Week 10: Historiography
Vitanza divides the realm of historiographies into three main categories. The first is the Traditional Historiography which consists of what most people would think of when they hear "historiography." It is time-focused and relies entirely, or at least heavily on the collection and representation of "facts." Vitanza’s other two categories of historiographies are both in opposition to the traditional view of historiographies. The more common of these is the Revisionist Historiography which seeks to uncover more truth or see how facts were distorted or ignored in a traditional historiography. There are many points during the occurrence, recording, research, and representation of facts during which the “truth” can be distorted. Vitanza’s third category is called Sub/Versive Historiography. This kind of historiography is similar to reversionary historiography, but is more extreme. It focuses on the inability of traditional historiography to ever “know” and is open to any possible version of the past.
Corbetts’s What Classical Rhetoric Has to Offer the Teacher and the Student of Business and Professional Writing, takes a very strong traditional view of historiography. This article is a recounting of how business communication has misused or ignored the field of rhetoric.
Classical Rhetoric’s 4 components:
1. A speaker or writer
2. Listeners
3. A message
4. A reality or universe that the message is discussing
Corbett makes the following claims about business writing:
The audience gets the most attention (as it should)
Business writing is plain (not moderate or grand)
Sayings actually make writing clearer
Establishing ethos is obviously important (tone, prononcio, acio)
If business writers were aware of how classical rhetoricians viewed style, they would be more interested in it.
In the written form, neatness, correct spelling and grammar are most important in business writing.
“For a long time, business writing” has not paid attention to ethos, until recently
Classical Rhetoric pays more attention to emotional appeals (Aristotle) and has a different view of ethos (more on writing and less on resume of speaker)
“All of us need help honing our communication skills”
Zappen’s Francis Bacon and the Historiography of Scientific Rhetoric is an example of revisionary historiography. This article argues that Francis Bacon’s writing has been used inappropriately, although successfully to argue different (and often competing) points of view. Zappen’s appreciation of the flexibility of writing (and presumably the flexibility of researched facts, such as statistics) would put him in the revisionary or perhaps even sub/versive camp.
Howard’s Who 'Owns' Electronic Texts? is another example of traditional historiography. Part of the paper focuses on the history of copyright law, after which the author poses questions about the effects of new electronic media and techniques on future copyright law.
Monday, April 6, 2009
Saturday, March 28, 2009
Week 9: Experiements
Lauer & Asher: Quantitative Descriptive Studies
Quantitative Descriptive research goes beyond case studies and isolates the most important variables and then attempts to quantify these. There are no control groups or treatments.
These studies need a larger subject group than ethnographies. Variable selection (choosing most important variables to quantify) is the most important part of this research. Alternative hypotheses are also used . Researchers look at the most important variables and find significance and how variables interact. Quantitative Descriptive Studies help to build theories, but they are usually inadequate to claim cause and effect relationships.
Study #1 A description of the composing process of freshmen
Study #2 Writing in a non academic setting
Study #3 Measuring growth in English
Farber: Popularizing Nanoscience
This research looked at how Nanoscience is discussed in popular media. Its Hypothesis and its findings showed that articles about nanoscience were socially adapted and used and that the articles created “personas” of the field.
Research Question: How has the field of Nanoscience been represented in the media from 1986 to 1999?
Subject Selection: Articles were selected by a search in a multi university database of articles and magazines.
Data Collection: The researcher looked for mentions of “nanoscience” and several other key categories in the field.
Data Analysis: The researcher looked at each articles discussion using the discursive categories of Theme, Rheme, Topic, and Representation
Golen: A Factor Analysis of Barriers to Effective Listening
This article found that there were 6 major barriers to effective listening. Age didn’t matter, but gender changed the impact of two of these barriers.
Research Question: What barriers do students perceive as most frequent? What are the listening barrier factors among students? How do these barriers differ among demographics?
Subject Selection: All subjects came from a large business lecture class after a lecture about overcoming listening barriers. This class broke into 33 sections. 10 of these sections were randomly selected for further study.
Data Collection: The barriers were obtained by finding the most common barriers as evidenced through a literature review, then edited through a pilot study and with professor’s and student’s suggestions. Next, the students’ age, major, and sex were measured across each barrier.
Data Analysis: Listening barriers were compiled into barrier “factors” in order for the data to be analyzed effectively. “Laziness” defined by students’ lack of effort put into listening to a complex topic was the most frequently cited barrier factor by students.
Lauer & Asher: True Experiments
Uses treatments and control groups. Randomization is an important part of the research design. A Hypothesis is claimed. Pretests for each group can ensure that the groups were randomized.
type 1 error – research says something was stat sig, but this was only due to chance
type 2 error – researcher says something wasn’t stat sig, but it really was
internal validity threats – instrumentation, measurement, regression towards the mean, mortality, maturation, selection, history, instability, psych threats
external validity – the “generalizableness” of research
Lauer & Asher: Quasi Experiments
How is it not a true experiment? There is no randomization, it must have a pretest to see that groups are equal, and there must be research design hypothesis to account for ineffective treatments.
2 types: strong (high equality between groups) and weak quasi experiments
Other types of quasi experiments: time-interrupted, repeated treatment, regression-discontinuity
Quasi experiments can produce strong results when true experiments are not possible
Carroll et al.: The Minimal Manual
This research showed that shorter manuals often result in better understanding of the material. The researchers first tried to design a shorter, minimal handbook and then tested to see if users were able to improve their learning of the software when using it.
Research Question: Would a minimal manual help users learn and complete tasks easier?
Experiment 1:
Subject Selection: Used 19 people who had experience with routine office work and little computer experience
Data Collection: In a realistic office environment, subjects were asked to complete 8 tasks with the software in able to learn it
Data Analysis: Researchers compared learners with a standard software training manual with learners with a designed minimal manual. Subjects were scored on the completion of tasks and how quickly they were able to do so.
Experiment 2:
Subject Selection: 32 subjects were used, 8 for each category. Again, they wanted subjects with routine office work experience and little computer experience.
Data Collection: Used a 2 x 2 research design to compare a standard manual with a minimal manual, each with w types of learning: learning by the book and learning with tasks approaches. Learners were monitored while they attempted to complete 6 tasks. They were encouraged to think aloud.
Data Analysis: Subjects were scored on the successful completion of tasks and how quickly they were able to do so. Researchers also measured how quickly learners started the system and what errors they encountered. A taxonomy of error types was developed during the pilot study to be able to categorize these errors.
Notarantonio & Cohen: The Effects of Open and Dominant Communication Styles on Perceptions of the Sales Interaction
Research Question: How would students rate sales pitches from salespeople with low or high dominance and high or low openness?
Subject Selection:
80 Subjects in this research were chosen from business school.
Data Collection:
Videotaped questionnaires and subject’s self reports on their communication styles were used
Data Analysis:
Answers were compiled together and analyzed based on which video they watched. 2 x 2 research design showed that high-low combinations worked, but salespeople with either a high-high, or a low-low combination of dominance and openness were viewed unfavorably by students
Kroll: Explaining how to Play a Game: The Development of Informative Writing Skills
Research Question: How do students of various ages use writing to explain a task?
Subject Selection: Students in grades 5, 7, 9, 11, and college freshmen were chosen as subjects.
Data Collection: Subjects were shown a video explaining how to play a game and then were told to write explanations of the game. 2 researchers evaluated the student’s explanations, with one of these being aware of the study.
Data analysis: Scores for the student’s explanatory ability were analyzed based on their grade level. Researchers also looked at the student’s ability to explain 10 different elements of the game.
Quantitative Descriptive research goes beyond case studies and isolates the most important variables and then attempts to quantify these. There are no control groups or treatments.
These studies need a larger subject group than ethnographies. Variable selection (choosing most important variables to quantify) is the most important part of this research. Alternative hypotheses are also used . Researchers look at the most important variables and find significance and how variables interact. Quantitative Descriptive Studies help to build theories, but they are usually inadequate to claim cause and effect relationships.
Study #1 A description of the composing process of freshmen
Study #2 Writing in a non academic setting
Study #3 Measuring growth in English
Farber: Popularizing Nanoscience
This research looked at how Nanoscience is discussed in popular media. Its Hypothesis and its findings showed that articles about nanoscience were socially adapted and used and that the articles created “personas” of the field.
Research Question: How has the field of Nanoscience been represented in the media from 1986 to 1999?
Subject Selection: Articles were selected by a search in a multi university database of articles and magazines.
Data Collection: The researcher looked for mentions of “nanoscience” and several other key categories in the field.
Data Analysis: The researcher looked at each articles discussion using the discursive categories of Theme, Rheme, Topic, and Representation
Golen: A Factor Analysis of Barriers to Effective Listening
This article found that there were 6 major barriers to effective listening. Age didn’t matter, but gender changed the impact of two of these barriers.
Research Question: What barriers do students perceive as most frequent? What are the listening barrier factors among students? How do these barriers differ among demographics?
Subject Selection: All subjects came from a large business lecture class after a lecture about overcoming listening barriers. This class broke into 33 sections. 10 of these sections were randomly selected for further study.
Data Collection: The barriers were obtained by finding the most common barriers as evidenced through a literature review, then edited through a pilot study and with professor’s and student’s suggestions. Next, the students’ age, major, and sex were measured across each barrier.
Data Analysis: Listening barriers were compiled into barrier “factors” in order for the data to be analyzed effectively. “Laziness” defined by students’ lack of effort put into listening to a complex topic was the most frequently cited barrier factor by students.
Lauer & Asher: True Experiments
Uses treatments and control groups. Randomization is an important part of the research design. A Hypothesis is claimed. Pretests for each group can ensure that the groups were randomized.
type 1 error – research says something was stat sig, but this was only due to chance
type 2 error – researcher says something wasn’t stat sig, but it really was
internal validity threats – instrumentation, measurement, regression towards the mean, mortality, maturation, selection, history, instability, psych threats
external validity – the “generalizableness” of research
Lauer & Asher: Quasi Experiments
How is it not a true experiment? There is no randomization, it must have a pretest to see that groups are equal, and there must be research design hypothesis to account for ineffective treatments.
2 types: strong (high equality between groups) and weak quasi experiments
Other types of quasi experiments: time-interrupted, repeated treatment, regression-discontinuity
Quasi experiments can produce strong results when true experiments are not possible
Carroll et al.: The Minimal Manual
This research showed that shorter manuals often result in better understanding of the material. The researchers first tried to design a shorter, minimal handbook and then tested to see if users were able to improve their learning of the software when using it.
Research Question: Would a minimal manual help users learn and complete tasks easier?
Experiment 1:
Subject Selection: Used 19 people who had experience with routine office work and little computer experience
Data Collection: In a realistic office environment, subjects were asked to complete 8 tasks with the software in able to learn it
Data Analysis: Researchers compared learners with a standard software training manual with learners with a designed minimal manual. Subjects were scored on the completion of tasks and how quickly they were able to do so.
Experiment 2:
Subject Selection: 32 subjects were used, 8 for each category. Again, they wanted subjects with routine office work experience and little computer experience.
Data Collection: Used a 2 x 2 research design to compare a standard manual with a minimal manual, each with w types of learning: learning by the book and learning with tasks approaches. Learners were monitored while they attempted to complete 6 tasks. They were encouraged to think aloud.
Data Analysis: Subjects were scored on the successful completion of tasks and how quickly they were able to do so. Researchers also measured how quickly learners started the system and what errors they encountered. A taxonomy of error types was developed during the pilot study to be able to categorize these errors.
Notarantonio & Cohen: The Effects of Open and Dominant Communication Styles on Perceptions of the Sales Interaction
Research Question: How would students rate sales pitches from salespeople with low or high dominance and high or low openness?
Subject Selection:
80 Subjects in this research were chosen from business school.
Data Collection:
Videotaped questionnaires and subject’s self reports on their communication styles were used
Data Analysis:
Answers were compiled together and analyzed based on which video they watched. 2 x 2 research design showed that high-low combinations worked, but salespeople with either a high-high, or a low-low combination of dominance and openness were viewed unfavorably by students
Kroll: Explaining how to Play a Game: The Development of Informative Writing Skills
Research Question: How do students of various ages use writing to explain a task?
Subject Selection: Students in grades 5, 7, 9, 11, and college freshmen were chosen as subjects.
Data Collection: Subjects were shown a video explaining how to play a game and then were told to write explanations of the game. 2 researchers evaluated the student’s explanations, with one of these being aware of the study.
Data analysis: Scores for the student’s explanatory ability were analyzed based on their grade level. Researchers also looked at the student’s ability to explain 10 different elements of the game.
Saturday, February 28, 2009
Week 8: Ethnographies
Blog Question: What distinguishes ethnographies from case studies, how does “triangulation” impact data collection and analysis, and what must ethnographers do to ensure their work is both reliable and valid?
Ethnographies require a much longer and deeper immersion with the subjects in their natural environment, but Case Studies are often performed in a slightly more sterile environment, or as broader and shallower research with individuals or groups. As a result ethnographers are faced with a mountain of data about a fairly narrow topic. Case Studies look for variables, whereas Ethnographies try to capture the process and the “feel” of people and their environment and look to find similarities and trends in the data.
Ethnographies use triangulation to go through and notice consistencies within the data to find the “truth” through a variety of research methods, subjects, on across time. Triangulation is used to ensure that ethnographer's findings are both reliable and valid, although it does have limitations.
Dohney & Farhina: Writing in an Emerging Organization
Research Question: How do writing processes shape the organizational structure of an emerging organization?
Subject Selection: The software company was picked because it was an emerging organization.
Data Collection: The ethnographer visited the company 3 to 5 times a week for 8 months and attended meetings. They collected field notes, tape-recorded meetings, open ended interviews, and discourse-based interviews.
Data Analysis: Data analysis followed the constant comparison method. The ethnographer developed categories for different events and linked them to form major themes for the study.
Beaufort: Learning the Trade
Research Question: What are the ways in which particular configurations of roles aid or hinder a writer’s socialization process in becoming a productive member of a community of practice? What differentiated simpler from more complex writing tasks? What determined writer’s social roles in this community? What methods of socialization were used for writers new to this organization and to what effect?
Subject Selection: The ethnographer chose the research site because of an overriding concern with issues of transfer of learning; in particular she wanted to examine the contrasts between academic and workplace settings for composition.
Data Collection: The ethnographer interviewed each woman almost weekly, kept records of their writing, and attended business and informal meetings between them and their colleagues.
Data Analysis: The ethnographer looked at field notes, transcripts, and writing samples for patterns and themes in relation to social roles for texts and for writers within the discourse community of the company.
Sheeny: The Social Life of an Essay
Research Question: Seeks to understand the standardization processes involved in the writing done by a class of seventh grade students, half of whom did not do well in school or on tests.
Subject Selection: The researcher picked seventh graders in a science classroom at the only middle school in a large city. Many of these students had a history of performing poorly in school and many were from underprivileged backgrounds.
Data Collection: The researcher collected data from two focus groups and from the class as a whole. She collected field notes, voice recordings, community surveys, and samples of the students writing; both from class assignments and from journals.
Data Analysis: The researcher didn’t really seem to have a very concrete idea of data analysis. She used triangulation to determine the results from her data collection.
Ellis: Shattered Lives
This research explored disastrous events using the author's own experience on a flight on September 11th, and used the methods of autoethnography to do so. The researcher did not explain subject selection, data collection, and data analysis.
Anderson: Analytic Autoethnography
This article did not seem to have as much focus on research as the other research papers we have read this week. The author discusses the history of autoethnography, it’s impacts on research, and it’s virtues and limitations. The author argues that autoethnography fits well into the larger group of ethnography and uses similar techniques of the researcher being actively involved with the subjects.
Ethnographies require a much longer and deeper immersion with the subjects in their natural environment, but Case Studies are often performed in a slightly more sterile environment, or as broader and shallower research with individuals or groups. As a result ethnographers are faced with a mountain of data about a fairly narrow topic. Case Studies look for variables, whereas Ethnographies try to capture the process and the “feel” of people and their environment and look to find similarities and trends in the data.
Ethnographies use triangulation to go through and notice consistencies within the data to find the “truth” through a variety of research methods, subjects, on across time. Triangulation is used to ensure that ethnographer's findings are both reliable and valid, although it does have limitations.
Dohney & Farhina: Writing in an Emerging Organization
Research Question: How do writing processes shape the organizational structure of an emerging organization?
Subject Selection: The software company was picked because it was an emerging organization.
Data Collection: The ethnographer visited the company 3 to 5 times a week for 8 months and attended meetings. They collected field notes, tape-recorded meetings, open ended interviews, and discourse-based interviews.
Data Analysis: Data analysis followed the constant comparison method. The ethnographer developed categories for different events and linked them to form major themes for the study.
Beaufort: Learning the Trade
Research Question: What are the ways in which particular configurations of roles aid or hinder a writer’s socialization process in becoming a productive member of a community of practice? What differentiated simpler from more complex writing tasks? What determined writer’s social roles in this community? What methods of socialization were used for writers new to this organization and to what effect?
Subject Selection: The ethnographer chose the research site because of an overriding concern with issues of transfer of learning; in particular she wanted to examine the contrasts between academic and workplace settings for composition.
Data Collection: The ethnographer interviewed each woman almost weekly, kept records of their writing, and attended business and informal meetings between them and their colleagues.
Data Analysis: The ethnographer looked at field notes, transcripts, and writing samples for patterns and themes in relation to social roles for texts and for writers within the discourse community of the company.
Sheeny: The Social Life of an Essay
Research Question: Seeks to understand the standardization processes involved in the writing done by a class of seventh grade students, half of whom did not do well in school or on tests.
Subject Selection: The researcher picked seventh graders in a science classroom at the only middle school in a large city. Many of these students had a history of performing poorly in school and many were from underprivileged backgrounds.
Data Collection: The researcher collected data from two focus groups and from the class as a whole. She collected field notes, voice recordings, community surveys, and samples of the students writing; both from class assignments and from journals.
Data Analysis: The researcher didn’t really seem to have a very concrete idea of data analysis. She used triangulation to determine the results from her data collection.
Ellis: Shattered Lives
This research explored disastrous events using the author's own experience on a flight on September 11th, and used the methods of autoethnography to do so. The researcher did not explain subject selection, data collection, and data analysis.
Anderson: Analytic Autoethnography
This article did not seem to have as much focus on research as the other research papers we have read this week. The author discusses the history of autoethnography, it’s impacts on research, and it’s virtues and limitations. The author argues that autoethnography fits well into the larger group of ethnography and uses similar techniques of the researcher being actively involved with the subjects.
Saturday, February 21, 2009
Week 7: Surveys
Surveys are a commonly used research technique and are relatively inexpensive to implement. If used correctly, surveys can be generalizable and can allow researchers to gather information about a very large population. To do so, the sample chosen from a population must be randomized. Even when choosing a random sample, a large enough sample size must be found for the results to be reliable. According to Lauer and Asher, "The precision of information is directly related to the sample size." There are several methods to approximate a random sample group including systematic (e.g. every 10th subject is chosen), quota (e.g. including 10% of a certain group in your sample, if the entire population has 10% of that same group), stratified sampling (e.g. focusing your sample on one specific group, but still studying the larger population in which that group resides), and cluster sampling (e.g. choosing an entire classroom to study a population of the whole school rather than randomly selecting students across the school).
Another important part of surveys that researchers must keep in mind is to make sure that they have specific definitions for their sample, units, population, and their measurement methods. If possible, it may be a good idea to use a pre-existing, proven measurement technique. Lauer and Asher also warn that a low response rate can make a study's results unreliable and that if a new measurement method (a new survey) does need to be created, it should be tested on a representative group before hand, so that it can be adjusted for the best results in the real survey.
This chapter is especially useful to the research group that I am currently in for this class. As of right now, our plan is to survey undergraduate students at Clemson to get their opinions on possible methods of communicating to students that they have an upcoming tuition bill. Before reading this chapter, I did not think of the fact that a low response rate would lead to unreliable results, but just thought that it was an unavoidable, but unimportant part of the surveying process. Additionally, I did not think it was necessary to find a sample group from our sample group to do a test run of our initial survey, but upon reading this, I can certainly see the importance of such an activity. We would hate to either spend hours collecting survey answers or even worse, spend our one chance at getting the university to send out a survey on our behalf, only to find out that our questions were not properly worded. These issues pose even more challenges to our project in class. Frankly, I'm a little worried, but this chapter was certainly necessary for our group to read before conducting our research.
Another important part of surveys that researchers must keep in mind is to make sure that they have specific definitions for their sample, units, population, and their measurement methods. If possible, it may be a good idea to use a pre-existing, proven measurement technique. Lauer and Asher also warn that a low response rate can make a study's results unreliable and that if a new measurement method (a new survey) does need to be created, it should be tested on a representative group before hand, so that it can be adjusted for the best results in the real survey.
This chapter is especially useful to the research group that I am currently in for this class. As of right now, our plan is to survey undergraduate students at Clemson to get their opinions on possible methods of communicating to students that they have an upcoming tuition bill. Before reading this chapter, I did not think of the fact that a low response rate would lead to unreliable results, but just thought that it was an unavoidable, but unimportant part of the surveying process. Additionally, I did not think it was necessary to find a sample group from our sample group to do a test run of our initial survey, but upon reading this, I can certainly see the importance of such an activity. We would hate to either spend hours collecting survey answers or even worse, spend our one chance at getting the university to send out a survey on our behalf, only to find out that our questions were not properly worded. These issues pose even more challenges to our project in class. Frankly, I'm a little worried, but this chapter was certainly necessary for our group to read before conducting our research.
Thursday, February 12, 2009
Week 6: Case Studies
Case studies are appropriate for many different situations. This kind of qualitative descriptive research tries to describe the overall situation and attempts to define the variables involved. Quantitative research, on the other hand, attempts to find relationships among variables. Oftentimes, qualitative research will discover what variables need to be further explored in additional qualitative or quantitative research. Case studies are appropriate for situations that are complex or highly contextual.
Case study subjects are often chosen from a group of volunteers. Researchers should attempt to gather subjects with varied backgrounds. These background differences between subjects can be differences in sex, race, experience level, knowledge level, socioeconomic status, etc. Unlike quantitative research, qualitative research does not seek to control any of these variables that may effect outcomes.
In case studies, data is collected in a variety of ways. The researcher's memory, notes, tape recordings, interviews, subjects talking aloud through their thought processes, relevant records and past research can all be used in case studies. All of these methods can bring new information and insights into the research, but each new method carries additional risk of not being seen as valid or reliable.
Case studies don't seek to find "facts" in the same sense that quantitative research does. Instead they "report results in the form of extensive descriptions, conclusions, hypotheses, and questions for further research." Generalizations are limited because researchers do not want to assume that the results of a case study will apply to every case. But when supported by other research, case studies can support certain findings and results.
Case study subjects are often chosen from a group of volunteers. Researchers should attempt to gather subjects with varied backgrounds. These background differences between subjects can be differences in sex, race, experience level, knowledge level, socioeconomic status, etc. Unlike quantitative research, qualitative research does not seek to control any of these variables that may effect outcomes.
In case studies, data is collected in a variety of ways. The researcher's memory, notes, tape recordings, interviews, subjects talking aloud through their thought processes, relevant records and past research can all be used in case studies. All of these methods can bring new information and insights into the research, but each new method carries additional risk of not being seen as valid or reliable.
Case studies don't seek to find "facts" in the same sense that quantitative research does. Instead they "report results in the form of extensive descriptions, conclusions, hypotheses, and questions for further research." Generalizations are limited because researchers do not want to assume that the results of a case study will apply to every case. But when supported by other research, case studies can support certain findings and results.
Saturday, February 7, 2009
Week 5: Internet Research - Bryan Ricke
Question: How does conducting research on the Internet impact the ways that researchers must deal with human subjects?
As with anything else that the Internet has touched, it has fundamentally altered research with human subjects via the Internet. In some ways this has opened up new opportunities, both in the availability of research subjects and in research design. It can also cause a few problems. Some of the opportunities are for researchers to be able to inexpensively contact a large number of people, for researchers to be able to reach subjects outside of their local community, and for researchers to see how subjects behave while on the Internet and how this may be different than their behavior in the "real world." The problems it may cause are just as prevalent.
Researchers need to decide whether or not an item posted on the Internet constitutes a "public" document and can therefore be used for research. This is a tricky subject because not everything posted on the Internet is meant to be seen by everyone, but at least most posters know that it may be possible for the document to be seen by a large number of people. Unless a document is posted on a password-protected website, I can't really see how the author can expect that it couldn't be studied and used for social or behavioral research. Furthermore researchers must document these findings anonymously, so even should someone post something embarrassing on the Internet and not want it to be further dissected by a researcher, at least their name will be left out of the paper.
Another possible problem is that it is difficult to tell if a subject is old enough and has the mental capacity to be participating in the research effectively. Not only could this give the researcher incorrect results, but it bring the researcher a host of other problems, calling in to question their research design. Furthermore it is more difficult to tell if a subject is simply lying about their gender, age, or any other information that may be relevant to the test. This of course could be a problem in traditional research designs as well.
Using the Internet as a research tool can be very powerful as it allows researchers to perform research on larger groups of people, and can find subjects outside of their area. However it can introduce more variables and other problems into the research. It must be planned and implemented carefully.
As with anything else that the Internet has touched, it has fundamentally altered research with human subjects via the Internet. In some ways this has opened up new opportunities, both in the availability of research subjects and in research design. It can also cause a few problems. Some of the opportunities are for researchers to be able to inexpensively contact a large number of people, for researchers to be able to reach subjects outside of their local community, and for researchers to see how subjects behave while on the Internet and how this may be different than their behavior in the "real world." The problems it may cause are just as prevalent.
Researchers need to decide whether or not an item posted on the Internet constitutes a "public" document and can therefore be used for research. This is a tricky subject because not everything posted on the Internet is meant to be seen by everyone, but at least most posters know that it may be possible for the document to be seen by a large number of people. Unless a document is posted on a password-protected website, I can't really see how the author can expect that it couldn't be studied and used for social or behavioral research. Furthermore researchers must document these findings anonymously, so even should someone post something embarrassing on the Internet and not want it to be further dissected by a researcher, at least their name will be left out of the paper.
Another possible problem is that it is difficult to tell if a subject is old enough and has the mental capacity to be participating in the research effectively. Not only could this give the researcher incorrect results, but it bring the researcher a host of other problems, calling in to question their research design. Furthermore it is more difficult to tell if a subject is simply lying about their gender, age, or any other information that may be relevant to the test. This of course could be a problem in traditional research designs as well.
Using the Internet as a research tool can be very powerful as it allows researchers to perform research on larger groups of people, and can find subjects outside of their area. However it can introduce more variables and other problems into the research. It must be planned and implemented carefully.
Thursday, January 29, 2009
Week 4: Research Methods
There is a great deal of difference between qualitative and quantitative design, although there are some research methods that take a little bit from each category. Qualitative studies include ethnographies, case studies, and descriptive studies. This type of research aims to investigate and describe the process of the situation, be it an participant ethnography, or a world-wide internet survey. Qualitative researchers collect 6 sources of data: documentation, archival records, interviews/surveys, direct observation, participant observation, and physical artifacts. Quantitative research includes correlational research and experimental research, of which there are several types: true experiments, quasi-experiments, and many, many others. Quantitative research frequently uses randomly-selected sample groups and seeks to control as many variables as possible, especially in true experiments. It also poses a questions and a hypothesis as the basis of the research. Statistics come into play much more in quantitative research, including descriptive stats (mean, median, mode) and inferential stats (chi squared, t test, f test.) An important point from Morgan is that it is not useful to argue over the merits of one or the other, but that researchers should instead choose the method that best answers their particular question. Qualitative research designs are most useful in describing in-depth situations as they occur in “natural” settings -as things occur in “real-life.” Quantitative research designs are most useful in describing correlational and causal relationships between different phenomena or variables.
There is a clear difference between Validity and Reliability, although the two terms are often used incorrectly when talking about research. They are both forms of accuracy, but measure different things. Validity is the degree to which the researcher measures what (s)he claims to measure. Reliability is the external and internal consistency of the measurements. Both are needed for the research to have credibility (of the research methods), transferability (of the results to a new researcher), dependability (explaining results), and confirmability (repeatable results from a similar test). These four aspects can be analyzed to gauge the effectiveness of the research.
Statistical probability is a way of looking at research results. Total probabilities should always equal 1.0 and the results often form a bell-shaped distribution. Probability is a useful method to infer population distributions from the actual sample results. Significance is the degree of probability of the result occurring strictly from chance. In a well-controlled test, if a certain result is more than this- that is if it is statistically significant- then it can be inferred that the result is caused by the independent variable and not from random chance. Statistical significance is the difference between correlation and causation.
There is a clear difference between Validity and Reliability, although the two terms are often used incorrectly when talking about research. They are both forms of accuracy, but measure different things. Validity is the degree to which the researcher measures what (s)he claims to measure. Reliability is the external and internal consistency of the measurements. Both are needed for the research to have credibility (of the research methods), transferability (of the results to a new researcher), dependability (explaining results), and confirmability (repeatable results from a similar test). These four aspects can be analyzed to gauge the effectiveness of the research.
Statistical probability is a way of looking at research results. Total probabilities should always equal 1.0 and the results often form a bell-shaped distribution. Probability is a useful method to infer population distributions from the actual sample results. Significance is the degree of probability of the result occurring strictly from chance. In a well-controlled test, if a certain result is more than this- that is if it is statistically significant- then it can be inferred that the result is caused by the independent variable and not from random chance. Statistical significance is the difference between correlation and causation.
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