Ch. 2 Flashcards
simple model of scientific research in psychology
The researchers formulate a research question, conduct an empirical study designed to answer the question, analyze the resulting data, draw conclusions about the answer to the question, and publishes the results so that they become part of the research literature (i.e., all the published research in that field).
Because the research literature is one of the primary sources of new research questions, this process can be thought of as a cycle.
New research leads to new questions, which lead to new research, and so on.
research questions can originate outside of this cycle either with informal observations or with practical problems that need to be solved.
But even in these cases, the researcher would start by checking the research literature to see if the question had already been answered and to refine it based on what previous research had already found.
Finding a Research Topic
- Finding Inspiration
Research questions often begin as more general research ideas—usually focusing on some behavior or psychological characteristic.
Three of the most common sources of inspiration are informal observations, practical problems, and previous research.
- Informal observations include direct observations of our own and others’ behavior as well as secondhand observations from non-scientific sources such as newspapers, books, blogs, and so on.
- Practical problems can also inspire research ideas, leading directly to applied research in such domains as law, health, education, and sports.
- Previous research. Recall that science is a kind of large-scale collaboration in which many different researchers read and evaluate each other’s work and conduct new studies to build on it. Of course, experienced researchers are familiar with previous research in their area of expertise and probably have a long list of ideas.
This suggests that novice researchers can find inspiration by consulting with a more experienced researcher (e.g., students can consult a faculty member). But they can also find inspiration by picking up a copy of almost any professional journal and reading the titles and abstracts.
Finding a Research Topic
- Reviewing the Research Literature
Research literature: All the published research in that field.
important to review the literature early in the research process.
means finding, reading, and summarizing the published research relevant to your topic of interest.
In addition to helping you discover new research questions, reviewing the literature early in the research process can help you in several other ways.
- It can tell you if a research question has already been answered.
- It can help you evaluate the interestingness of a research question.
- It can give you ideas for how to conduct your own study.
- It can tell you how your study fits into the research literature.
research literature definitely does not include self-help and other pop psychology books, dictionary and encyclopedia entries, websites, and similar sources that are intended mainly for the general public.
These are considered unreliable because they are not reviewed by other researchers and are often based on little more than common sense or personal experience.
Finding a Research Topic
- Professional Journals
Are periodicals that publish original research articles.
Most articles in professional journals are one of two basic types: empirical research reports and review articles.
- Empirical research reports describe one or more new empirical studies conducted by the authors. They introduce a research question, explain why it is interesting, review previous research, describe their method and results, and draw their conclusions.
- Review articles summarize previously published research on a topic and usually present new ways to organize or explain the results.
- When a review article is devoted primarily to presenting a new theory, it is often referred to as a theoretical article.
- When a review article provides a statistical summary of all of the previous results it is referred to as a meta-analysis.
- Most professional journals undergo a process of double-blind peer review: A process in which the reviewers of a research article do not know the identity of the researcher(s) and vice versa.
is helpful because it ensures that the work meets basic standards of the field before it can enter the research literature.
However, in order to increase transparency and accountability, some newer open access journals utilize an open peer review process wherein the identities of the reviewers (which remain concealed during the peer review process) are published alongside the journal article.
Finding a Research Topic
- Scholarly Books
Books written by researchers and practitioners mainly for use by other researchers and practitioners.
- A monograph is written by a single author or a small group of authors and usually, gives a coherent presentation of a topic much like an extended review article.
- Edited volumes have an editor or a small group of editors who recruit many authors to write separate chapters on different aspects of the same topic.
Although edited volumes can also give a coherent presentation of the topic, it is not unusual for each chapter to take a different perspective or even for the authors of different chapters to openly disagree with each other.
In general, scholarly books undergo a peer review process similar to that used by professional journals.
Finding a Research Topic
- Literature Search Strategies
The primary method used to search the research literature involves using one or more electronic databases.
Finding a Research Topic
- Using Other Search Techniques
First, if you have one good article or book chapter on your topic—a recent review article is best—you can look through the reference list of that article for other relevant articles, books, and book chapters.
In fact, you should do this with any relevant article or book chapter you find.
You can also start with a classic article or book chapter on your topic, find its record in PsycINFO (by entering the author’s name or article’s title as a search term), and link from there to a list of other works in PsycINFO that cite that classic article.
This works because other researchers working on your topic are likely to be aware of the classic article and cite it in their own work.
You can also do a general Internet search using search terms related to your topic or the name of a researcher who conducts research on your topic.
This might lead you directly to works that are part of the research literature (e.g., articles in open-access journals or posted on researchers’ own websites).
Finally, you can talk to people (e.g., your instructor or other faculty members in psychology) who know something about your topic and can suggest relevant articles and book chapters.
Finding a Research Topic
-What to Search For
focus on sources that help you do four basic things: (a) refine your research question, (b) identify appropriate research methods, (c) place your research in the context of previous research, and (d) write an effective research report.
Several basic principles can help you find the most useful sources.
- First, it is best to focus on recent research, keeping in mind that what counts as recent depends on the topic.
— A good general rule, however, is to start with sources published in the past five years.
— The main exception to this rule would be classic articles that turn up in the reference list of nearly every other source.
— If other researchers think that this work is important, even though it is old, then, by all means, you should include it in your review.
-Second, you should look for review articles on your topic because they will provide a useful overview of it—often discussing important definitions, results, theories, trends, and controversies—giving you a good sense of where your own research fits into the literature.
— You should also look for empirical research reports addressing your question or similar questions, which can give you ideas about how to measure your variables and collect your data.
— As a general rule, it is good to use methods that others have already used successfully unless you have good reasons not to.
— Finally, you should look for sources that provide information that can help you argue for the interestingness of your research question. For a study on the effects of cell phone use on driving ability, for example, you might look for information about how widespread cell phone use is, how frequent and costly motor vehicle crashes are, and so on.
Generating Good Research Questions
- Generating Empirically Testable Research Questions
— discussion section
questions expressed in terms of a single variable or relationship between variables.
One way to do this is to look closely at the discussion section in a recent research article on the topic.
These suggestions often take the form of specific research questions, which you can then try to answer with additional research.
This can be a good strategy because it is likely that the suggested questions have already been identified as interesting and important by experienced researchers.
Generating Good Research Questions
- Generating Empirically Testable Research Questions
— If scientific research has already answered the question of how frequent or intense the behavior or characteristic is
then you should consider turning it into a question about a relationship between that behavior or characteristic and some other variable.
— What are some possible causes of the behavior or characteristic?
— What are some possible effects of the behavior or characteristic?
— What types of people might exhibit more or less of the behavior or characteristic?
— What types of situations might elicit more or less of the behavior or characteristic?
Generating Good Research Questions
- Generating Empirically Testable Research Questions
— may also want to generate your own research questions
First, if you have a particular behavior or psychological characteristic in mind, you can simply conceptualize it as a variable and ask how frequent or intense it is.
How many words on average do people speak per day?
How accurate are our memories of traumatic events?
What percentage of people have sought professional help for depression?
If the question has never been studied scientifically—which is something that you will learn when you conduct your literature review—then it might be interesting and worth pursuing.
Generating Good Research Questions
- Generating Empirically Testable Research Questions
- already been studied scientifically
For one thing, the fact that the question has been studied scientifically and the research published suggests that it is of interest to the scientific community.
For another, the question can almost certainly be refined so that its answer will still contribute something new to the research literature.
Again, asking yourself a series of more general questions about the relationship is a good strategy.
- Are there other ways to define and measure the variables?
- Are there types of people for whom the relationship might be stronger or weaker?
- Are there situations in which the relationship might be stronger or weaker—including situations with practical importance?
Generating Good Research Questions
- Interestingness
How interesting the question is to people generally or the scientific community.
Three things need to be considered: Is the answer in doubt, fills a gap in research literature, and has important practical implications.
- First, a research question is interesting to the extent that its answer is in doubt.
— the fact that a question has not been answered by scientific research does not necessarily make it interesting. There has to be some reasonable chance that the answer to the question will be something that we did not already know.
— But how can you assess this before actually collecting data? One approach is to try to think of reasons to expect different answers to the question—especially ones that seem to conflict with common sense. If you can think of reasons to expect at least two different answers, then the question might be interesting. - Second important factor to consider when deciding if a research question is interesting is whether answering it will fill a gap in the research literature.
— this means in part that the question has not already been answered by scientific research. But it also means that the question is in some sense a natural one for people who are familiar with the research literature. - Third factor to consider when deciding whether a research question is interesting is whether its answer has important practical implications.
Generating Good Research Questions
- Feasibility
How likely is the research question going to be successfully answered depending on the amount of time, money, equipment and materials, technical knowledge and skill, and access to research participants there will be.
A second important criterion for evaluating research questions is the feasibility of successfully answering them
A final point here is that it is generally good practice to use methods that have already been used successfully by other researchers.
theory
A theory is a coherent explanation or interpretation of one or more phenomena.
Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly.
It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community.
Theories and hypotheses always have this if-then relationship.
hypothesis
A hypothesis, on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate.
It is an explanation that relies on just a few key concepts.
Hypotheses are often specific predictions about what will happen in a particular study.
They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest.
Hypotheses are often but not always derived from theories.
So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed.
how do researchers derive hypotheses from theories?
One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question.
hypothetico-deductive method
Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with.
They then make a prediction about some new phenomenon that should be observed if the theory is correct.
Again, this prediction is called a hypothesis.
The researchers then conduct an empirical study to test the hypothesis.
Finally, they reevaluate the theory in light of the new results and revise it if necessary.
This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on.
Incorporating Theory into Your Research
The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results.
This format works well for applied research questions and for research questions that existing theories do not address.
The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory.
This format works well when there is an existing theory that addresses the research question-especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.
Characteristics of a Good Hypothesis
testable and falsifiable.
must be logical.
- Hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning.
- Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory.
- Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis.
should be positive.
- That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist.
variable
A variable is a quantity or quality that varies across people or situations.
Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured
quantitative variable
A quantitative variable is a quantity, such as height, that is typically measured by assigning a number to each individual.
categorical variable
A categorical variable is a quality, such as chosen major, and is typically measured by assigning a category label to each individual (e.g., Psychology, English, Nursing, etc.). Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.
operational definition
-a definition of the variable in terms of precisely how it is to be measured.
Most variables that researchers are interested in studying cannot be directly observed or measured and this poses a problem because empiricism (observation) is at the heart of the scientific method.
Operationally defining a variable involves taking an abstract construct like depression that cannot be directly observed and transforming it into something that can be directly observed and measured.
population.
A large group of people about whom researchers in psychology are usually interested in drawing conclusions, and from whom the sample is drawn.
Researchers in psychology are usually interested in drawing conclusions about some very large group of people.
sample
A smaller portion of the population the researcher would like to study.
But they usually study only a small subset or sample of the population.
For example, a researcher might measure the talkativeness of a few hundred university students with the intention of drawing conclusions about the talkativeness of men and women in general.
It is important, therefore, for researchers to use a representative sample-one that is similar to the population in important respects.
simple random sampling,
in which every member of the population has an equal chance of being selected for the sample.
convenience sampling
The most common alternative to random sampling
in which the sample consists of individuals who happen to be nearby and willing to participate (such as introductory psychology students).
Of course, the obvious problem with convenience sampling is that the sample might not be representative of the population and therefore it may be less appropriate to generalize the results from the sample to that population.
Experimental Research
Researchers who want to test hypotheses about causal relationships between variables (i.e., their goal is to explain) need to use an experimental method.
This is because the experimental method is the only method that allows us to determine causal relationships.
Using the experimental approach, researchers first manipulate one or more variables while attempting to control extraneous variables, and then they measure how the manipulated variables affect participants’ responses.
independent variable
is the variable the experimenter manipulates (it is the presumed cause)
dependent variable
is the variable the experimenter measures (it is the presumed effect).
Extraneous variables
are any variable other than the dependent variable.
Confounds
are a specific type of extraneous variable that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results.
When researchers design an experiment they need to ensure that they control for confounds; they need to ensure that extraneous variables don’t become confounding variables because in order to make a causal conclusion they need to make sure alternative explanations for the results have been ruled out.
Non-Experimental Research
Researchers who are simply interested in describing characteristics of people, describing relationships between variables, and using those relationships to make predictions can use non-experimental research.
Using the non-experimental approach, the researcher simply measures variables as they naturally occur, but they do not manipulate them.
Laboratory study
is a study that is conducted in the laboratory environment.
Laboratory experiments typically have high internal validity.
typically low in external validity
high internal validity because we manipulate one variable while controlling all other outside extraneous variables.
When we manipulate an independent variable and observe an effect on a dependent variable and we control for everything else so that the only difference between our experimental groups or conditions is the one manipulated variable then we can be quite confident that it is the independent variable that is causing the change in the dependent variable.
field study
is a study that is conducted in the real-world, in a natural environment.
typically high in external validity,
because field studies are conducted in the real-world, the experimenter typically has less control over the environment and potential extraneous variables, and this decreases internal validity, making it less appropriate to arrive at causal conclusions.
internal validity
refers to the degree to which we can confidently infer a causal relationship between variables.
External validity
simply refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment.
When internal validity is high, external validity tends to be low; and when internal validity is low, external validity tends to be high.
field experiments
where an independent variable is manipulated in a natural setting and extraneous variables are controlled.
Depending on their overall quality and the level of control of extraneous variables, such field experiments can have high external and high internal validity.
Once the study is complete and the observations have been made and recorded the researchers need to analyze the data and draw their conclusions.
Typically, data are analyzed using both descriptive and inferential statistics.
Descriptive statistics are used to summarize the data and inferential statistics are used to generalize the results from the sample to the population.
In turn, inferential statistics are used to make conclusions about whether or not a theory has been supported, refuted, or requires modification.
Descriptive Statistics
Are used to organize or summarize a set of data.
- Examples include percentages, measures of central tendency (mean, median, mode), measures of dispersion (range, standard deviation, variance), and correlation coefficients.
Measures of central tendency are used to describe the typical, average and center of a distribution of scores.
- The mode is the most frequently occurring score in a distribution.
- The median is the midpoint of a distribution of scores.
- The mean is the average of a distribution of scores.
Measures of dispersion
- used to describe the degree of spread in a set of scores.
- The range is a measure of dispersion that measures the distance between the highest and lowest scores in a distribution.
- The standard deviation is a more sophisticated measure of dispersion that measures the average distance of scores from the mean.
- The variance is just the standard deviation squared. So it also measures the distance of scores from the mean but in a different unit of measure.
Descriptive Statistics for experimental research
Typically means and standard deviations are computed for experimental research studies in which an independent variable was manipulated to produce two or more groups and a dependent variable was measured quantitatively.
The means from each experimental group or condition are calculated separately and are compared to see if they differ.
Descriptive Statistics for non-experimental research
simple percentages may be computed to describe the percentage of people who engaged in some behavior or held some belief.
But more commonly non-experimental research involves computing the correlation between two variables.
Including info about correlation coefficient
correlation coefficient
A correlation coefficient describes the strength and direction of the relationship between two variables.
The values of a correlation coefficient can range from -1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship).
A value of 0 means there is no relationship between the two variables.
Positive correlation coefficients indicate that as the values of one variable increase, so do the values of the other variable.
Negative correlation coefficients indicate that as the value of one variable increase, the values of the other variable decrease.
Inferential Statistics
allow researchers to draw conclusions about a population based on data from a sample.
Are crucial because the effects (i.e., the differences in the means or the correlation coefficient) that researchers find in a study may be due simply to random chance variability or they may be due to a real effect (i.e., they may reflect a real relationship between variables or a real effect of an independent variable on a dependent variable).
Use this to determine whether their effects are statistically significant.
statistically significant
A statistically significant effect is one that is unlikely due to random chance and therefore likely represents a real effect in the population.
More specifically results that have less than a 5% chance of being due to random error are typically considered statistically significant.
When an effect is statistically significant it is appropriate to generalize the results from the sample to the population.
In contrast, if inferential statistics reveal that there is more than a 5% chance that an effect could be due to chance error alone then the researcher must conclude that their result is not statistically significant.
Type I error
is a false positive.
It is when a researcher concludes that their results are statistically significant (so they say there is an effect in the population) when in reality there is no real effect in the population and the results are just due to chance (they are a fluke).
When the threshold is set to 5%, which is the convention, then the researcher has a 5% chance or less of making a Type I error.
why researchers don’t set it even lower to reduce the chances of making a Type I error. The reason is when the chances of making a Type I error are reduced, the chances of making a Type II error are increased.
Type Il error
is a missed opportunity.
It is when a researcher concludes that their results are not statistically significant when in reality there is a real effect in the population and they just missed detecting it.
Once again, these Type Il errors are more likely to occur when the threshold is set too low (e.g., set at 1% instead of 5%) and/or when the sample was too small.
Drawing Conclusions
If the results are statistically significant and consistent with the hypothesis and the theory that was used to generate the hypothesis, then researchers can conclude that the theory is supported.
- Not only did the theory make an accurate prediction, but there is now a new phenomenon that the theory accounts for.
If a hypothesis is disconfirmed in a systematic empirical study, then the theory has been weakened.
- It made an inaccurate prediction, and there is now a new phenomenon that it does not account for.
Drawing Conclusions complications
Can never prove
First, confirming a hypothesis can strengthen a theory but it can never prove a theory.
- why there’s an avoidance for “prove”
— result may reflect a type I error.
— there may be other plausible theories that imply the same hypothesis, which means that confirming the hypothesis strengthens all those theories equally.
— it is always possible that another test of the hypothesis or a test of a new hypothesis derived from the theory will be disconfirmed.
“problem of induction.” One cannot definitively prove a general principle (e.g., “All swans are white.”) just by observing confirming cases (e.g., white swans)-no matter how many. It is always possible that a disconfirming case (e.g., a black swan) will eventually come along.
Drawing Conclusions complications
When hypothesis is disconfirmed
A second complication has to do with what it means when a hypothesis is disconfirmed.
- According to the strictest version of the hypothetico-deductive method, disconfirming a hypothesis disproves the theory it was derived from.
- “if A then B” and “not B” necessarily lead to the conclusion “not A.” If A is the theory and B is the hypothesis (“if A then B), then disconfirming the hypothesis (“not B”) must mean that the theory is incorrect (“not A”).
- One reason is that one disconfirmed hypothesis could be a missed opportunity (the result of a type Il error) or it could be the result of a faulty research design.
— Perhaps the researcher did not successfully manipulate the independent variable or measure the dependent variable.
A disconfirmed hypothesis could also mean that some unstated but relatively minor assumption of the theory was not met.
Reporting the Results
The final step in the research process.
results are typically reported in peer-reviewed journal articles and at conferences.
The most prestigious way to report one’s findings is by writing a manuscript and having it published in a peer-reviewed scientific journal.
Another way to report findings is by writing a book chapter that is published in an edited book. Preferably the editor of the book puts the chapter through peer review but this is not always the case and some scientists are invited by editors to write book chapters.
A fun way to disseminate findings is to give a presentation at a conference.
This can either be done as an oral presentation or a poster presentation.
Oral presentations involve getting up in front of an audience of fellow scientists and giving a talk that might last anywhere from 10 minutes to 1 hour (depending on the conference) and then fielding questions from the audience.
Alternatively, poster presentations involve summarizing the study on a large poster that provides a brief overview of the purpose, methods, results, and discussion.
The presenter stands by their poster for an hour or two and discusses it with people who pass by.
Presenting one’s work at a conference is a great way to get feedback from one’s peers before attempting to undergo the more rigorous peer-review process involved in publishing a journal article.
