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A discussion on the strengths and weaknesses of using participant observations to research teacher a

By Megan Sumeracki Image from Pixabay There are a lot of different methods of conducting research, and each comes with its own set of strengths and weaknesses. I've been thinking a lot about the various research approaches because I'm teaching a senior-level research methods class with a lab this spring. This has led me to think a lot about how these different research methodologies might work together. While most researchers are exposed to a variety of methodologies throughout graduate training, we tend to become engrossed with our own specialty.

This makes sense, at least to me, as there are so many nuances that it can take years to become truly proficient in conducting research in our own areas. Specialization seems necessary; however, this is exactly why effective communication and collaboration is key. We have said many times before that "it takes a village" and open communication to solve large problems.

  1. For research conducted in indigenous communities, it may be necessary to gain permission from the tribal leader or council.
  2. When it comes to student learning, I feel strongly that it takes a diverse group of experts from different research backgrounds and various experiences teaching in schools.
  3. While not all methodologies discussed in this blog allow us to determine cause and effect, but they have other strengths that go along with them.

When it comes to student learning, I feel strongly that it takes a diverse group of experts from different research backgrounds and various experiences teaching in schools.

With the amount of time and dedication that it takes to become an expert researcher and an expert teacher, it would be hard for one person to become both!

The same is true for research methodologies. There are pros and cons to each, and science is best served when we combine our efforts and tackle our questions from many different directions. Image from Pixabay In this spirit, in today's blog I am writing about the general research methodologies that might be used to help us understand student learning.

For each methodology, I describe what it is and how it might be used, as well as the strengths and weaknesses of the approach. This blog is a bit longer than our typical blogs because I'm tackling some big topics, but hopefully you'll find the discussion of various research methodologies, together in one place, as important as I do! Descriptive Research The main purpose of descriptive research is exactly what it sounds like it should be: There are a lot of individual approaches that fall under the descriptive research umbrella.

Here are a few: A researcher might conduct a case study on an individual who has a specific learning disability, or on a classroom that is engaging in a particular mode of instruction.

Observation research involves sitting back so to speak and watching how individuals interact in natural environments. A researcher might with permission from the school and parents of the children, of course watch a group of preschoolers through a 2-way mirror to see how the children interact with one another.

There is also a special type of observation research called participatory observation.

Assess the strengths and weaknesses of participant observation, as a research method

This method is used when it would be difficult or impossible to simply watch from a distance. You can think of this as going under cover, where the researcher joins a group to learn about the group. A classic example involves a researcher, Leon Festinger, who joined a cult who believed the world was going to be destroyed by a flood in the 1950s.

From this work, Festinger proposed Cognitive Dissonance Theory to read more, check out this page. Survey research is considered descriptive research. In this work, the researcher compiles a set of questions and asks people to answer these questions. The types of questions can vary. Some surveys might people to rate their feelings or beliefs on a scale from 1-7 also known as a "Likert" scale or answer yes-no questions. Some surveys might ask more open-ended questions, and there are many that utilize a mix of these types of questions.

If the researcher is asking a lot of open-ended questions, then we might call the research an interview, or a focus group if there are a few people discussing a topic and answering questions in a group. In this research, the participants may actually be guiding the direction of the research. There is another important distinction to be made under the descriptive research umbrella: In quantitative research, data is collected in the forms of numbers.

If a researcher asks a student to indicate on a scale from 1-10 how much they think they will remember from a lesson, then we are quantifying the student's perception of their own learning. In qualitative research, words are collected, and sometimes those words might be quantified in some way to use for statistical analysis.

If a researcher a discussion on the strengths and weaknesses of using participant observations to research teacher a a student to describe their learning process, or conducts in-depth interviews with teachers about classroom learning, then we are dealing with qualitative research.

Descriptive research can provide an in-depth view of any topic we might want to study, and the level of detail that we can find in descriptive research is extremely valuable. This is particularly true of descriptive research that is collected qualitatively.

In this form of research, we may find information that we never even knew to look for! This type of research can be used to create new research questions, or form hypotheses about cause and effect relationships though we cannot determine cause and effect from this research alone. Observation research has an added benefit of allowing us to see how things work in their natural environments. We cannot determine a cause and effect relationship from descriptive research.

For example, if a student talks about engaging with a particular learning strategy, and then provides an in-depth account of why they think it helped them learn, we cannot conclude that this strategy actually did help the student learn.

We also have to be very careful of reactivity in this type of research. Sometimes, people and animals too change their behavior if they know they're being observed. Similarly, in surveys we have to worry about participants providing responses that are considered desirable or in line with social norms. For example, if a parent is asked, "did you ever smoke while pregnant with your child? Correlational Research Correlational studies involve measuring two or more variables.

For that reason, this research is inherently quantitative. The researchers can then look at how related to variables are to one another. If two variables are related, or correlated, then we can use one variable to predict the value of another variable.

The greater the correlation, the greater accuracy our prediction will have. For example, correlational research might be able to tell us what factors at home are related to greater student learning in the classroom.

These factors a discussion on the strengths and weaknesses of using participant observations to research teacher a include things like eating a healthy breakfast, getting enough sleep, having access to a lot of books, feeling safe, etc. I often have my students think about car insurance to explain correlational research. Car insurance companies measure a lot of different variables, and then try to do their best to predict which customers are likely to cost them the most money e. They know that on average younger males are more likely to cost them money, and that drivers who have received speeding tickets are more likely to cost them money.

They also know that people living in certain areas are more likely to get into car accidents due to dense populations, or to have their car damaged while parked. Does this mean that a 16-year old boy who got a speeding ticket and lives in the city is definitely going to cause a car accident? No, of course not. Does this mean that getting a speeding ticket specifically causes later car accidents? Correlational research can help us understand the complex relationships between a lot of different variables.

If we measure these variables in realistic settings, then we can learn more about how the world really works. This type of research allows us to make predictions, and can tell us if two variables are not related, and thus searching for a cause-effect relationship between the two is a huge waste of time. Correlation is not the same as causation! Even if two variables are related to one another, that does not mean we can say for certain how the cause and effect relationship works.

Take caffeine average consumption and average test. Lets say we find that the two are correlated, where increased caffeine is related to higher test performance.

We cannot say that caffeine caused greater test performance, or that greater test performance caused greater caffeine consumption.

In reality, either of those could work! For example, students may drink more caffeine and this might lead them to perform better on tests. Or, the students who perform better on tests are then more likely to drink more caffeine. A third variable could be related to both of these as well! It could be that students who are more concerned about their grades might study more and achieve better test performance, and might also drink more caffeine to help them stay awake to study! We just don't know from the correlation alone, but knowing that the two variables are in some way related can be very useful information.

True Experiments True experiments involve manipulating or changing one variable and then measuring another. There are a few things that are required in order for research to be considered a true experiment.

Correlational Research

First, we need to randomly assign students to different groups. This random assignment helps create equivalent groups from the beginning.

Second, we need to change something for example, the type of learning strategy across the two groups, holding everything else as constant as possible. The key here is to make sure to isolate the thing we are changing, so that it is the only difference between the groups. We also need to make sure at least one of the groups serves as a control group, or a group that serves as a comparison. We need to make sure that the only thing being systematically changed is our manipulation.

Note, sometimes we can systematically manipulate multiple things at once, but these are more complicated designs. Finally, we then measure learning across the different groups. If we find that our manipulation led to greater learning compared to the control group, and we made sure to conduct the experiment properly with random assignment and appropriate controls, then we can say that our manipulation caused learning.

  1. This role also has disadvantages, in that there is a trade off between the depth of the data revealed to the researcher and the level of confidentiality provided to the group for the information they provide.
  2. It sometimes involves the researcher's working with and participating in everyday activities beside participants in their daily lives.
  3. Regarding developing models, he indicates that the aim is to construct a picture of the culture that reflects the data one has collected.

Taking the example from the correlational section, if we want to know whether drinking coffee increases test performance, then we need to randomly assign some students drink coffee and other students to drink a non-caffeinated beverage the control and then measure test performance.

And then, we repeat to be more confident in our conclusions! Usually, we're repeating experiments with little changes to continue obtaining new information.

This means that each individual participating in the experiment is serving as their own control. In these experiments, each person participates in all of the conditions. To make sure that the order of conditions or materials are not affecting the results, the researcher randomizes the order of conditions and materials in a process called counterbalancing. The researcher then randomly assigns different participants to different versions of the experiment, with the conditions coming up in different orders.

There are a number of ways to implement counterbalancing to maintain control in an experiment so that researchers can identify cause and effect relationships. The specifics of how to do this are not important for our purposes here. The important thing to note is that, even when participants are in within-subjects experiments and are participating in multiple learning conditions, in order to determine cause and effect we still need to maintain control and rule out alternate explanations for any findings e.

This type of experiment allows us to determine cause and effect relationships! True experiments are often be designed based on descriptive research or correlational research to determine underlying causes.