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Statistics and Data Analysis for Nursing Research Essay Paper

To prepare:

Review the Statistics and Data Analysis for Nursing Research chapters assigned in this week’s Learning Resources. Pay close attention to the examples presented, as they provide information that will be useful when you complete the software exercise this week. You may also wish to review the Research Methods for Evidence-Based Practice video resources to familiarize yourself with the software.

Refer to the Week 4 Descriptive Statistics Assignment page and follow the directions to calculate descriptive statistics for the data provided using SPSS software. Download and save the Polit2SetA.sav data set. You will open the data file in SPSS.Statistics and Data Analysis for Nursing Research Essay Paper

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Compare your data output against the tables presented in the Week 4 Descriptive Statistics SPSS Output document. This will enable you to become comfortable with defining variables, entering data, and creating tables and graphs.

Formulate an initial interpretation of the meaning or implication of your calculations.

To complete:

Complete the Part I, Part II, and Part III steps and Assignment as outlined in the Week 4 Descriptive Statistics Assignment page.

Part I

Using the Polit2SetA data set, run descriptive statistics on the following variables: respondent’s age (age) and highest school grade completed (higrade). Create a frequency distribution for the variables: race and ethnicity (racethn) and currently employed (worknow). Create a table (in APA format) summarizing the results, using the below table shell as a model. Write a paragraph summarizing the information in the table.

Table 1. Demographic Data (N = 30)

n % M (SD)

Age (in years) 30 15(2.4)

Highest School Grade Completed 29 11(1.2)

Race and Ethnicity

Black, Not Hispanic 14 (46.67)

Hispanic 8 (26.67)

White, Not Hispanic 6 (20.0)

Other 2 (6.66)

Currently Employed

Yes 27 (90)

No 3 (10)

Note. Differences in sample size are due to missing data.

Follow these steps when using SPSS:

1. Open Polit2SetA data set.

2. Click on Analyze, then click on Descriptives Statistics, then Descriptives.Statistics and Data Analysis for Nursing Research Essay Paper

3. Click on the first continuous variable you wish to obtain descriptives for (respondent’s age), and then click on the arrow button and move it into the Variables box. Then click on highest school grade completed and then click on the arrow button and move it into the Variables box.

4. Click on the Options button in the upper-right corner. Click on mean, standard deviation, minimum, maximum, and skewness.

5. Click on Continue and then click on OK.

To run the frequency distribution in SPSS, do the following:

1. Click on Analyze, then click on Descriptive Statistics, then Frequencies.

2. Click on the first categorical variable you wish to obtain a frequency for (race and ethnicity), and then click on the arrow button and move it into the Variables box. Then click on currently employed, and then click on the arrow button and move it into the Variables box. Click on the Statistics button in the upper-right corner, then in the Dispersion box click on Minimum and Maximum.

3. Click on Continue and then click on OK.

Assignment: Create a table (in APA format) summarizing the results, using the below table shell as a model. Write a paragraph summarizing the information in the table.

Part II

For the variables respondent’s age (age) and highest school grade completed (higrade) create a histogram with a normal curve displayed over the histogram.

To create a histogram for respondent’s age in SPSS, do the following:

1. Click on Graphs, then on Legacy Dialogs, then Histogram.

2. Click on the variable respondent’s age and then click on the arrow button and move it into the Variables box. Click on the Display Normal Curve button, which is right below the Variables box.

3. Click on OK.

To create a histogram for highest school grade completed in SPSS, do the following:

1. Click on Graphs, then on Legacy Dialogs, then Histogram.

2. Click on respondent’s age in the Variable box and click the arrow to move it back to the box on the left that contains all the variables.Statistics and Data Analysis for Nursing Research Essay Paper

3. Click on the variable highest school grade completed and then click the arrow button and move it into the Variables box. The Display Normal Curve button should alredy be on.

4. Click on OK.

Assignment: Using the data obtained when you ran the descriptives and the histograms, determine whether the data skewed. If so, is it a positive or negative skew?

Part III

Using the Polit2SetA data set, run descriptive statistics on the variable “Family Income Prior Month, all sources” (Income).

Follow these steps when using SPSS:

1. Click on Analyze, then click on Descriptives Statistics, then Descriptives.

2. Click on Family Income Prior Month, all sources, and then click on the arrow button and move it into the Variables box.

3. Click on the Options button in the upper-right corner. Click on mean, standard deviation, minimum, maximum, S.E. Mean (standard error of the mean), and skewness.

4. Click on Continue and then click on OK.

Assignment: Using the descriptive statistics for Family Income Prior Month, all sources (Income), answer the following questions:

1. What is the mean income in this sample?

2. What is the standard deviation?

3. What is the standard error of the mean?

4. Compute a 95% confidence interval around the mean. (Use 1.96 for the 95% CI and get the standard error from the descriptive statistics table). The formula is as follows:

95% CI = [mean ± (1.96 ´ SE)]

5. Compute a 99% confidence interval around the mean. (Use 2.58 for the 99% CI and get the standard error from the descriptive statistics table). The formula is as follows:

99% CI = [mean ± (2.58 ´ SE)]

6. Which interval is wider? Explain.

Review the corresponding Week 4 Descriptive Statistics Exercises SPSS Output document that has the SPSS output for the above problems. Compare your output with the output in the file.

This is a concise, step-by-step guide to conducting qualitative nursing research using various forms of data analysis. It is part of a unique series of books devoted to seven different qualitative designs and methods in nursing, written for both novice researchers and specialists seeking to develop or expand their competency. This practical resource encompasses such methodologies as content analysis, a means of organizing and interpreting data to elicit themes and concepts; discourse analysis, used to analyze language to understand social or historical context; narrative analysis, in which the researcher seeks to understand human experience through participant stories; and focus groups and case studies, used to understand the consensus of a group or the experience of an individual and his or her reaction to a difficult situation such as disease or trauma.Statistics and Data Analysis for Nursing Research Essay Paper

Good qualitative research uses a systematic and rigorous approach that aims to answer questions concerned with what something is like (such as a patient experience), what people think or feel about something that has happened, and it may address why something has happened as it has. Qualitative data often takes the form of words or text and can include images.

This six-part research series is aimed at clinicians who wish to develop research skills, or who have a particular clinical problem that they think could be addressed through research. The series aims to provide insight into the decisions that researchers make in the course of their work, and to also provide a foundation for decisions that nurses may make in applying the findings of a study to practice in their own Unit or Department. The series emphasises the practical issues encountered when undertaking research in critical care settings; readers are encouraged to source research methodology textbooks for more detailed guidance on specific aspects of the research process. A couple of points: 1. It is artificial to describe research as qualitative or quantitative. Studies often include both dimensions. However, for the purposes of this paper/series, this distinction is drawn for clarity of writing. 2. It is common practice for quantitative studies to refer to study ‘subjects’ and qualitative studies to refer to study ‘participants’. For ease of reading, the latter term will be used throughout this series.

What is sampling?

Research studies usually focus on a defined group of people, such as ventilated patients or the parents of chronically ill children. The group of people in a study is referred to as the sample. Because it is too expensive and impractical to include the total population in a research study, the ideal study sample represents the total population from which the sample was drawn (eg, all ventilated patients or all parents of chronically ill children). This point—that studying an entire population is, in most cases, unnecessary—is the key to the theory of sampling. Sampling means simply studying a proportion of the population rather than the whole. The results of a study that has assembled its sample appropriately can be more confidently applied to the population from which the sample came. Using the examples of samples provided at the start of the paragraph, we can see that Chlan sampled 54 patients from a population of patients who required mechanical ventilation,1(see Evidence-Based Nursing 1999 April, p49) whereas Burke et al sampled 50 children (and their parents) from a populationof all children requiring admission to hospital for chronic health conditions.2(see Evidence-Based Nursing 1998 July, p79) In both studies the researchers wanted to say something that would apply to the population by examining a small portion of those populations.Statistics and Data Analysis for Nursing Research Essay Paper

When reading a paper that sets out to say something about a population by studying a sample, readers need to assess the external validity. External validity is the degree to which the findings of a study can be generalised beyond the sample used in the study. The ability to generalise is almost totally dependent on the adequacy of the sampling process. Nurses should consider several possible threats to external validity when appraising a paper and deciding whether the results could be applied to patients in their care:

Unique sample selection: study findings may be applicable only to the group studied. For example, the findings of a study of a telephone support programme for caregivers of people with Alzheimer’s disease that recruited a sample from local Alzheimer’s Association branches would not necessarily apply to the population of caregivers as a whole because most caregivers do not belong to such local groups, and therefore are different from the sample.

Unique research settings: the particular context in which the study takes place can greatly affect the external validity of the findings. For example, Tourigny sampled 6 African-American youths to learn about deliberate exposure to HIV.3 (see Evidence-Based Nursing 1998 October, p130) The study took place in the context of extreme poverty in a uniquely deprived US urban setting. The opportunity for a suburban district nurse in the UK to apply the theory generated by this study may be limited by the very different social settings involved.

History: the passage of time can affect the findings. For example, studies of mechanisms for implementing research findings within healthcare organisations might be affected by the organisational and structural changes that occur at local and national levels over time. Examples of such reorganisation include the effects of separating healthcare provision from purchasing through the NHS “internal market” of the late 1980s and the creation of health maintenance organisations in the US.

Unique research constructs: the particular constructs, concepts, or phenomena studied may be specific to the group sampled. For example, researchers evaluating the concept of “quality” in healthcare should recognise that professionals and consumers may differ in their perceptions of the concept and should be explicit about how they actually measured it.

Sampling in quantitative research

Quantitative research is most often used when researchers wish to make a statement about the chance (or probability) of something happening in a population. For example, a person is 65% less likely to die a cardiac related death if she eats a Mediterranean type diet compared with the recommended Step 1 diet of the American Heart Association.4 (See Evidence-Based Nursing 1999 April, p48.) Quantitative studies usually use sampling techniques based on probability theory. Probability sampling, as it is known, has 2 central features:Statistics and Data Analysis for Nursing Research Essay Paper

The researcher has (in theory) access to all members of a population

Every member of the population has an equal and non-zero chance of being selected for the study sample. In other words they cannot have “no chance” of being sampled.

Three concepts relevant to probability samples are sampling error, random sampling, and sampling bias. Each of these will be described.

SAMPLING ERROR

Probability samples allow researchers to minimise sampling error in that they give the highest chance of the sample being representative of the total population. Sampling error occurs in all probability samples and is unavoidable because no sample can ever totally represent the population. There will always be a gap between a sample’s representativeness and the population’s known or unknown characteristics—the sampling error. Readers of quantitative research should look for evidence that the researchers tried to combat sampling error. Specifically, the authors should identify the study sample using a random selection process and should provide substantiation for the sample size. The size of the sampling error generally decreases as the size of the sample increases.

RANDOM SAMPLING

Random selection works because as individuals enter the sample, their characteristics (which are different from the population) balance the characteristics of other individuals. For example, in a randomly selected sample of users of mental health services, there will be users who are from upper socioeconomic groups, and these will be balanced by those who are from lower socioeconomic groups.

Successful random sampling requires a sufficiently large sample. If the sample is large enough, then differences in outcomes that exist between groups will be detected statistically, whereas if it is too small, important differences may be missed. One of the clues that can alert the reader to a study that is not large enough is the confidence interval around a study finding. Although not all studies provide confidence intervals, these are becoming increasingly popular in the reporting of quantitative studies. A confidence interval provides a statement on the level of confidence that the true value for a population lies within a specified range of values. A 95% confidence interval can be described as follows: “if sampling is repeated indefinitely, each sample leading to a new confidence interval, then in 95% of the samples, the interval will cover the true population value.5 The larger the sample size, the more narrow the confidence interval, and therefore the more precise the study finding. In the study by Egerman et al in this issue of Evidence-Based Nursing (p73), the relative risk of necrotising enterocolitis with oral versus intramuscular dexamethasone was 5.1 with a 95% confidence interval of 0.8 to 36.6. This range means that the true relative risk could be as low as 0.8 or as high as 36.6 and because a relative risk of 1.0 (meaning no difference between groups) is included in this range, the conclusion is that there is no difference between the 2 methods of dexamethasone administration in the risk of necrotising enterocolitis. The reader should, however, take careful note that this confidence interval is very wide. It is possible that with a larger sample size and, consequently, a narrower confidence interval, a statistically significant difference between groups may have been found because the confidence interval may no longer include the relative risk of 1.0.Statistics and Data Analysis for Nursing Research Essay Paper

The overall purpose of research for any profession is to discover the truth of the discipline This paper examines the controversy over the methods by which truth is obtained, by examining the differences and similarities between quantitative and qualitative research The historically negative bias against qualitative research is discussed, as well as the strengths and weaknesses of both approaches, with issues highlighted by reference to nursing research Consideration is given to issues of sampling, the relationship between the researcher and subject, methodologies and collated data, validity, reliability, and ethical dilemmas The author identifies that neither approach is superior to the other, qualitative research appears invaluable for the exploration of subjective experiences of patients and nurses, and quantitative methods facilitate the discovery of quantifiable information Combining the strengths of both approaches in triangulation, if time and money permit, is also proposed as a valuable means of discovering the truth about nursing It is argued that if nursing scholars limit themselves to one method of enquiry, restrictions will be placed on the development of nursing knowledge

The researchers of various disciplines often use qualitative and quantitative research methods and approaches for their studies. Some of these researchers like to be known as qualitative researchers; others like to be regarded as quantitative researchers. The researchers, thus, are sharply polarised; and they involve in a competition of pointing out the benefits of their own preferred methods and approaches. But, both the methods and approaches (qualitative and quantitative) have pros and cons. This study, therefore, aims to discuss the advantages and disadvantages of using qualitative and quantitative research approaches and methods in language testing and assessment research. There is a focus on ethical considerations too. The study found some strengths of using qualitative methods for language “assessment and testing” research—such as, eliciting deeper insights into designing, administering, and interpreting assessment and testing; and exploring test-takers’ behaviour, perceptions, feelings, and understanding. Some weaknesses are, for instance, smaller sample size and time consuming. Quantitative research methods, on the other hand, involve a larger sample, and do not require relatively a longer time for data collection. Some limitations are that quantitative research methods take snapshots of a phenomenon: not in-depth, and overlook test-takers’ and testers’ experiences as well as what they mean by something. Among these two research paradigms, the quantitative one is dominant in the context of language testing and assessment research.Statistics and Data Analysis for Nursing Research Essay Paper

Qualitative research gathers information on human behavior and then explores its implications. Research in nursing topics is used to broaden the pool of knowledge in the field. Investigators use qualitative research methods to develop and refine nursing care and to give the nursing field more credibility.

Unlike sampling error, which cannot be avoided completely, sampling bias is usually the result of a flaw in the research process. It is systematic, and increasing the size of the sample just increases the effect of the bias. Sampling bias occurs when the sample is not representative of the population. An example of sampling bias has already been highlighted in the section on external validity: sampling only caregivers from Alzheimer’s Association groups introduces a sampling bias if the study aim is to generalise to the whole population of caregivers of people with Alzheimer’s disease. Bias, in the context of RCTs, can be thought of as “…any factor or process that tends to deviate the results or conclusions of a trial away from the truth.”6 Two important biases related to sampling that could affect generalisability of study findings are referral filter bias and volunteer bias. In referral filter bias, the selection that occurs at each stage in the referral process from primary to secondary to tertiary care can generate patient samples that are very different from one another.7 For example, the results of a study of patients with asthma under the care of specialists are not likely to be generalisable to patients with asthma in primary care settings. In volunteer bias, people who volunteer to participate in a study may have exposures or outcomes (eg, they tend to be healthier) that differ from those of non-volunteers Statistics and Data Analysis for Nursing Research Essay Paper

Despite the hostility to positivism shown by qualitative methodologists in nursing, as in other disciplines, the epistemological and ontological instincts of qualitative researchers seem to coincide with those of the positivists, especially Bayesian positivists. This article suggests that positivists and qualitative researchers alike are pro-observation, proinduction, pro-plausibility and pro-subjectivity. They are also anti-cause, anti-realist, anti-explanation, anti-correspondence, anti-truth. In only one respect is there a significant difference between positivist and qualitative methodologists: most positivists have believed that, methodologically, the natural sciences and the social sciences are the same; most qualitative researchers are adamant that they are not. However, if positivism fails as a philosophy of the natural sciences (which it probably does), it might well succeed as a philosophy of the social sciences, just because there is a methodological watershed between the two. Reflex antagonism to positivism might therefore be a major obstacle to understanding the real reasons why qualitative research and the natural sciences are methodologically divergent; and less hostility on the part of qualitative nurse researchers might bring certain advantages in its wake.

The researcher has (in theory) access to all members of a population

Every member of the population has an equal and non-zero chance of being selected for the study sample. In other words they cannot have “no chance” of being sampled.

Three concepts relevant to probability samples are sampling error, random sampling, and sampling bias. Each of these will be described.

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The practice field is a significant learning arena for nursing students in Norway, as half of the bachelor’s program takes place in clinical practice [1]. Thus, preparations for the students’ meeting with real patients constitute a substantial part of teaching efforts within the university. The comprehension of the transfer value of these preparations when it comes to clinical practice probably has an impact on students’ achievements in the field of practice [2]. It has been argued that nursing education is inadequate in preparing students for practice and contributes to burnout syndrome among nurses and an earlier retirement from the profession [3, 4].

Nursing students have various backgrounds and different prerequisites for goal achievement in accordance with the National Curriculum of Nursing [1]. Requirements for Patient Safety [5] suggest that students encounter patients well prepared and with the proper knowledge and practical skills required within an increasingly specialized healthcare. Consequently, several nursing education institutions have introduced clinical skills tests ahead of clinical practice periods.

At the Norwegian University of Science and Technology (NTNU), simulated patient scenarios are used to a large extent as preparation for the students’ clinical studies in practice. In clinical laboratory practice (CLP) the students simulate patient situations at various levels, from basic simulation in which fellow students play the roles of “patient” and “nurse” to more advanced scenarios with technologically advanced simulators (manikins) [6, 7]. The practical exercises are usually organized with student groups (10–12) working together under the supervision of one lecturer per group. Each student experiences merely one supervised training per procedure, due to the fact that this is a resource intensive learning activity. A single training session is not sufficient to assure the level of the students’ skills before passing the tests required to enter clinical practice. Hence, students are encouraged to familiarize themselves with the procedures before and after the organized CLP. The development of electronic textbooks, with evidence-based descriptions and instructive videos of relevant procedures, has been produced to support the students in these unsupervised study activities. It is uncertain to what extent this has been done. Various CLP models have been tried out, some of these in cooperation with nurses from the clinical fields [8, 9]. It is also questioned to what extent the preparations within the university should be extended, so that training sessions (simulation) can replace some of the time spent in clinical practice [10, 11].

The current project was completed for freshmen in bachelor’s nursing in the spring of 2014 and was part of the CLP before the first clinical practice in nursing homes. This includes skills training in various procedures before their first practice period in community healthcare setting.Statistics and Data Analysis for Nursing Research Essay Paper

We found statistically significant improvements postimplementation in four patient survey items specifically impacted by the change to bedside report. Nursing perceptions of report were significantly improved in the areas of patient safety and involvement in care and nurse accountability postimplementation. However, there was a decline in nurse perception that report took a reasonable amount of time after bedside report implementation; contrary to these perceptions, there was no significant increase in nurse overtime. Patient falls at shift change decreased substantially after the implementation of bedside report. An intervening variable during the study period invalidated the comparison of medication errors pre‐ and postintervention. There was some indication from both patients and nurses that bedside report was not always consistently implemented.

Conclusions

Several positive outcomes were documented in relation to the implementation of a blended bedside shift report, with few drawbacks. Nurse attitudes about report at the final data collection were more positive than at the initial postimplementation data collection.

Relevance to clinical practice

If properly implemented, nursing bedside report can result in improved patient and nursing satisfaction and patient safety outcomes. However, managers should involve staff nurses in the implementation process and continue to monitor consistency in report format as well as satisfaction with the process.

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis.

Qualitative research covers a very broad range of philosophical underpinnings and methodological approaches. Each has its own particular way of approaching all stages of the research process, including analysis, and has its own terms and techniques, but there are some common threads that run across most of these approaches. This Research Made Simple piece will focus on some of these common threads in the analysis of qualitative research.

So you have collected all your qualitative data – you may have a pile of interview transcripts, field-notes, documents and notes from observation. The process of analysis is described by Richards and Morse1 as one of transformation and interpretation.

It is easy to be overwhelmed by the volume of data – novice qualitative researchers are sometimes told not to worry and the themes will emerge from the data. This suggests some sort of epiphany, (which is how it happens sometimes!) but generally it comes from detailed work and reflection on the data and what it is telling you. There is sometimes a fine line between being immersed in the data and drowning in it!Statistics and Data Analysis for Nursing Research Essay Paper

A first step is to sort and organise the data, by coding it in some way. For example, you could read through a transcript, and identify that in one paragraph a patient is talking about two things; first is fear of surgery and second is fear of unrelieved pain. The codes for this paragraph could be ‘fear of surgery’ and ‘fear of pain’. In other areas of the transcript fear may arise again, and perhaps these codes will be merged into a category titled ‘fear’. Other concerns may emerge in this and other transcripts and perhaps best be represented by the theme ‘lack of control’. Themes are thus more abstract concepts, reflecting your interpretation of patterns across your data. So from codes, categories can be formed, and from categories, more encompassing themes are developed to describe the data in a form which summarises it, yet retains the richness, depth and context of the original data. Using quotations to illustrate categories and themes helps keep the analysis firmly grounded in the data. You need to constantly ask yourself ‘what is happening here?’ as you code and move from codes, to categories and themes, making sure you have data to support your decisions. Analysis inevitably involves subjective choices, and it is important to document what you have done and why, so a clear audit trail is provided. The coding example above describes codes inductively coming from the data. Some researchers may use a coding framework derived from, for example, the literature, their research questions or interview prompts, (Ritchie and Spencer2) or a combination of both approaches.

Qualitative data, such as transcripts from an interview, are often routed in the interaction between the participant and the researcher. Reflecting on how you, as a researcher, may have influenced both the data collected and the analysis is an important part of the analysis.

As well as keeping your brain very much in gear, you need to be really organised. You may use highlighting pens and paper to keep track of your analysis, or use qualitative software to manage your data (such as NVivio or Atlas Ti). These programmes help you organise your data – you still have to do all the hard work to analyse it! Whatever you choose, it is important that you can trace your data back from themes to categories to codes. There is nothing more frustrating than looking for that illustrative patient quote, and not being able to find it.

If your qualitative data are part of a mixed methods study, (has both quantitative and qualitative data) careful thought has to be given to how you will analyse and present findings. Refer to O’Caithain et al3 for more details.Statistics and Data Analysis for Nursing Research Essay Paper

There are many books and papers on qualitative analysis, a very few of which are listed below.4,–,6 Working with someone with qualitative expertise is also invaluable, as you can read about it, but doing it really brings it alive.

Unquestionably, data analysis is the most complex and mysterious of all of the phases of a qualitative project, and the one that receives the least thoughtful discussion in the literature. For neophyte nurse researchers, many of the data collection strategies involved in a qualitative project may feel familiar and comfortable. After all, nurses have always based their clinical practice on learning as much as possible about the people they work with, and detecting commonalities and variations among and between them in order to provide individualised care. However, creating a database is not sufficient to conduct a qualitative study. In order to generate findings that transform raw data into new knowledge, a qualitative researcher must engage in active and demanding analytic processes throughout all phases of the research. Understanding these processes is therefore an important aspect not only of doing qualitative research, but also of reading, understanding, and interpreting it.

For readers of qualitative studies, the language of analysis can be confusing. It is sometimes difficult to know what the researchers actually did during this phase and to understand how their findings evolved out of the data that were collected or constructed. Furthermore, in describing their processes, some authors use language that accentuates this sense of mystery and magic. For example, they may claim that their conceptual categories “emerged” from the data1—almost as if they left the raw data out overnight and awoke to find that the data analysis fairies had organised the data into a coherent new structure that explained everything! In this EBN notebook, I will try to help readers make sense of some of the assertions that are made about qualitative data analysis so that they can develop a critical eye for when an analytical claim is convincing and when it is not.Statistics and Data Analysis for Nursing Research Essay Paper

Qualitative data come in various forms. In many qualitative nursing studies, the database consists of interview transcripts from open ended, focused, but exploratory interviews. However, there is no limit to what might possibly constitute a qualitative database, and increasingly we are seeing more and more creative use of such sources as recorded observations (both video and participatory), focus groups, texts and documents, multi-media or public domain sources, policy manuals, photographs, and lay autobiographical accounts.

Qualitative data are not the exclusive domain of qualitative research. Rather, the term can refer to anything that is not quantitative, or rendered into numerical form. Many quantitative studies include open ended survey questions, semistructured interviews, or other forms of qualitative data. What distinguishes the data in a quantitative study from those generated in a qualitatively designed study is a set of assumptions, principles, and even values about truth and reality. Quantitative researchers accept that the goal of science is to discover the truths that exist in the world and to use the scientific method as a way to build a more complete understanding of reality. Although some qualitative researchers operate from a similar philosophical position, most recognise that the relevant reality as far as human experience is concerned is that which takes place in subjective experience, in social context, and in historical time. Thus, qualitative researchers are often more concerned about uncovering knowledge about how people think and feel about the circumstances in which they find themselves than they are in making judgements about whether those thoughts and feelings are valid.Statistics and Data Analysis for Nursing Research Essay Paper

Qualitative analytic reasoning processes

What makes a study qualitative is that it usually relies on inductive reasoning processes to interpret and structure the meanings that can be derived from data. Distinguishing inductive from deductive inquiry processes is an important step in identifying what counts as qualitative research. Generally, inductive reasoning uses the data to generate ideas (hypothesis generating), whereas deductive reasoning begins with the idea and uses the data to confirm or negate the idea (hypothesis testing).2 In actual practice, however, many quantitative studies involve much inductive reasoning, whereas good qualitative analysis often requires access to a full range of strategies.3 A traditional quantitative study in the health sciences typically begins with a theoretical grounding, takes direction from hypotheses or explicit study questions, and uses a predetermined (and auditable) set of steps to confirm or refute the hypothesis. It does this to add evidence to the development of specific, causal, and theoretical explanations of phenomena.3 In contrast, qualitative research often takes the position that an interpretive understanding is only possible by way of uncovering or deconstructing the meanings of a phenomenon. Thus, a distinction between explaining how something operates (explanation) and why it operates in the manner that it does (interpretation) may be a more effective way to distinguish quantitative from qualitative analytic processes involved in any particular study.

Because data collection and analysis processes tend to be concurrent, with new analytic steps informing the process of additional data collection and new data informing the analytic processes, it is important to recognise that qualitative data analysis processes are not entirely distinguishable from the actual data. The theoretical lens from which the researcher approaches the phenomenon, the strategies that the researcher uses to collect or construct data, and the understandings that the researcher has about what might count as relevant or important data in answering the research question are all analytic processes that influence the data. Analysis also occurs as an explicit step in conceptually interpreting the data set as a whole, using specific analytic strategies to transform the raw data into a new and coherent depiction of the thing being studied. Although there are many qualitative data analysis computer programs available on the market today, these are essentially aids to sorting and organising sets of qualitative data, and none are capable of the intellectual and conceptualising processes required to transform data into meaningful findings.Statistics and Data Analysis for Nursing Research Essay Paper

Specific analytic strategies

Although a description of the actual procedural details and nuances of every qualitative data analysis strategy is well beyond the scope of a short paper, a general appreciation of the theoretical assumptions underlying some of the more common approaches can be helpful in understanding what a researcher is trying to say about how data were sorted, organised, conceptualised, refined, and interpreted.

Many qualitative analytic strategies rely on a general approach called “constant comparative analysis”. Originally developed for use in the grounded theory methodology of Glaser and Strauss,4 which itself evolved out of the sociological theory of symbolic interactionism, this strategy involves taking one piece of data (one interview, one statement, one theme) and comparing it with all others that may be similar or different in order to develop conceptualisations of the possible relations between various pieces of data. For example, by comparing the accounts of 2 different people who had a similar experience, a researcher might pose analytical questions like: why is this different from that? and how are these 2 related? In many qualitative studies whose purpose it is to generate knowledge about common patterns and themes within human experience, this process continues with the comparison of each new interview or account until all have been compared with each other. A good example of this process is reported in a grounded theory study of how adults with brain injury cope with the social attitudes they face (see Evidence-Based Nursing, April 1999, p64).Statistics and Data Analysis for Nursing Research Essay Paper

Constant comparison analysis is well suited to grounded theory because this design is specifically used to study those human phenomena for which the researcher assumes that fundamental social processes explain something of human behaviour and experience, such as stages of grieving or processes of recovery. However, many other methodologies draw from this analytical strategy to create knowledge that is more generally descriptive or interpretive, such as coping with cancer, or living with illness. Naturalistic inquiry, thematic analysis, and interpretive description are methods that depend on constant comparative analysis processes to develop ways of understanding human phenomena within the context in which they are experienced.

Constant comparative analysis is not the only approach in qualitative research. Some qualitative methods are not oriented toward finding patterns and commonalities within human experience, but instead seek to discover some of the underlying structure or essence of that experience through the intensive study of individual cases. For example, rather than explain the stages and transitions within grieving that are common to people in various circumstances, a phenomenological study might attempt to uncover and describe the essential nature of grieving and represent it in such a manner that a person who had not grieved might begin to appreciate the phenomenon. The analytic methods that would be employed in these studies explicitly avoid cross comparisons and instead orient the researcher toward the depth and detail that can be appreciated only through an exhaustive, systematic, and reflective study of experiences as they are lived.Statistics and Data Analysis for Nursing Research Essay Paper

Although constant comparative methods might well permit the analyst to use some pre-existing or emergent theory against which to test all new pieces of data that are collected, these more phenomenological approaches typically challenge the researcher to set aside or “bracket” all such preconceptions so that they can work inductively with the data to generate entirely new descriptions and conceptualisations. There are numerous forms of phenomenological research; however, many of the most popular approaches used by nurses derive from the philosophical work of Husserl on modes of awareness (epistemology) and the hermeneutic tradition of Heidegger, which emphasises modes of being (ontology).5 These approaches differ from one another in the degree to which interpretation is acceptable, but both represent strategies for immersing oneself in data, engaging with data reflectively, and generating a rich description that will enlighten a reader as to the deeper essential structures underlying a particular human experience. Examples of the kinds of human experience that are amenable to this type of inquiry are the suffering experienced by individuals who have a drinking problem (see Evidence-Based Nursing, October 1998, p134) and the emotional experiences of parents of terminally ill adolescents (see Evidence-Based Nursing, October 1999, p132). Sometimes authors explain their approaches not by the phenomenological position they have adopted, but by naming the theorist whose specific techniques they are borrowing. Colaizzi and Giorgi are phenomenologists who have rendered the phenomenological attitude into a set of manageable steps and processes for working with such data and have therefore become popular reference sources among phenomenological nurse researchers.

Ethnographic research methods derive from anthropology’s tradition of interpreting the processes and products of cultural behaviour. Ethnographers documented such aspects of human experience as beliefs, kinship patterns and ways of living. In the healthcare field, nurses and others have used ethnographic methods to uncover and record variations in how different social and cultural groups understand and enact health and illness. An example of this kind of study is an investigation of how older adults adjust to living in a nursing home environment (see Evidence-Based Nursing, October 1999, p136). When a researcher claims to have used ethnographic methods, we can assume that he or she has come to know a culture or group through immersion and engagement in fieldwork or participant observation and has also undertaken to portray that culture through text.6 Ethnographic analysis uses an iterative process in which cultural ideas that arise during active involvement “in the field” are transformed, translated, or represented in a written document. It involves sifting and sorting through pieces of data to detect and interpret thematic categorisations, search for inconsistencies and contradictions, and generate conclusions about what is happening and why.Statistics and Data Analysis for Nursing Research Essay Paper

Many qualitative nurse researchers have discovered the extent to which human experience is shaped, transformed, and understood through linguistic representation. The vague and subjective sensations that characterise cognitively unstructured life experiences take on meaning and order when we try to articulate them in communication. Putting experience into words, whether we do this verbally, in writing, or in thought, transforms the actual experience into a communicable representation of it. Thus, speech forms are not the experiences themselves, but a socially and culturally constructed device for creating shared understandings about them. Narrative analysis is a strategy that recognises the extent to which the stories we tell provide insights about our lived experiences.7 For example, it was used as a strategy to learn more about the experiences of women who discover that they have a breast lump (see Evidence-Based Nursing, July 1999, p93). Through analytic processes that help us detect the main narrative themes within the accounts people give about their lives, we discover how they understand and make sense of their lives.

By contrast, discourse analysis recognises speech not as a direct representation of human experience, but as an explicit linguistic tool constructed and shaped by numerous social or ideological influences. Discourse analysis strategies draw heavily upon theories developed in such fields as sociolinguistics and cognitive psychology to try to understand what is represented by the various ways in which people communicate ideas. They capitalise on critical inquiry into the language that is used and the way that it is used to uncover the societal influences underlying our behaviours and thoughts.8 Thus, although discourse analysis and narrative analysis both rely heavily on speech as the most relevant data form, their reasons for analysing speech differ. The table⇓ illustrates the distinctions among the analytic strategies described above using breast cancer research as an example.Statistics and Data Analysis for Nursing Research Essay Paper

Cognitive processes inherent in qualitative analysis

The term “qualitative research” encompasses a wide range of philosophical positions, methodological strategies, and analytical procedures. Morse1 has summarised the cognitive processes involved in qualitative research in a way that can help us to better understand how the researcher’s cognitive processes interact with qualitative data to bring about findings and generate new knowledge. Morse believes that all qualitative analysis, regardless of the specific approach, involves:

comprehending the phenomenon under study

synthesising a portrait of the phenomenon that accounts for relations and linkages within its aspects

theorising about how and why these relations appear as they do, and

recontextualising, or putting the new knowledge about phenomena and relations back into the context of how others have articulated the evolving knowledge.

Although the form that each of these steps will take may vary according to such factors as the research question, the researcher’s orientation to the inquiry, or the setting and context of the study, this set of steps helps to depict a series of intellectual processes by which data in their raw form are considered, examined, and reformulated to become a research product.

Quality measures in qualitative analysis

It used to be a tradition among qualitative nurse researchers to claim that such issues as reliability and validity were irrelevant to the qualitative enterprise. Instead, they might say that the proof of the quality of the work rested entirely on the reader’s acceptance or rejection of the claims that were made. If the findings “rang true” to the intended audience, then the qualitative study was considered successful. More recently, nurse researchers have taken a lead among their colleagues in other disciplines in trying to work out more formally how the quality of a piece of qualitative research might be judged. Many of these researchers have concluded that systematic, rigorous, and auditable analytical processes are among the most significant factors distinguishing good from poor quality research.9 Researchers are therefore encouraged to articulate their findings in such a manner that the logical processes by which they were developed are accessible to a critical reader, the relation between the actual data and the conclusions about data is explicit, and the claims made in relation to the data set are rendered credible and believable. Through this short description of analytical approaches, readers will be in a better position to critically evaluate individual qualitative studies, and decide whether and when to apply the findings of such studies to their nursing practice.Statistics and Data Analysis for Nursing Research Essay Paper

Written by a noted qualitative research scholar and contributing experts, the book describes the philosophical basis for conducting research using data analysis and delivers an in-depth plan for applying its methodologies to a particular study, including appropriate methods, ethical considerations, and potential challenges. It presents practical strategies for solving problems related to the conduct of research using the various forms of data analysis and presents a rich array of case examples from published nursing research. These include author analyses to support readers in decision making regarding their own projects. The book embraces such varied topics as data security in qualitative research, the image of nursing in science fiction literature, the trajectory of research in several nursing studies throughout Africa, and many others. Focused on the needs of both novice researchers and specialists, it will be of value to health institution research divisions, in-service educators and students, and graduate nursing educators and students.

Key Features:
Explains how to conduct nursing research using content analysis, discourse analysis, narrative analysis, and focus groups and case studies
Presents state-of-the-art designs and protocols
Focuses on solving practical problems related to the conduct of research
Features rich nursing exemplars in a variety of health/mental health clinical settings in the United States and internationally Statistics and Data Analysis for Nursing Research Essay Paper

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