A complete but to-the-point summary, including images and examples to better understand the material. It contains all 12 chapters of 'Research Methods - The Essential Knowledge Base', which is exam material for the course 'Academic Project' of the Premaster Business Administration at the UvA. Good ...
TEST BANK FOR RESEARCH METHODS THE ESSENTIAL KNOWLEDGE BASE 2ND EDITION BY WILLIAM TROCHIM (ISBN 978-1133954774)
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Pre-master Business Administration
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CHAPTER 1 FOUNDATIONS OF RESEARCH METHODS
Research = type of systematic investigation that is empirical in nature and designed to contribute to
public knowledge.
Volume → social research = have to do with our societies, the things we do, how we interact,
how we live, how we feel and how we see ourselves.
✓ Systematic investigation. Research is a conscious effort to concentrate our thinking, to do
it in a rational, careful manner.
✓ Empirical endeavour. Collecting data based upon systematic observation to use in
decision making. (Based on observations and measurements of reality)
✓ Public effort. Research that contribute to a broader base of knowledge than just your
own. Understandable for people.
Research enterprise = the macro-level effort to accumulate knowledge across multiple empirical
systematic public research projects = the broader effort that each research project contributes to.
We accumulate knowledge with the idea that it may contribute some day to something we can use.
When we move research from discovery to practice, we can say we are translating research into
practice.
Translational research = the systematic effort to move research from initial discovery to practice and
ultimately to impacts on our lives.
Research-practice continuum (within the research enterprise) is the process of moving from an initial
research idea or discovery to practice, and the potential for the idea to influence our lives or world.
▪ Basic research: designed to generate discoveries and to understand how the discoveries
work
▪ Applied research: research where a discovery is tested under increasingly controlled
conditions in real-world contexts
During the testing of new discovery during the basic and applied research period a number of
separate research projects are likely to be conducted. The research enterprise has evolved a system
for synthesizing the large numbers of research studies in different topical areas. Research synthesis =
a systematic study of multiple projects that address the same research question or topic and that
summarizes the results in a manner that can be used by practitioners. 2 main forms of research
synthesis:
1. Meta-analysis: uses statistical methods to combine he results of similar studies
quantitatively in order to allow general conclusions to be made
2. Systematic review: focuses on a specific question or issue and uses pre-planned methods to
identify, select, assess, and summarize the findings of multiple studies
Research vocabulary
Theoretical – pertaining to theory. Social research is theoretical, meaning that much of it is
concerned with developing, exploring, or testing the theories or ideas that social researchers have
about hoe the world operates.
Empirical – based on direct observations and measurements of reality.
Probabilistic – based on probabilities.
Causal – pertaining to a cause-effect relationship, hypothesis, or relationship. Something is causal if it
leads to an outcome or makes an outcome happen.
Causal relationship – a cause-effect relationship. For example, when you evaluate whether your
treatment or program causes an outcome to occur, you are examining a causal relationship.
,Types of studies
1. Descriptive studies – designed primarily to document what is going on or what exists. Public
opinions that seek to describe the proportion of people who hold various opinions. E.g. the %
of the population that would vote for a Democrat or a Republican in the next presidential
election.
2. Relational studies – look at the relationships between 2 or more variables. A public opinion
poll that compares the proportion of males and females who say they would vote for a
Democratic or Republican candidate. Studies the relationship between gender and voting
preference.
3. Causal studies – designed to determine whether 1 or more variables causes or affects one or
more outcome variables. Public opinion poll to try to determine whether a recent political
advertising campaign changed voter preferences.
Time in research
Cross-sectional studies – take place at a single point in time.
Longitudinal studies – take place over multiple points in time. You measure your participants on at
least 2 separate occasions in time.
Types of relationships
Correlational relationship – 2 variables perform in a synchronised manner. The level on one variable
is related to the level on the other. Technically, the term ‘correlational relationship’ is redundant: a
correlation by definition always refers to a relationship. However the term correlational relationship
is used to distinguish it from the specific type of association called a causal relationship.
Causal relationship – synchronised relationship between 2 variables just as a correlational
relationship is, but in a causal relationship we say that 1 variable causes the other to occur.
Often leads to consideration of what is termed the 3rd variable or missing variable = an
unobserved variable that accounts for a correlation between 2 variables.
Patterns of relationships
Hypotheses
Hypotheses = a specific statement of prediction.
▪ Alternative hypothesis = a specific statement of prediction that usually states what
you expect will happen in your study
, ▪ Null hypothesis = the hypothesis that describes the possible outcomes other than
the alternative hypothesis. Usually, the null hypothesis predicts there will be no
effect.
If your prediction specifies a direction, the null hypothesis automatically includes both the no-
difference prediction and the prediction that would be opposite in direction to yours = one-tailed
hypothesis. E.g.
H0: As a result of the new program, there will either be no significant difference in depression
or there will be a significant increase
H1: As a result of the new program, there will be a significant decrease in depression
When your prediction does not specify a direction, you have a two-tailed hypothesis.
H0: As a result of 300 mg/day of the ABC drug, there will be no significant difference in
depression
H1: As a result of 300 mg/day of the ABC drug, there will be a significant difference in
depression
2 principles when hypothesis testing:
• 2 mutually exclusive hypothesis statements that, together, exhaust all possible outcomes
• Hypotheses must be tested so that one is necessarily accepted and the other rejected
If your original prediction (H1) was correct, you would reject the null hypothesis and
accept the alternative. If your original prediction was not supported in the data, you
will accept the null hypothesis and reject the alternative.
Variables
Variable = any entity that can take on different values.
E.g. gender, agreement, age, country.
▪ Independent: the variable that you manipulate. E.g. a program or treatment.
▪ Dependent: the variable affected by the independent variable. E.g. the outcome.
Attribute = a specific value on a variable.
E.g. male/female, disagree/neutral/agree.
The attribute should be both exhaustive and mutually exclusive:
Exhaustive: the property of a variable that occurs when you include all possible answerable
responses. E.g. If the variable is religion and the only options are Protestants, Jewish and
Muslim, not all religions are included. Therefore, list the most common attributes and the use
a general category like ‘Other’ to account for all remaining ones.
Mutually exclusive: the property of a variable that ensures that the respondent is not able to
assign 2 attributes simultaneously. E.g. Gender as it is impossible to claim to be both male
and female.
Types of Data
Qualitative data: data in which the variables are not in a numerical form, but in the form of text,
photographs, sound bites, etc.
Quantitative data: data that appear in numerical form.
All quantitative data are based upon qualitative judgements; and all qualitative data can be
summarized and manipulated numerically. E.g. simple texts that are represented on a scale of 1-5 or
categorising qualitative information and numbering them.
The Unit of Analysis
Unit of analysis: the entity that you are analysing in your analysis. E.g. individuals, groups or social
interactions.
, E.g. 1) Survey where you ask individuals to tell you their opinions about something, and you
combine their responses to get some idea of what ‘typical individuals think → unit of analysis
is the individual.
E.g. 2) If you collect data bout crime rates in major cities in the country → unit of analysis is
the city.
Deduction and Induction
Deductive reasoning: top-down reasoning that works from the more general to the more specific
You begin with a theory about your topic of interest, then narrow it down into more specific
hypotheses to test, narrow it down further to collect observations to eventually test the
hypotheses with specific data – a confirmation of original theories.
• Narrower in nature
• Concerned with testing / confirming hypotheses
Inductive reasoning: bottom-up reasoning that begins with specific observations and measures and
ends up as general conclusion or theory.
Begin with specific observations and measures, detect initial patterns and regularities,
formulate tentative hypotheses to explore and end up with developing general
conclusions/theories.
• More open-ended and exploratory
The Structure of Research
Narrow down the question in a hypothesis or focus question.
Operationalisation: translating a construct into its manifestation
→ hypothesis to exactly describe what you think will happen in the study
Narrowest point: direct measurement or observation of the
question of interest.
, Major components in a causal study:
1) Research problem
2) Research question
3) Program (cause)
4) Units (= the participants and different from units of analysis)
5) Outcomes (effect)
6) Design
The Validity of Research
Validity is a term that we use to discuss the quality of various conclusions you might reach based on
a research project: the best available approximation of the truth of a given proposition, inference or
conclusion.
The major realms and components of research →
Cause construct: the abstract idea or theory
of what the cause is in a cause-effect
relationship you are investigating
Effect construct: the abstract idea or theory
of what the outcome is in a cause-effect
relationship you are investigating
Only causal questions involve all 4 validity types:
1. Construct validity = the degree to which conclusions you reach about relationships in your
data are reasonable
Is there a relationship between the variables?
2. Internal validity = the approximate truth about inferences regarding cause-effect or causal
relationships
Assuming there is a relationship, is the relationship causal?
3. Construct validity = the degree to which inferences can legitimately be made from the
operationalisations in your study to the theoretical constructs on which those
operationalisations are based
Did you measured the outcome you wanted to measure?
4. External validity = the degree to which the conclusions in your study would hold for other
persons in other places and at other times
Assuming that there is a causal relationship in this study between the constructs of the cause
and effect, can you generalise this effect to other persons, places, or times?
The validity types build on one another: each question that the validity type addresses presupposes
an affirmative answer to the previous one.
CHAPTER 2 ETHICS
Nuremberg Code – this code was developed following the trial of Nazi doctors after World War II. It
includes 10 principles to guide research involving human subjects. The Code has been extremely
important as a reference point for all regulations related to the protection of human subjects. Among
other things, it established the principles of informed consent, voluntary participation without
coercion, clear scientific justification for research, and most important, limits on the risk of harm.
• Ethical lesson: physical harm to research subjects.
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