Week 2 - The Scientific Method Flashcards

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1
Q

What is a general model of scientific research in psychology?

A

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).
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2
Q

Describe Cycle - general model of scientific research in psychology

A

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.

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3
Q

What are common sources of research ideas?

A

Three of the most common sources of inspirationare: 1. informal observations
2. practical problems
3. 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. For example, you might notice that you always seem to be in the slowest moving line at the grocery store.

**Practical problems **can also inspire research ideas, leading directly to applied research in such domains as law, health, education, and sports. Does taking lecture notes by hand improve students’ exam performance?

**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. Novice researchers can find inspiration by consulting with a more experienced researcher (e.g., students can consult a faculty member).

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4
Q

What is research literature and why is it important?

A

The research literature in any field is all the published research in that field. Reviewing the research literature 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.

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5
Q

Define and describe professional journals

A

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 in psychology undergo a process of double-blind peer review. Researchers who want to publish their work in the journal submit a manuscript to the editor—who is generally an established researcher too—who in turn sends it to two or three experts on the topic. Each reviewer reads the manuscript, writes a critical but constructive review, and sends the review back to the editor along with recommendations about whether the manuscript should be published or not.

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6
Q

What are scholarly books?

A

Scholarly books are 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.

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7
Q

Literature Search Strategies?

A

The primary method used to search the research literature involves using one or more electronic databases.

These include Academic Search Premier, JSTOR, and ProQuest for all academic disciplines, ERIC for education, and PubMed for medicine and related fields.

The most important for our purposes, however, is PsycINFO, which is produced by the American Psychological Association (APA). PsycINFO

Google Scholar

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8
Q

Describe some techniques for turning research ideas into empirical research questions…

A

Once you have a research idea, you need to use it to generate one or more empirically testable research questions, that is, 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.

But you may also want to generate your own research questions. How can you do this? 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. **

Eg. - How many words on average do people speak per day?

You could consider turning it into a question about a relationship between that behavior or characteristic and some other variable. One way to do this is to ask yourself the following series of more general questions and write down all the answers you can think of.

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?

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?

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9
Q

State and define the two key criteria for evaluating a good research question…

A
  1. the interestingness of the question
  2. the feasibility of answering it.

Interestingness - Here we look at three factors that affect the interestingness of a research question: the answer is in doubt, the answer fills a gap in the research literature, and the answer has important practical implications.

Feasibility - A second important criterion for evaluating research questions is the feasibility of successfully answering them. There are many factors that affect feasibility, including time, money, equipment and materials, technical knowledge and skill, and access to research participants.

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10
Q

Distinguish between a theory and a hypothesis

A

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.

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.

If-then relationship - Theories and hypotheses always have this if-then relationship. “If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.”

Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?”

Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

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11
Q

Describe the hypothetico-deductive method…

A

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.

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12
Q

Define characteristics of a Good Hypothesis

A
  1. good hypothesis must be testable and falsifiable. We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false.
  2. a good hypothesis must be logical. As described above, 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.
  3. The 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. Our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist.
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13
Q

Define a variable and give an example

A

A variable is a quantity or quality that varies across people or situations.

For example, the height of the students enrolled in a university course is a variable because it varies from student to student.

The chosen major of the students is also a variable as long as not everyone in the class has declared the same major.

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14
Q

State the two types of variables and give examples

A

A **quantitative variable **is a quantity, such as height, that is typically measured by assigning a number to each individual.

Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have.

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.

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15
Q

Define ‘operational definition’ and why its important

A

A definition of the variable in terms of precisely how it is to be measured.

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.

For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder.

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16
Q

Define and give examples - sampling and measurement…

A

Researchers need to identify the population of interest.

Researchers in psychology are usually interested in drawing conclusions about some very large group of people.

This is called the population. It could be all American teenagers, children with autism, professional athletes, or even just human beings—depending on the interests and goals of the researcher.

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.

17
Q

Define and describe two forms of sampling…

A

**simple random sampling **- in which every member of the population has an equal chance of being selected for the sample.

For example, a pollster could start with a list of all the registered voters in a city (the population), randomly select 100 of them from the list (the sample), and ask those 100 whom they intend to vote for.

convenience 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.

18
Q

What is Experimental Research and why is it important?

A

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.
19
Q

Describe and define variables in Experimental Research…

A

The independent variable is the variable the experimenter manipulates (it is the presumed cause).
Eg. Examining the light brightness on worker productivity
Independent variable - Light in a room (bright to dim)

The dependent variable is the variable the experimenter measures (it is the presumed effect).
Eg. Workers level of produtctivity

Extraneous variables are any variable other than the dependent variable.
Eg. The noise produced by the light is at the same level when its bright and dim

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.
Eg. Noise is made when the light is bright and less noise is made when it is dim - this may impact the causal relationship

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.

20
Q

Define and describe Non-experimental research

A

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.

For instance, if I just measured the number of traffic fatalities in America last year that involved the use of a cell phone but I did not actually manipulate cell phone use then this would be categorized as non-experimental research.

21
Q

Define and describe Laboratory vs. Field Research

A

A laboratory study is a study that is conducted in the laboratory environment.

A field study is a study that is conducted in the real-world, in a natural environment.

Laboratory experiments typically have high internal validity. Internal validity refers to the degree to which we can confidently infer a causal relationship between variables.

When we conduct an experimental study in a laboratory environment we have very high internal validity because we manipulate one variable while controlling all other outside extraneous variables.

TRADE OFF

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.

22
Q

Define and describe Descriptive Statistics

A

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.

23
Q

Define and describe Measures of central tendency

A

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.

24
Q

Define and describe Measures of dispersion

A

They are used to describe the degree of spread in a set of scores.

So are all of the scores similar and clustered around the mean or is there a lot of variability in the 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.

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.

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. *

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.

A good example of a positive correlation is the correlation between height and weight, because as height increases weight also tends to increase.

Negative correlation coefficients indicate that as the value of one variable increase, the values of the other variable decrease.

An example of a negative correlation is the correlation between stressful life events and happiness; because as stress increases, happiness is likely to decrease.

25
Q

Define/describe ** Inferential statistics**

A

** Inferential statistics** allow researchers to draw conclusions about a population based on data from a sample.

Inferential statistics 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).

Researchers use inferential statistics to determine whether their effects are 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.

It is important to keep in mind that statistics are probabilistic in nature. They allow researchers to determine whether the chances are low that their results are due to random error, but they don’t provide any absolute certainty. Hopefully, when we conclude that an effect is statistically significant it is a real effect that we would find if we tested the entire population. And hopefully when we conclude that an effect is not statistically significant there really is no effect and if we tested the entire population we would find no effect. And that 5% threshold is set at 5% to ensure that there is a high probability that we make a correct decision and that our determination of statistical significance is an accurate reflection of reality.

But mistakes can always be made. Specifically, two kinds of mistakes can be made. First, researchers can make a **Type I error, which 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. You might wonder 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.

A Type II 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 II 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.

26
Q

Define a version of the famous philosophical “problem of induction.”

A

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.

27
Q

Describe and define positive and negative linear relationships

A

A positive linear relationship is a type of correlation between two variables, in which an increase in the value of one variable is associated with an increase in the value of the other variable. In other words, the two variables move in the same direction.

For example, if we measure the amount of time spent studying for a test and the resulting grade on that test, we would expect to see a positive linear relationship, as more study time would generally lead to higher grades.

A negative linear relationship, on the other hand, is a type of correlation in which an increase in the value of one variable is associated with a decrease in the value of the other variable. In other words, the two variables move in opposite directions.

For example, if we measure the amount of junk food consumed per week and the resulting weight gain, we would expect to see a negative linear relationship, as more junk food consumption would generally lead to more weight gain.

28
Q

describe curvilinear u shaped relationship

A

A curvilinear U-shaped relationship is a type of correlation between two variables in which the relationship is not linear but instead takes the shape of a U.

In this type of relationship, as one variable increases, the other variable initially decreases, and then increases again, forming a U shape when plotted on a graph.

For example, let’s consider the relationship between job satisfaction and age.
When people start their first job, they tend to be very enthusiastic and optimistic, and their job satisfaction is high. As they gain experience and age, however, they may encounter various challenges and obstacles in their work, and their job satisfaction may decrease. However, as they approach retirement age, they may begin to feel more secure and satisfied with their work and their job satisfaction may increase again.

On a graph, this relationship would appear as a U shape, with job satisfaction on the Y-axis and age on the X-axis. The curve would be low at the beginning and then gradually rise, before falling again and then rising once more towards the end of the graph.

29
Q

Define and describe Test-retest reliability

A

Test-retest reliability is a measure of the consistency of a test or measure over time. Specifically, it refers to the degree to which a test or measure produces consistent results when administered to the same individual or group on two or more occasions.

To assess test-retest reliability, the same test or measure is given to the same group of people on two different occasions, and the results from the two administrations are then compared.

If the test or measure is reliable, the scores obtained on the two occasions should be highly correlated, indicating that the results are consistent over time.

Test-retest reliability is an important measure of the validity of a test or measure, as it indicates whether the test is measuring the same thing consistently over time. It is commonly used in the fields of psychology, education, and healthcare to evaluate the reliability of various tests and measures, such as intelligence tests, personality assessments, and medical diagnostic tests.

30
Q

Define internal consistency reliability

A

Internal consistency reliability is a measure of the consistency or homogeneity of a test or measure. Specifically, it refers to the degree to which the items or questions within a test or measure are measuring the same construct or dimension.

To assess internal consistency reliability, researchers typically use a statistical technique called Cronbach’s alpha. Cronbach’s alpha calculates the degree of correlation between the items within a test or measure, with values ranging from 0 to 1. A high value (closer to 1) indicates that the items are highly correlated and are measuring the same construct or dimension, whereas a low value (closer to 0) indicates poor internal consistency.

Internal consistency reliability is an important measure of the validity of a test or measure, as it indicates whether the items within a test are consistent with each other and are measuring the same thing.

31
Q

Define Interrater reliability

A

Interrater reliability is a measure of the consistency or agreement between two or more raters or observers when evaluating or scoring a particular behavior, event, or phenomenon.

Specifically, it refers to the degree to which different raters or observers produce consistent scores or ratings when evaluating the same thing.

32
Q

How do you achieve reliability and validity?

A

How do you achieve reliability and validity?

Control groups - to compare effect of experimental group
Placebo control -
Random Selection
Random allocation
Blinding to condition - both participants and experimenters dont know which group each participant is allocated to
Matching
Counterbalancing - the order of conditions