# Mixed Methods

Mix Methods research involves the combination of qualitative and quantitative approaches to addressing a research problem. Generally, qualitative and quantitative methods have separate philosophical positions when it comes to how to uncover insights in addressing research questions.

For many, mixed methods have their own philosophical position, which is pragmatism. Pragmatists believe that if it works it’s good. Therefore, if mixed methods lead to a solution it’s an appropriate method to use.

This post will try to explain some of the mixed method designs. Before explaining it is important to understand that there are several common ways to approach mixed methods

• Qualitative and Quantitative are equal (Convergent Parallel Design)
• Quantitative is more important than qualitative (explanatory design)
• Qualitative is more important than quantitative

Convergent Parallel Design

This design involves the simultaneous collecting of qualitative and quantitative data. The results are then compared to provide insights into the problem. The advantage of this design is the quantitative data provides for generalizability while the qualitative data provides information about the context of the study.

However, the challenge is in trying to merge the two types of data. Qualitative and quantitative methods answer slightly different questions about a problem. As such it can be difficult to paint a picture of the results that are comprehensible.

Explanatory Design

This design puts emphasis on the quantitative data with qualitative data playing a secondary role. Normally, the results found in the quantitative data are followed up on in the qualitative part.

For example, if you collect surveys about what students think about college and the results indicate negative opinions, you might conduct an interview with students to understand why they are negative towards college. A Likert survey will not explain why students are negative. Interviews will help to capture why students have a particular position.

The advantage of this approach is the clear organization of the data. Quantitative data is more important. The drawback is deciding what about the quantitative data to explore when conducting the qualitative data collection.

Exploratory Design

This design is the opposite of explanatory. Now the qualitative data is more important than the quantitative. This design is used when you want to understand a phenomenon in order to measure it.

It is common when developing an instrument to interview people in focus groups to understand the phenomenon. For example, if I want to understand what cellphone addiction is I might ask students to share what they think about this in interviews. From there, I could develop a survey instrument to measure cell phone addiction.

The drawback to this approach is the time consumption. It takes a lot of work to conduct interviews, develop an instrument, and assess the instrument.

Conclusions

Mixed methods are not that new. However, they are still a somewhat unusual approach to research in many fields. Despite this, the approaches of mixed methods can be beneficial depending on the context.

# Narrative Research

Narrative research is a form of qualitative research that is used when a researcher wants to share the stories of individuals. There are many everyday examples that employ narrative design including autobiographies, biographies, narrative interviews, oral histories, and personal accounts. In this post, we will examine several characteristics of narrative design including the following…

• Focus on chronological experiences
• Restorying
• Coding of themes
• Collaboration with participants

Focus on Chronological Experiences

Narrative research places the data in chronological order. This is one main characteristic that makes narrative research different from other forms of research. The sequencing of events helps in creating a picture for the reader to appreciate the experience of the individual.

Restorying

Restorying is taking the words of the person providing the information and rewording the text in the words of the researcher. Restorying allows the researcher to develop a sequence of events while establishing cause and effect.

This analysis is for rewriting the experience in a comprehensible manner. The researcher needs to be sensitive to the interaction of characters in the narrative, the continuity of the text, and the setting of the experience. Improving the readability of the text for the future audience is important with restorying.

Coding of Themes

As most qualitative research, narrative research involves coding. The themes can be addressed within the narrative or after sharing the person’s story through a reflective approach. One benefit of coding is that it is one way in which to summarize information and make it understandable to the readers.

Collaboration with Participants

In narrative research, collaboration involves including the participants in the interpretation and results of the project. There an s active discussion about the presentation and meaning of the data. It also can serve as a form of validity as the participant can check the accuracy of the findings.

Conclusion

Narrative research is one way of documenting the experiences of individual people. However, presenting this information means understanding the characteristics of this approach. Through keeping in mind these traits, it can help you to communicate the experiences of others clearly.

# Traits of Grounded Theory: Core Category, Theory Generation, Memos

In a previous post, we began a discussion on grounded theory traits. There are at least six traits of grounded as listed below.

• Process approach
• Theoretical sampling
• Constant comparison
• Core category
• Generation of theory
• Memos

In this post, we will look at the last three characteristics in detail.

Core Category

Qualitative research emphasizes the use of categories. A core category a category which serves as the foundation for the development of a theory. Below is a criteria for developing a core category.

• It needs to recur frequently in the data
• It is at the center of the study is it interacts with all aspects of the study
• It is logical and naturally appears from the data
• It is highly abstract

It is difficult to provide an example of developing a core category. The point is that this category plays a significant role in understanding the central phenomenon in comparison to other categories you may develop.

Theory Generation

Generating a theory in grounded theory involves the explanation of a process in abstract terms. The theory developed has little external validity because it is grounded so thoroughly in the data. In general, a grounded theory can appear in one of three forms

• propositions (hypotheses)
• narrative form

A visual coding paradigm is an illustration of the theory that a researcher creates. There are many examples on the internet of visual representation of a theory.

Propositions are statements that explain the relationships among the various categories of a study.  These statements can also be worded as hypotheses. These hypotheses are often tested in the future quantitatively.

A narrative form involves the development of propositions but rather than being only statements, the statements are connected to create a description of the central phenomenon. This involves a high level of creativity to not only interpret the data but to capture in a narrative description.

Memos

Memos are short notes a researcher takes while conducting grounded theory research. They’re similar to field notes but they involve personal reflection rather than raw data. Memos help to shape the data analysis aspect of grounded theory.

Conclusion

Grounded theory is a useful way to assess processes that take place in the real world. These six characteristics provide some basic information about this approach.

# Traits of Grounded Theory: Process, Sampling, & Comparision

In this post, we will look at some of the traits of grounded theory regardless of the design that is used by a researcher. Generally, there are six core traits of grounded theory and they are

• Process approach
• Theoretical sampling
• Constant comparison
• Core category
• Generation of theory
• Memos

We will only look at the first three in this post and save the rest for a future discussion.

Process Approach

A core trait of grounded theory is its use to examine a process. A process is a sequence of actions among people. As a grounded theory research breaks down the process into steps, these steps become know as categories. The categories can be further broken down into codes.

For example, let’s say a teacher wants to develop a grounded theory about the “process of dropping out of college.” Such a study would involve describing the steps that lead a person to dropout of college. The various steps in this process would come from interviewing students who dropout of college to determine the order of events the precipitated dropout.

Theoretical Sampling

Theoretical sampling involves selecting data to collect based on its use in developing a theory. A grounded theory researcher is always seeking to find data that would be useful in the continual development of a theory.

Returning to our dropout example, a grounded theorist may choose to collect data from student dropouts, teachers, and parents. The reason for selecting these participants is that the researcher may be convinced that these participants have useful information in developing a theory.

It is important to use theoretical sampling while the theory emerges. A grounded theory researcher is constantly collecting and analyzing data simultaneously. This process is mutually beneficial because the sampling helps the analysis while the analysis helps to focus the sampling.

Data collection does not stop until the data becomes saturated. Saturation is the point that new data will not provide any additional information. At what point this happens is at the discretion of the researcher.

Constant Comparison

As information is coded and then put into categories, new information is compared to existing codes and categories. This is a constant comparison. By comparing information constantly it allows for new codes and categories to emerge if current ones do not fit new data. In addition, codes and or categories that were separate may be combined as the data indicates.

Conclusion

Grounded theory involves looking at and describing processes by employing theoretical sampling and constant comparison. These are just some of the characteristics of grounded theory

# Grounded Theory: Emerging & Constructivist Design

Grounded theory is a qualitative methodology that was described briefly in this blog previously when we looked at systematic design. In this post, we will look at two other designs that fall under the grounded theory approach which are emerging and constructivist design.

Emerging Design

Emerging design was in many ways a reaction to systematic design. Glaser and Strauss worked together to develop grounded theory during the 1960’s. By the 1990’s Strauss along with Corbin had refined grounded theory into what is now know as systematic design.

Glasser had issues with systematic design. He considered it too rigid and strict with the emphasis on rules and procedures. In response to this, he developed the emerging design.

Glasser proposed to allow the theory to emerge from the data rather than forcing the data into preconceived categories. Glasser was also focused on a more iterative approach. This means that data was compared to data, data was compared to category, and category compared to category.

Glasser viewed grounded theory as the process of abstracting to higher and higher level rather than only describing a process. The generate theory should appropriately fit the data, should actually work, be relevant, and changeable.

Constructivist Design

The constructivist design is the youngest of the three grounded theory designs. It was first developed in the earlier 2000’s by Charmaz. Unlike the other forms of grounded theory with the focus on categories, codes, and theory generating. The constructivist design emphasizes the views, values, and feelings of the people rather than the process.

Whereas Strauss & Corbin and Glasser would focus on describing a process in their systematic or emerging design approach, the constructivist design would focus on how people felt during these process and try to extract meaning from the experience.

For example, if we conducted a study on men with chronic illness the results would vary depending on the grounded theory design we used. If we used systematic or emerging design we would focus on the common process of acquiring and dealing with a chronic illness. However, if we used the constructivist design we would focus on how the men feel during their experience with a chronic illness as well as trying to determine what it means to have a chronic illness.

Which to Choose?

The decision of what design is best depends on the purpose of the study and the preference of the researcher. It is difficult to say one is better or worst than the other. Rather, each is appropriate depending on the context of the study.

# Grounded Theory: Systematic Design

Grounded theory is a systematic approach to qualitative research that involves the development of theory or the description of a process/action. The key characteristic of grounded theory is the systematic nature of it. This in contrast to most qualitative methods that are highly flexible in how a researcher can go about collection and analysis of data.

Due to its structured nature, grounded theory is an excellent beginning point for those who are interested in qualitative research. This is especially true for those who come from a quantitative background in which the steps of conducting research are clear.

However, there is some disagreement in conducting grounded theory as there are several different approaches that vary in the amount of structure they provided. In this post, we will look specifically at the grounded theory design know as the systematic approach.

Systematic Approach

The systematic approach to grounded theory focuses heavily on inductive thinking. In many ways, the researcher starts with the most specific information they collected and summarize and move to the most abstract characteristics they were able to find through analyzing the data. This experience involves three steps in the coding process.

1. Open coding
2. Axial coding
3. Selective coding

Open coding involves making the initial categories in which to place the data. For example, let’s say you are looking at how principals support their teachers in professional development. You notice that several teachers share how the principals serve a leadership role in their professional development. This information of the principals as leaders in professional development could serve as a category.

A Category can also have dimensionalized properties. This means that there is a continuum on which the trait is seen. For example, a principal can be one of several types of leaders in professional development. He can be a dictator or at the other extreme, he can be laissez faire. Both of these are examples of leadership and there would be many examples in-between.

Axial Coding

Step two involves axial coding. This involves taking one of your categories and making it the central phenomenon of the study. For example, if you are convinced that the heart of professional development for teachers is the leadership of the principal this would become the central phenomenon.   All the other categories are one of the following.

• Causal conditions: These influences the central phenomenon
• Context: The setting
• Strategies: Influenced by the central phenomenon
• Intervening conditions: Influence the strategies
• Consequences: The results of using the strategies

Another name for this is the coding paradigm. So an example is attached Doc1

The attachment shows the coding for systematic grounded theory and the interrelation among the various factors. It is very similar to developing a statistical model which is why grounded theory is an excellent starting point for first-time qualitative researchers.

Selective Coding

Selective coding involves taking the coding paradigm and converting it to written text. It involves writing out the storyline in which the process happens and providing an explanation. It is not at all easy to take all of the information involved with interviews, developing a paradigm, and finally writing this down in coherent language.

Conclusion

Grounded theory is an established qualitative method. This method involves three steps that take data, diagrams it, and lastly, describes a process using words.

# Survey Design

Survey design is used to describe the opinions, beliefs, behaviors, and or characteristics of a population based on the results of a sample. This design involves the use of surveys that include questions, statements, and or other ways of soliciting information from the sample. This design is used for descriptive purpose primarily but can be combined with other designs (correlational, experimental) at times as well. In this post, we will look at the following.

• Types of Survey Design
• Characteristics of Survey Design

Types of Survey Design

There are two common forms of survey design which are cross-sectional and longitudinal.   A cross-sectional survey design is the collection of data at one specific point in time. Data is only collected once in a cross-sectional design.

A cross-sectional design can be used to measure opinions/beliefs, compare two or more groups, evaluate a program, and or measure the needs of a specific group. The main goal is to analyze the data from a sample at a given moment in time.

A longitudinal design is similar to a cross-sectional design with the difference being that longitudinal designs require collection over time.Longitudinal studies involve cohorts and panels in which data is collected over days, months, years and even decades. Through doing this, a longitudinal study is able to expose trends over time in a sample.

Characteristics of Survey Design

There are certain traits that are associated with survey design. Questionnaires and interviews are a common component of survey design. The questionnaires can happen by mail, phone, internet, and in person. Interviews can happen by phone, in focus groups, or one-on-one.

The design of a survey instrument often includes personal, behavioral and attitudinal questions and open/closed questions.

Another important characteristic of survey design is monitoring the response rate. The response rate is the percentage of participants in the study compared to the number of surveys that were distributed. The response rate varies depending on how the data was collected. Normally, personal interviews have the highest rate while email request has the lowest.

It is sometimes necessary to report the response rate when trying to publish. As such, you should at the very least be aware of what the rate is for a study you are conducting.

Conclusion

Surveys are used to collect data at one point in time or over time. The purpose of this approach is to develop insights into the population in order to describe what is happening or to be used to make decisions and inform practice.

# Qualitative Research: Hermeneutics & Phenomenology

Key components of qualitative research include hermeneutics and phenomenology. This post will examine these two terms and their role in qualitative research.

Hermeneutics

Hermeneutics is essential a method of interpretation of a text. The word hermeneutics comes from Hermes, the Greek messenger God. As such, at least for the ancient Greeks, there was a connection with interpreting and serving as a messenger. Today, his term is most commonly associated with theology such as biblical hermeneutics.

In relation to biblical hermeneutics, Augustine (354-430) develop a process of hermeneutics that was iterative. Through studying the Bible and the meaning of one’s own interpretations of the Bible, a person can understand divine truth. There was no need to look at the context, history, or anything else. Simply the Word and your interpretation of it.

In the 17th century, the Dutch philosopher Spinoza expanded on Augustine’s view of hermeneutics by stating that the text, its historical context, and even the author of a text, should be studied to understand the text. In other words, text plus context leads to truth.

By combing Augustine’s view of the role of the individual in hermeneutics with Spinoza’s contribution to the context we arrive at how interpretation happens in qualitative research.

In qualitative research, data interpretation (aka hermeneutics) involves the individual’s interpretation combined with the context that the data comes from. Both the personal interpretation and the context of the data influence each other.

Phenomenology

The develops in hermeneutics led to the development of the philosophy called phenomenology. Phenomenology states that a phenomenon can only be understood subjectively (from a certain viewpoint) and intuitively (through thinking and finding hidden meaning).

In phenomenology, interpretation happens through describing events, analyzing an event, and by connecting a current experience to another one or by finding similarities among distinct experiences.

For a phenomenologist, there is a constant work of reducing several experiences into abstract constructs through an inductive approach. This is a form of theory building that is connected with several forms of qualitative research, such as grounded theory.

Conclusion

Hermeneutics has played an important role in qualitative research by influencing the development of phenomenology. The study of a phenomenon is for the purpose of seeing how context will influence interpretation.

# Philosophical Foundations of Research: Epistemology

Epistemology is the study of the nature of knowledge. It deals with questions as is there truth and or absolute truth, is there one way or many ways to see something. In research, epistemology manifest itself in several views. The two extremes are positivism and interpretivism.

Positivism

Positivism asserts that all truth can be verified and proven scientifically and can be measured and or observed. This position discounts religious revelation as a source of knowledge as this cannot be verified scientifically. The position of positivist is also derived from realism in that there is an external world out there that needs to be studied.

For researchers, positivism is the foundation of quantitative research. Quantitative researchers try to be objective in their research, they try to avoid coming into contact with whatever they are studying as they do not want to disturb the environment. One of the primary goals is to make generalizations that are applicable in all instances.

For quantitative researchers, they normally have a desire to test a theory. In other words, the develop one example of what they believe is a truth about a phenomenon (a theory) and they test the accuracy of this theory with statistical data. The data determines the accuracy of the theory and the changes that need to be made.

By the late 19th and early 20th centuries, people were looking for alternative ways to approach research. One new approach was interpretivism.

Interpretivism

Interpretivism is the complete opposite of positivism in many ways. Interpretivism asserts that there is no absolute truth but relative truth based on context. There is no single reality but multiple realities that need to be explored and understood.

For interpretist, There is a fluidity in their methods of data collection and analysis. These two steps are often iterative in the same design. Furthermore, intrepretist see themselves not as outside the reality but a player within it. Thus, they often will share not only what the data says but their own view and stance about it.

Qualitative researchers are interpretists. They spend time in the field getting close to their participants through interviews and observations. They then interpret the meaning of these communications to explain a local context specific reality.

While quantitative researchers test theories, qualitative researchers build theories. For qualitative researchers, they gather data and interpret the data by developing a theory that explains the local reality of the context. Since the sampling is normally small in qualitative studies, the theories do not often apply to many.

Conclusion

There is little purpose in debating which view is superior. Both positivism and interpretivism have their place in research. What matters more is to understand your position and preference and to be able to articulate in a reasonable manner. It is often not what a person does and believes that is important as why they believe or do what they do.

# Analyzing Qualitative Data

Analyzing qualitative data is not an easy task. Instead of punching script into a statistical programming and receiving results, you become the computer who needs to analyze the data. For this reason alone, the analysis of qualitative data is difficult as different people will have vastly different interpretations of data.

This post will look at the following

• The basic characteristics of qualitative data analysis
• Exploring and coding data

Basic Characteristics

Qualitative data analysis has the following traits

• Inductive form
• Analyzing data while still collecting data
• Interpretative

Qualitative analysis is inductive by nature. This indicates that a researcher goes from specific examples to the generation of broad concepts or themes. In many ways, the researcher is trying to organize and summarize what they found in their research coherently in nice neat categories and themes.

Qualitative analysis also involves analyzing while still collecting data.You begin to process the data while still accumulating data. This is an iterative process that involves moving back and forth between analysis and collection of data. This is a strong contrast to quantitative research which is usually linear in nature.

Lastly, qualitative analysis is highly subjective. Everyone who views the data will have a different perspective on the results of a study. This means that people will all see different ideas and concepts that are important in qualitative research.

Exploring and Coding

Coding data in qualitative research can be done with text, images, and or observations. In coding, a researcher determines which information to share through the development of segments, codes, categories, themes. Below is the process for developing codes and categories

Reading the text means to get familiar with it and now what is discussed.

2. Pick segments of quotes to include in the article

After reading the text, you begin to pick quotes from the interview that will be used for further inductive processing

3, Develop codes from segments

After picking many different segments, you need to organize them into codes. All segments in one code have something in common that unites them as a code.

4. Develop categories from codes

The next level of abstract is developing categories from codes. The same process in step 3 is performed here.

5. Develop themes from categories

This final step involves further summarizing the results of the categories development into themes. The process is the same as steps 3 and 4.

Please keep in mind that as you move from step 1 to 5 the number of concepts decreases. For example, you may start with 50 segments that are reduced to 10 codes then reduce to 5 categories and finally 3 themes.

Conclusion

Qualitative data analysis is not agreed upon. There are many different ways to approach this. In general, the best approach possible is one that is consistent in terms of its treatment of the data. The example provided here is just one approach to organizing data in qualitative research.

# Content Analysis In Qualitative Research

Content analysis serves the purpose in qualitative research to enable you to study human behavior indirectly through how people choose to communicate. The type of data collected can vary tremendously in this form of research. However, common examples of data include images, documents, and media.

In this post, we will look at the following in relation to content analysis

• The purpose of content analysis
• Coding in content analysis
• Analysis in content analysis
• Pros and cons in content analysis

Purpose

The purpose of content analysis is to study the central phenomenon through the analysis of examples of the communication of people connected with the central phenomenon. This information is coded into categories and themes. Categories and themes are just different levels of summarizing the results of the analysis. Themes are a summary of categories and categories are a direct summary of the data that was analyzed.

Coding

Coding the data is the process of organizing the results in a comprehensible way. In terms of coding, there are two choices.

• Establish categories before beginning the analysis
• Allow the categories to emerge during analysis

Which is best depends on the research questions and context of the study.

There are two forms of content manifest content and latent content. Manifest content is evidence that is directly seen such as the words in an interview. Latent content refers to the underlying meaning of content such as the interpretation of an interview.

The difference between these two forms of content is the objective or subjective nature of them. Many studies include both forms as this provides a fuller picture of the central phenomenon.

Analysis

There are several steps to consider when conducting a content analysis such as…

• Explain what you are analyzing-This can be words, phrases, interviews, pictures, etc.
• Explain your coding approach-Explained above
• Present results

This list is far from complete but provides some basis for content analysis

Pros and Cons

Pros of content analysis include

• Unobtrusive-Content analysis does not disturb the field or a people group normally
• Replication-Since the documents are permanent, it is possible to replicate a study
• Simplicity-Compared to other forms of research, content analysis is highly practical to complete

Cons include

• Validity-It is hard to assess the validity of the analysis. The results of an analysis is the subjective opinion of an individual(s)
• Limited data-Content analysis is limited to recorded content. This leaves out other forms of information

Conclusion

Content analysis provides another way for the qualitative research to analyze the world. There are strengths and weaknesses to this approach as there are such for forms of analysis. The point is to understand that there are times when the content analysis is appropriate

# Interviews in Qualitative Research

Interviews provide another way to collect data when conducting qualitative research. In this post, we will look at the following,

• Characteristics of the interviewees
• Types of interviews
• Types of questions
• Tips for conducting interviews

Characteristics of the Interviewees

Qualitative research involves two types of interviewees. If you are interviewing only one person this is a one-on-one interview. If you are interviewing a group this is often called a focus group.

One-on-One interviewing allows for in-depth data collection but takes a great deal of time. Focus groups, on the other hand, allows a researcher to gather a more varied opinion while saving time. Care also must be taken to make sure everyone participates in a focus group.

Types of Interviews

There are three common types of interview structured, semi-structured and informal. Structured interviews consist of a strict set of questions that are read in order word for word to interviewees. The goal is for the interviewee to answer all questions.

At the opposite extreme are informal interviews which are conversations that can flow in any direction. There is no set script of questions and the interviewee can go anywhere they want in the conversation

The middle ground between formal and informal interviewing is semi-structured interviews. In this approach, the researcher has questions they want to ask but they can vary the order, reword, ask follow-up questions, and or omit questions As such, there is a negotiable formatÂ in semi-structuredÂ interviews.

Types of Questions

There are several types of questions that are used in qualitative research. The types are self-explanatory and are listed below with an example

• Knowledge question-“How does email work?”
• Experience question-“What was it like growing up in the 1990’s?”
• Opinion question-“What is your view of the tax cuts?”
• Feeling question-“How do the change in curfew make you feel?”
• Sensory question-“What does the kitchen smell like?”

Keep in mind that open ended questions are more common the closed-ended questions in qualitative research. This allows the interviewee to share their perspective rather than reply yes and no.

Tips for Conducting Interviews

Below are some tips for conducting interviews

• Establish rapport-Establishing some form of relationship helps the interviewee to feel comfortable.
• Location matters-Pick a quiet place to conduct the interview(s) if possible.
• Record the interview-This is standard practice and is necessary in order to develop a transcript.
• Take notes-Even with a recording, taking notes helps you to recall what happened during the interview.
• Use wisdom with questions-Avoid leading questions which are unfair and make sure to ask one question at a time.
• Show respect and courtesy during the interview-Be polite and considerate of the interviewee who has given you some of their time.

This not meant to be an exhaustive list but rather to provide some basic guidelines.

Conclusion

Along with observations, interviews is one of the most common forms of data collection in qualitative research. When you are in the field doing interviews it is important to consider what kind of interview you are doing, what questions you are going to ask, as well as the guidelines for conducting interviews presented in this post.

# Observation in Qualitative Research

Observation is one of several forms of data collection in qualitative research. It involves watching and recording, through the use of notes, the behavior of people at the research site. In this post, we will cover the following

• Different observational roles
• The guidelines for observation
• Problems with observation

Observational Roles

The role you play as an observer can vary between two extremes which are

nonparticipant to participant observer. A nonparticipant observer does not participate in any of the activities of the people being studied. For example, you are doing teaching observations, as you sit in the classroom you only watch what happens and never participate.

The other extreme is a participant observer. In this role, a researcher takes part in the activities of the group. For example, if you are serving as a teacher in a lower income community and are observing the students while you teach and interact with them this is participant observer.

Between these two extremes of non-participation and participation are several other forms of observation.  For example, a a non-participant observer can be an observer-as-participant or a complete observer. Furthermore, a participant observer can be a participant-as-observer or complete participant. The difference between these is whether or not the the group being studied knows the identity of the researcher.

Guidelines for Observation

• Decide your role-What type of observer are you
• Determine what you are observing-The observation must support what you are trying to learn about the central phenomenon
• Observe the subject multiple times-This provides a deeper understanding of the subjects
• Take notes-An observer should of some way of taking notes. These notes are called fieldnotes and provide a summary of what was seen during the observation.

Problems with Observation

Common problems that are somewhat related when doing observations are observer effect, observer bias, observer expectations. The observer effect is how the people being observed change their behavior because of the presence of an outsider. For example, it is common for students to behave differently when the principal comes to observe the teacher. They modify their behavior because of the presence of the principal. In addition, if the students are aware of the principal’s purpose, they may act extra obedient for the sake of their teacher.

Observer bias is the potential that a researchers viewpoint may influence what they see. For example, if a principal is authoritarian he may view a democratic classroom with a laid back teacher as chaotic when the students may actually be learning a great deal.

Observer expectation is the observer assuming beforehand what they are going to see. For example, if a researcher is going to observe students in a lower income school, he will expect to see low performing unruly students. This becomes a self-fulfilling prophecy as the researcher sees what they expected to see.

Conclusion

Observation is one of the forms of data collection in qualitative research. Keeping in mind the types of observation, guidelines, and problems can help a researcher to succeed.

# Qualitative Research Sampling Methods

Qualitative research employs what is generally called purposeful sampling, which is the intentional selection of individuals to better understand the central phenomenon. Under purposeful sampling, there are several ways of selecting individuals for a qualitative study. Below are some examples discussed in this post.

• Maximal variation
• Extreme case
• Theory
• Homogeneous
• Opportunistic
• Snowball

We will also look at suggestions for sample size.

Maximal Variation Sampling

Maximal variation involves selecting individuals that are different on a particular characteristic. For example, if you are doing a study on discrimination, you might select various ethnicities to share their experience with discrimination. By selecting several races you are ensuring a richer description of discrimination.

Extreme Case Sampling

Extreme case sampling involves looking out outliers or unusually situations. For example, studying a successful school in a low-income area may be an example since high academic performance does not normally correlate with low-income areas.

Theory Sampling

Theory sampling involves selecting people based on their ability to help understand theory or process. For example, if you are trying to understand why students drop out of school. You may select dropout students and their teachers to understand the events that lead to dropping out. This technique is often associated with grounded theory.

Homogeneous Sampling

This approach involves selecting several members from the same subgroup. For example, if we are looking at discrimination at a university, we may select only African-American English Majors. Such an example is a clear sub-group of a larger community.

Opportunistic Sampling

Opportunistic sampling is in many ways sampling without a plan or starting with on sampling method and then switching to another because of changes in the circumstances. For example, you may begin with theory sampling as you study the process of dropping out of high school. While doing this, you encounter a student who is dropping out in order to pursue independent studies online. This provides you with the “opportunity” to study an extreme case as well.

Snowball Sampling

Sometimes it is not clear who to contact. In this case, snowball sampling may be appropriate. Snowball sampling is an approach commonly used by detectives in various television shows. You find one person to interview and this same person recommends someone else to talk to. You repeat this process several times until an understanding of the central phenomenon emerges.

Sample Size

Qualitative research involves a much lower sampling size than quantitative. This is for several reasons

• You want to provide an in-depth look at one perspective rather than a shallow overview of many perspectives.
• The more people involved the harder it is to conduct the analysis.
• You want to share the complexities rather than the generalizations of a central phenomenon.

One common rule of thumb is to collect data until saturation is reached. Saturation is when the people in your data begin to say the same things. How long this takes depends and this is by far not an absolute standard.

Conclusion

This is just some of the more common forms of sampling in qualitative research. Naturally, there are other methods and approaches to sampling. The point is that the questions of the study and the context shape the appropriateness of a sampling method.

# Qualitative Data Collection

Qualitative data collection involves normally collecting non-numerical information about a phenomenon. The data collected can be text, pictures, and numerical observation at times. This post will examine differences between qualitative and quantitative research as well as the steps of qualitative data collection

Qualitative vs. Quantitative Research

There are several major difference between qualitative and quantitative data collection.

• Qualitative relies on a much smaller sample size using purposeful sampling while quantitative involves larger samples selected through random/non-random sampling.
• Qualitative uses open-ended questions while quantitative uses closed questions when interviewing
• Qualitative researchers almost always develop their own instruments while quantitative researchers use other people’s instruments.
• Qualitative research involves personal contact with the respondents while quantitative research does not necessarily require this.
• Qualitative research tries to help us understand a central phenomenon better while quantitative research seeks to test theories.

Steps of Qualitative Data Collection

Anything that involves qualitative research is rarely linear in nature. This means that the steps mention below does not necessarily happen in the order present. Qualitative research is often iterative and involves repeating steps, moving back and forth as well. In general, it is common to see the following steps happen in qualitative research.

1. Determine participants-This involves deciding who you will collect data from by identifying a population and appropriate sampling strategy.
2. Ethical concerns-This relates to obtaining the needed permissions to contact the sample population.
3. Decide what to collect-You need to determine what information that the participants supply is needed to answer your research questions.