Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases as this would likely be impossible ; instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities world view, perspectives, biases , so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported.
From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers. What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave.
Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use e. As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them.
However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.
Whatever philosophical standpoint the researcher is taking and whatever the data collection method e. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording.
If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place.
Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research.
It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from.
To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors J. We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented. Interpretation of the data will depend on the theoretical standpoint taken by researchers. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method.
With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live.
Thus, these 2 standpoints and there may have been others relevant to the research of Thurston and others 7 will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective.
Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings.
This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work. For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded.
As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group.
It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about.
This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms as in pharmacy practice.
Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin 8 is a tool that can help keep data organized by linking media and data files particularly valuable if, for example, video-taping of interviews is complemented by transcriptions.
It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.
All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data.
While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. While continuing with the processes of coding and theming described in the next 2 sections , it is important to consider not just what the person is saying but also what they are not saying.
For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? It is now apparent why determining the scale of measurement is important before beginning to utilize descriptive statistics.
For example, nominal scales where data is coded, as in the case of gender, would not have a mean score. Therefore, you must first use the scale of measurement to determine what type of descriptive statistic may be appropriate.
The results are then expressed as exact numbers and allow you to begin to give meaning to the data. For some studies, descriptive statistics may be sufficient if you do not need to generalize the results to a larger population.
For example, if you are comparing the percentage of teenagers that smoke in private versus public high schools, descriptive statistics may be sufficient. However, if you want to utilize the data to make inferences or predictions about the population, you will need to go anther step farther and use inferential statistics.
Inferential statistics examine the differences and relationships between two or more samples of the population. These are more complex analyses and are looking for significant differences between variables and the sample groups of the population.
Inferential statistics allow you test hypotheses and generalize results to population as whole. Following is a list of basic inferential statistical tests:.
Finally, the type of data analysis will also depend on the number of variables in the study. Studies may be univariate, bivariate or multivariate in nature. The following Slideshare presentation, Quantitative Data Analysis explains the use of appropriate statistical analyses in relation to the number of variables being examined.
Evaluation Toolkit — Analyze Quantitative Data — This resource provides an overview of four key methods for analyzing quantitative data. Analyzing Quantitative Data — The following link discusses the use of several types of descriptive statistics to analyze quantitative data.
Analyze Data — This website discusses how to determine the type of data analysis needed, descriptive statistics, inferential statistics, and useful software packages. Descriptive and Inferential Statistics — This resources provides an overview of these types of statistical analyses and how they are used.
From the table, you can see that 15 of the students surveyed who participated in the summer program reported being satisfied with the experience. A percent distribution displays the proportion of participants who are represented within each category see below. The most common descriptives used are:.
Depending on the level of measurement, you may not be able to run descriptives for all variables in your dataset. The mode most commonly occurring value is 3, a report of satisfaction. By looking at the table below, you can clearly see that the demographic makeup of each program city is different.
You can also disaggregate the data by subcategories within a variable. This allows you to take a deeper look at the units that make up that category.
In the table below, we explore this subcategory of participants more in-depth. From these results it may be inferred that the Boston program is not meeting the needs of its students of color. This result is masked when you report the average satisfaction level of all participants in the program is 2. In addition to the basic methods described above there are a variety of more complicated analytical procedures that you can perform with your data.
These types of analyses generally require computer software e. We provide basic descriptions of each method but encourage you to seek additional information e. For more information on quantitative data analysis, see the following sources: A correlation is a statistical calculation which describes the nature of the relationship between two variables i.
An important thing to remember when using correlations is that a correlation does not explain causation. A correlation merely indicates that a relationship or pattern exists, but it does not mean that one variable is the cause of the other.
An analysis of variance ANOVA is used to determine whether the difference in means averages for two groups is statistically significant. For example, an analysis of variance will help you determine if the high school grades of those students who participated in the summer program are significantly different from the grades of students who did not participate in the program.
In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables.
Quantitative Research Approach. Quantitative research most often uses deductive logic, in which researchers start with hypotheses and then collect data which can be used to determine whether empirical evidence to support that hypothesis exists.. Quantitative analysis requires numeric information in the form of variables. A variable is a way of measuring any characteristic that varies or has.
Analyze Quantitative Data Quantitative data analysis is helpful in evaluation because it provides quantifiable and easy to understand results. Quantitative data can be . 1/19 Quantitative data analysis. First of all let's define what we mean by quantitative data analysis. It is a systematic approach to investigations during which numerical data is collected and/or the researcher transforms what is collected or observed into numerical data.
Quantitative Research. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational bisnesila.tktative research focuses on gathering numerical data and generalizing it across groups of people or to explain a. Analyzing Quantitative Research. The following module provides an overview of quantitative data analysis, including a discussion of the necessary steps and types of statistical analyses.