any data that has been measured. It is generally precise and based upon numbers. One of the purposes of quantitative research is to be deductive, rather than inductive.

Quantitative research is numbers-based. This can make it very precise, which is important when you are doing research in the medical field. When used appropriately, the results of quantitative research can be generalized.

Considerations

When you are doing quantitative research, you will need a hypothesis. You should select your approach to research based on what is suitable for your topic.

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Jamie Peacock began writing in 2009 for various blogs and Shakespeare Squared. She is an expert in travel, public health and shopping. She holds a Bachelor of Arts in psychology from Southern New Hampshire University and a Masters of Education from the University of North Dakota.

Cultivating New Leaders: 4th Pathways into Quantitative Aging Research Program

Twelve ambitious undergraduates from across the United States traveled to New York City this summer to participate in the popular, NIA-funded GPH program known as Pathways into Quantitative Aging Research (PQAR). Faculty and staff from the Department of Biostatistics hosted their fourth successful cohort as students explored substantive courses, innovative research and fun in the Big Apple!

Guided by interdisciplinary GPH faculty including Drs. Prince Amegbor, Mari Armstrong-Hough, Adolfo Cuevas and Hai Shu, students gained skills in statistics, computing and epidemiology, made connections through research projects with faculty and peers, and worked toward public health solutions to aging-related diseases.

The culminating experience was a day-long symposium attended by over 60 guests, including seven 2023 program alumni. First up was keynote speaker Dr. Lisa L. Barnes , a neuropsychologist at Rush University Medical College who researches the impact of social determinants of health on the African American aging process. She encouraged centering diversity in cognitive aging research, and wants to shift the paradigm about what we learn and how we think about disease. Her goal is to engage with communities, explaining what the data says and why it’s important.

Next came a panel discussion; 2023 program alumni presented their current research and gave an update on their academic progress. Torriana Avery told her peers that PQAR pushed her into a trajectory she never expected. “I'm going to apply for my master’s degree in applied biostatistics and epidemiology. I got an internship because I talked to a woman at the ENAR Datafest competition. I have way more experience in coding than I could ever imagine. I'm very happy to be the lead analyst on a project working with a pathology department, and I also work with a doctor for critically ill children in the pediatric intensive unit. It's been a wonderful experience,” she concluded.

Fellow alum Jessica Sanchez also benefitted from her PQAR experience. “The qualitative project I'm working on now uses different approaches, so having transferable basic research skills has been really helpful. Honestly, prior to participating in this program I didn't see myself as a researcher, but now that's my job! A lot of the support that I received was really helpful,” she said.

Finally, the 2024 PQAR students gave formal presentations on their research projects, expertly answering questions from an audience that included their families and friends. Some also shared their favorite takeaways from the program. Donjhai Holland’s deep dive into programming with R and machine learning models gave her the hands-on experience that broadened her technical skill set; she also described exploring iconic landmarks and attending her first ever Broadway show. But, she said, “The most rewarding part was the people: My cohort of brilliant, passionate individuals quickly became not just colleagues but friends.”

Carlos Rubin de Celis described his research under the supervision of Dr. Cuevas and doctoral student Cindy Patippe, which looked at the relationship between self-reported discrimination and chronic inflammation. He refined his skills in quality assurance and statistical analysis, and took coursework “which heavily contributed to my knowledge and continued joy in biostatistics and the field of public health!”

The month of August for the PQAR program is devoted to updates, review of evaluations, development of next year’s recruitment strategy and planning for  year-round support of conference attendance, office hours, and J term. As the program enters the fifth year of the grant, everyone in the Department of Biostatistics is hopeful it will be renewed. In just four years, the program has positively impacted the lives of 48 undergraduates, including some who are starting PhD programs in Biostatistics and related fields this September! For more information and engaging stories of PQAR “from the field,” check out this I AM GPH podcast which features a conversation with 2023 alumni Abena Dinizulu and Stephanie Perez, and hear first-hand about their PQAR experiences.

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Bensken WP , Koroukian SM , McGrath BM , Alberti PM , Cottrell EK , Sills MR. Unwinding of Continuous Medicaid Coverage Among Patients at Community Health Centers. JAMA Health Forum. 2024;5(1):e234622. doi:10.1001/jamahealthforum.2023.4622

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Unwinding of Continuous Medicaid Coverage Among Patients at Community Health Centers

• 1 Department of Research, OCHIN, Portland, Oregon
• 2 Quantitative Sciences Core, OCHIN, Portland, Oregon
• 3 Social Policy Program, OCHIN, Portland, Oregon
• 4 Case Western Reserve University, Cleveland, Ohio
• 5 AAMC Center for Health Justice, Association of American Medical Colleges, Washington, DC
• 6 Oregon Health and Science University, Portland, Oregon
• 7 University of Colorado, Anschutz Medical Campus, Aurora, Colorado

The US Families First Coronavirus Response Act of the COVID-19 pandemic required states to provide continuous Medicaid coverage to receive additional federal funding. 1 In April 2023, the Consolidated Appropriations Act required states to resume standard redeterminations, 2 with large initial decreases in Medicaid enrollment. 3 We studied a large national network of community health centers to assess Medicaid disenrollment during the first 6 months after the end of continuous enrollment.

This retrospective cohort study used data from the Accelerating Data Value Across a National Community Health Center Network 4 that included real-time electronic health record data from a nationwide network of community health care organizations supported on OCHIN’s single instance of the Epic electronic health record. 5 This study, including a waiver of informed consent, was approved by the Advarra Institutional Review Board and followed the STROBE reporting guideline.

The cohort included adult patients 18 years and older with an ambulatory or telehealth visit during continuous enrollment (April 1, 2020, to March 31, 2023) and after the provisions ended (April 1, 2023, to September 30, 2023) (eFigure in Supplement 1 ). These visits occurred at 1114 clinics within 158 health care organizations in 28 states.

The primary outcome was being disenrolled from Medicaid, defined as being Medicaid insured at the latest visit in the continuous enrollment period and uninsured at any visit in the post–continuous enrollment period. Our main independent variables were race and ethnicity and active comorbid conditions. To adjust for other factors associated with the primary outcome, the covariates sex, age at last visit during continuous enrollment, federal poverty level (FPL) percentage of income, state of residence, and the number of encounters (divided into quartiles) during continuous enrollment were included. The categories of race, ethnicity, and sex are described in the eMethods in Supplement 1 .

We used a multilevel logistic regression model, with a random intercept to account for patient clustering within states, to identify the association of the covariates with disenrollment from Medicaid. We report adjusted odds ratios, 95% CIs, and the intraclass correlation of the model. Data analyses were conducted in R.

Of the 575 170 patients who met inclusion criteria and were insured through Medicaid during continuous coverage, 16.7% (n = 96 189) were uninsured for at least 1 visit during the 6 months after continuous enrollment.

Disenrollment was higher among younger adults, American Indian and Alaska Native and Black or African American patients, those with a lower FPL percentage, those with higher utilization, and those with conditions including HIV/AIDS, mental health conditions, and substance use conditions ( Table 1 ).

In the adjusted analysis ( Table 2 ), American Indian and Alaska Native, Black or African American, Hispanic or Latino, those who reported multiple races, and those whose race was unknown had higher odds of being disenrolled relative to White patients ( Table 2 ). Those with a higher number of visits had higher odds of disenrollment ( Table 2 ). Those with HIV/AIDS, mental health conditions, and substance use conditions had higher odds of being disenrolled. There was substantial variation across states and the intraclass correlation of the model indicates approximately 8.3% of the variation among those who were disenrolled could be attributed to state differences.

Among patients with visits to community-based health care organizations, those who were American Indian and Alaska Native, Black or African American, and Hispanic or Latino had higher odds of being disenrolled from Medicaid after continuous enrollment. Additional risk factors for Medicaid disenrollment included more frequent prior visits, and diagnoses of HIV/AIDS, mental health needs, or substance use conditions. These findings call attention to the need to identify and address the reasons for inequities in Medicaid disenrollment and to implement strategies 6 to assist patients most likely to lose coverage.

A limitation is that our findings may not be generalizable to other patients insured through Medicaid who are not treated at community centers. Without eligibility data, we could not differentiate between Medicaid dropout and loss of eligibility.

Accepted for Publication: October 26, 2023.

Published: January 5, 2024. doi:10.1001/jamahealthforum.2023.4622

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Bensken WP et al. JAMA Health Forum .

Corresponding Author: Wyatt P. Bensken, PhD, OCHIN, 1881 SW Naito Pkwy, Portland, OR 97201 ( [email protected] ).

Author Contributions: Dr Bensken had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Bensken, Koroukian, Alberti, Cottrell, Sills.

Acquisition, analysis, or interpretation of data: Bensken, McGrath, Cottrell, Sills.

Drafting of the manuscript: Bensken.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Bensken, Koroukian, McGrath.

Administrative, technical, or material support: Bensken, Cottrell, Sills.

Supervision: Cottrell, Sills.

Conflict of Interest Disclosures: Dr Bensken reported a grant from the Patient-Centered Outcomes Research Institute (PCORI) during this work, and grants from the National Institutes of Health outside the submitted work. Dr Koroukian reported being supported by grants from the National Cancer Institute, Case Comprehensive Cancer Center (P30 CA043703); The Centers for Disease Control and Prevention, U48 DP005030-05S1 and U48 DP006404-03S7; National Institutes of Health (R15 NR017792, and UH3-DE025487, R01 AG074946-01); American Cancer Society (132678-RSGI-19-213-01-CPHPS and RWIA-20-111-02 RWIA); and by contracts from Cleveland Clinic Foundation, including a subcontract from Celgene Corporation. Dr McGrath reported grants from PCORI during the conduct of the study. Dr Cottrell reported grants from PCORI during the conduct of the study. Dr Sills reported grants from PCORI during the conduct of the study; grants from NIH outside the submitted work. No other disclosures were reported.

Funding/Support: This work was funded by and conducted with the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Research Network (CRN). ADVANCE is led by OCHIN in partnership with Health Choice Network, Fenway Health, and Oregon Health & Science University. ADVANCE is funded through PCORI, contract number RI-OCHIN-01-MC.

Role of the Funder/Sponsor: PCORI had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Molecular identification and functional characterization of lc-pufa biosynthesis elongase ( elovl2 ) gene in chinese sturgeon ( acipenser sinensis ).

Simple Summary

1. introduction, 2. materials and methods, 2.1. experimental animals and sample collection, 2.2. molecular cloning of elovl2 cdna and qpcr, 2.3. bioinformatic analyses, 2.4. functional characterization in yeast, 2.5. fatty acid analysis, 2.6. statistical analysis, 3.1. molecular identification of chinese sturgeon elovl2, 3.2. multiple protein sequence alignments of the elovl2 among vertebrates, 3.3. synteny and gene structure comparison of the elvol2 in vertebrates, 3.4. phylogenic analysis, 3.5. spatial and temporal distribution patterns of chinese sturgeon elovl2, 3.6. functional characterization of chinese sturgeon elovl2, 3.7. effect of dietary lipid sources on the elovl2 expression in chinese sturgeon, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Ding, H.; Shi, X.; Wen, Z.; Zhu, X.; Chen, P.; Hu, Y.; Xiao, K.; Yang, J.; Tian, T.; Zhang, D.; et al. Molecular Identification and Functional Characterization of LC-PUFA Biosynthesis Elongase ( elovl2 ) Gene in Chinese Sturgeon ( Acipenser sinensis ). Animals 2024 , 14 , 2343. https://doi.org/10.3390/ani14162343

Ding H, Shi X, Wen Z, Zhu X, Chen P, Hu Y, Xiao K, Yang J, Tian T, Zhang D, et al. Molecular Identification and Functional Characterization of LC-PUFA Biosynthesis Elongase ( elovl2 ) Gene in Chinese Sturgeon ( Acipenser sinensis ). Animals . 2024; 14(16):2343. https://doi.org/10.3390/ani14162343

Ding, Haoze, Xuetao Shi, Zhengyong Wen, Xin Zhu, Pei Chen, Yacheng Hu, Kan Xiao, Jing Yang, Tian Tian, Dezhi Zhang, and et al. 2024. "Molecular Identification and Functional Characterization of LC-PUFA Biosynthesis Elongase ( elovl2 ) Gene in Chinese Sturgeon ( Acipenser sinensis )" Animals 14, no. 16: 2343. https://doi.org/10.3390/ani14162343

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