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Lessons: Research and Assessment Methods
Lesson 1 - Research Methods
There are three approaches to research: qualitative, quantitative, and mixed methods. A good
reference on research methods is Creswell and Creswell's Research Design, which summarizes
the differences as follows:
Qualitative research
• An approach for understanding the meaning individuals and groups ascribe to a human or social
problem
• Emerging questions
• Flexible written report
• Analysis building from particular data to general themes (inductive)
Quantitative research
• An approach for testing objective theories by examining the relationships among variables
(deductive)
• Numbered data which can be analyzed using statistical procedures
• Structured written report
Mixed methods research
• Collection of both qualitative and quantitative data
• Integrating the two forms of data
• May involve both philosophical assumptions and theoretical frameworks
• Assumes a more complete understanding of a research problem than using one of the
approaches alone
Lesson 2 - Qualitative Research
Qualitative research can not provide a generalized understanding (such as a population trend),
but it can provide a deeper understanding of a given topic.
Below are some common examples of qualitative research.
Case Study Method
A research method focusing on the study of a single case. Usually it is not designed to compare
one individual or group to another, although sometimes a case study may be included in
comparative analysis as a key or illustrative example.
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Comparative Analysis
Analysis where data from different settings or groups at the same point in time or from the
same settings or groups over a period of time are analyzed to identify similarities and
differences.
Discourse Analysis
A study of the way versions or the world, society, events, and psyche are produced in the use of
language and discourse. It is often concerned with the construction of subjects within various
forms of knowledge/power. Semiotics, deconstruction, and narrative analysis are forms of
discourse analysis.
e-Research
Also known as e-Science or e-Social Science, e-Research is the harnessing of any digital
technology to undertake and promote social research. This includes treating the digital sphere
as a site of research by examining social interaction in the e-infrastructure.
Ethnography
A multi-method qualitative (participant observation, interviews, discourse analyses of natural
language and personal documents) approach that studies people in their "...naturally occuring
settings or 'fields' by means of methods which capture their social meanings and ordinary
activities, involving the researcher participating directly in the setting..."
Field Research
Field research is when a researcher goes to observe an everyday event in the environment
where it occurs.
Grounded Theory
An inductive form of qualitative research where data collection and analysis are conducted
together. Theories remain grounded in the observations rather than generated in the abstract.
Grounded theory is an approach that develops the theory from the data collected, rather than
applying a theory to the data.
Narrative Analysis
Narrative analysis is a form of discourse analysis that seeks to study the textual devices at work
in the constructions of process or sequence within a text.
In narrative research, the respondent gives a detailed account of themselves and is encouraged to
tell their story rather than answer a predetermined list of questions. This method is more
successful when people are discussing a life changing event.
Analysis of the narrative tells the researcher about the person's understanding of the meaning of
events in their lives.
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Lesson 3 - Statistical Analysis
The statistics questions on the exam are primarily related to terminology rather than problemsolving.
The problem-solving questions are usually simple enough that they do not require a calculator.
Many candidates feel somewhat intimidated when preparing for the statistics portion of the AICP exam
because their math skills are not strong or it has been several years since they have taken a statistics
course, or both. A good statistics refresher that covers a lot of terminologies is the Statistics How To site.
A more extensive statistics course (more like a semester-length course) is offered by the
CarnegieMellon Open Learning Initiative. This may be helpful to refresh specific topics.
Generally speaking, there are three important steps in the statistical process: (1) collect data (e.g.,
surveys); (2) describe and summarize the distribution of the values in the data set; and (3) interpret by
means of inferential statistics and statistical modeling (i.e., draw general conclusions for the population
on the basis of the sample).
Candidates should be familiar with all of the terminology that is shown in bold text.
Before proceeding with statistics, it is important to consider the different types of measurement and
the different types of variables.
Types of measurement:
Nominal data are classified into mutually exclusive groups or categories and lack intrinsic order. A
zoning classification, social security number, and sex are examples of nominal data. The label of the
categories does not matter and should not imply any order. So, even if one category might be labeled as
1 and the other as 2, those labels can be switched.
Ordinal data are ordered categories implying a ranking of the observations. Even though ordinal data
may be given numerical values, such as 1, 2, 3, and 4, the values themselves are meaningless. Only the
rank counts. It would be incorrect to infer, for example, that 4 is twice 2, despite the temptation.
Examples of ordinal data include letter grades, suitability for development, and response scales on a
survey (e.g., 1 through 5).
Interval data has an ordered relationship where the difference between the scales has a meaningful
interpretation. The typical example of interval data is temperature, where the difference between 40
and 30 degrees is the same as between 30 and 20 degrees, but 20 degrees is not twice as cold as 40
degrees.
Ratio data is the gold standard of measurement, where both absolute and relative differences have a
meaning. The classic example of ratio data is a distance measure, where the difference between 40 and
30 miles is the same as the difference between 30 and 20 miles, and in addition, 40 miles is twice as far
as 20 miles.
Types of Variables:
Lessons: Research and Assessment Methods
Lesson 1 - Research Methods
There are three approaches to research: qualitative, quantitative, and mixed methods. A good
reference on research methods is Creswell and Creswell's Research Design, which summarizes
the differences as follows:
Qualitative research
• An approach for understanding the meaning individuals and groups ascribe to a human or social
problem
• Emerging questions
• Flexible written report
• Analysis building from particular data to general themes (inductive)
Quantitative research
• An approach for testing objective theories by examining the relationships among variables
(deductive)
• Numbered data which can be analyzed using statistical procedures
• Structured written report
Mixed methods research
• Collection of both qualitative and quantitative data
• Integrating the two forms of data
• May involve both philosophical assumptions and theoretical frameworks
• Assumes a more complete understanding of a research problem than using one of the
approaches alone
Lesson 2 - Qualitative Research
Qualitative research can not provide a generalized understanding (such as a population trend),
but it can provide a deeper understanding of a given topic.
Below are some common examples of qualitative research.
Case Study Method
A research method focusing on the study of a single case. Usually it is not designed to compare
one individual or group to another, although sometimes a case study may be included in
comparative analysis as a key or illustrative example.
, lOMoARcPSD| 63525276
Comparative Analysis
Analysis where data from different settings or groups at the same point in time or from the
same settings or groups over a period of time are analyzed to identify similarities and
differences.
Discourse Analysis
A study of the way versions or the world, society, events, and psyche are produced in the use of
language and discourse. It is often concerned with the construction of subjects within various
forms of knowledge/power. Semiotics, deconstruction, and narrative analysis are forms of
discourse analysis.
e-Research
Also known as e-Science or e-Social Science, e-Research is the harnessing of any digital
technology to undertake and promote social research. This includes treating the digital sphere
as a site of research by examining social interaction in the e-infrastructure.
Ethnography
A multi-method qualitative (participant observation, interviews, discourse analyses of natural
language and personal documents) approach that studies people in their "...naturally occuring
settings or 'fields' by means of methods which capture their social meanings and ordinary
activities, involving the researcher participating directly in the setting..."
Field Research
Field research is when a researcher goes to observe an everyday event in the environment
where it occurs.
Grounded Theory
An inductive form of qualitative research where data collection and analysis are conducted
together. Theories remain grounded in the observations rather than generated in the abstract.
Grounded theory is an approach that develops the theory from the data collected, rather than
applying a theory to the data.
Narrative Analysis
Narrative analysis is a form of discourse analysis that seeks to study the textual devices at work
in the constructions of process or sequence within a text.
In narrative research, the respondent gives a detailed account of themselves and is encouraged to
tell their story rather than answer a predetermined list of questions. This method is more
successful when people are discussing a life changing event.
Analysis of the narrative tells the researcher about the person's understanding of the meaning of
events in their lives.
, lOMoARcPSD| 63525276
Lesson 3 - Statistical Analysis
The statistics questions on the exam are primarily related to terminology rather than problemsolving.
The problem-solving questions are usually simple enough that they do not require a calculator.
Many candidates feel somewhat intimidated when preparing for the statistics portion of the AICP exam
because their math skills are not strong or it has been several years since they have taken a statistics
course, or both. A good statistics refresher that covers a lot of terminologies is the Statistics How To site.
A more extensive statistics course (more like a semester-length course) is offered by the
CarnegieMellon Open Learning Initiative. This may be helpful to refresh specific topics.
Generally speaking, there are three important steps in the statistical process: (1) collect data (e.g.,
surveys); (2) describe and summarize the distribution of the values in the data set; and (3) interpret by
means of inferential statistics and statistical modeling (i.e., draw general conclusions for the population
on the basis of the sample).
Candidates should be familiar with all of the terminology that is shown in bold text.
Before proceeding with statistics, it is important to consider the different types of measurement and
the different types of variables.
Types of measurement:
Nominal data are classified into mutually exclusive groups or categories and lack intrinsic order. A
zoning classification, social security number, and sex are examples of nominal data. The label of the
categories does not matter and should not imply any order. So, even if one category might be labeled as
1 and the other as 2, those labels can be switched.
Ordinal data are ordered categories implying a ranking of the observations. Even though ordinal data
may be given numerical values, such as 1, 2, 3, and 4, the values themselves are meaningless. Only the
rank counts. It would be incorrect to infer, for example, that 4 is twice 2, despite the temptation.
Examples of ordinal data include letter grades, suitability for development, and response scales on a
survey (e.g., 1 through 5).
Interval data has an ordered relationship where the difference between the scales has a meaningful
interpretation. The typical example of interval data is temperature, where the difference between 40
and 30 degrees is the same as between 30 and 20 degrees, but 20 degrees is not twice as cold as 40
degrees.
Ratio data is the gold standard of measurement, where both absolute and relative differences have a
meaning. The classic example of ratio data is a distance measure, where the difference between 40 and
30 miles is the same as the difference between 30 and 20 miles, and in addition, 40 miles is twice as far
as 20 miles.
Types of Variables:
