PSYC 101 EXAM QUESTIONS AND ANSWERS LATEST UPDATE GRADED
A+
Standard Deviation
Used to show and measure the variation in data
Histograms/Bar Graphs
A graph that shows the frequency between two things
Operationalization
The process of defining a measurement that isn't necessarily measurable
Theory
A list of ideas that are used to explain predictions and to make predictions
Hypothesis
An educated guess on what the outcome will be that supports the theory
Research
The actual collection of the data being tested. The test of the hypothesis
Descriptive Research
Used to describe behavior and characteristics of the population. Usually Naturalistic
Observation, Case Studies, and Surveys
Longitudinal studies
Take long periods of time, hence "long". Used to show the changes in a person over a long
period of time. These take a long time, they're expensive, and because of the long amount of
time, you may lose participants (death) which makes it all a waste
Cross-sectional studies
Observe and classify the changes in different types of people and different groups at the same
time. This is sometimes at a disadvantage because unidentified variables can get involved
Naturalistic observation
The process of observing and classifying, not explaining, behavior of people in a natural setting
(at home, parks, a mall). The people are being observed without interference between the
observer and the one being observed. The bad thing about it is observer bias, it's time consuming,
and you don't have control over the environment.
Observer Bias
When the researcher them self alters or changes the results of the study. For example, a teacher
studying differences in math skills between boys and girls might spend more time teaching boys
because he/she believes that boys are better at math.
Case Study
Kind of the same as naturalistic observation, except a case study has a deeper study on a certain
topic with less people. For example, health. You usually can't replicate these and because of the
small amount of people, it takes away generalization.
Survey
AKA the interactive method, a survey is used to get large amounts of data in a short amount of
time, either though an interview or a questionnaire. These are very inexpensive, however, people
may lie because they know they're a part of an experiment, and it doesn't represent the entire
population. It also leads to more advanced research.
Hawthorne Effect
This is when you know you're being watched so you act differently, usually for the better,
however you may act worse because you're under pressure. For example, a teacher knows they're
, being evaluated so that day they act on their A game and do the best they can in front of the
interviewer.
Cohort Effect
When an entire group of people get eliminated from an experiment. For example, either young,
old, skinny, fat, tall, short, etc.
Social Desirability Bias
This is when the person who is being observed acts how they think the observer wants them to
act. For example, if someone is being experimented on for the use of drugs, however they are in
a room with fat people, they may think it's for weight loss and try to lose weight because that's
what they think it's all about.
Experimenter Bias
This is when the experimenter messes with the results of the experiment in order to make the
outcome how they wanted it to be. The experimenter's actions influence the outcome.
Selection Bias
This is when the proper form of randomness is not achieved. In order to fix this one may want to
use a random number generator instead of trying to randomize it themselves because everyone
may not be getting a fair chance of being selected.
Sampling Bias
This is when the group of people you experiment on do not represent the topic that's being
experimented.
Correlational Research
The process of examining how variables are naturally related to the real world. This only shows
the relation between two things, not how they were caused (Cannot determine causality, only
correlation. For example, you can't say less sleep causes more stress or vice versa). This is good
because you can explore relations in a natural environment, and provide a base for future
experiments.
Causality Problem
This shows the relation between two things, not how they were caused. You cannot determine
causality, you can only determine correlation. For example, you can't say less sleep causes more
stress or vice versa.
Directionality Problem
This refers to the possibility that Variable A is causing changes in Variable B, or that Variable B
is causing changes in Variable A. It could go in either direction.
Third Variable Problem
When two variables appear to be related to each other but there is another unknown variable (the
third variable) that is the real source of the link between the other two variables. For example,
you're getting less sleep because of more stress, but could that be caused by your new job?
Correlational Coefficients
The numerical relationship between the variables. The scale goes from -1.00 to +1.00. An
example of positive correlation (0 to +1.00) is time spent studying and grades. An example of
negative Correlation (0 to -1.00)
is the time spent playing video games and grades.
Scatterplots
The visual representation on the variables and how they correlate with each other.
Experimental Research
A+
Standard Deviation
Used to show and measure the variation in data
Histograms/Bar Graphs
A graph that shows the frequency between two things
Operationalization
The process of defining a measurement that isn't necessarily measurable
Theory
A list of ideas that are used to explain predictions and to make predictions
Hypothesis
An educated guess on what the outcome will be that supports the theory
Research
The actual collection of the data being tested. The test of the hypothesis
Descriptive Research
Used to describe behavior and characteristics of the population. Usually Naturalistic
Observation, Case Studies, and Surveys
Longitudinal studies
Take long periods of time, hence "long". Used to show the changes in a person over a long
period of time. These take a long time, they're expensive, and because of the long amount of
time, you may lose participants (death) which makes it all a waste
Cross-sectional studies
Observe and classify the changes in different types of people and different groups at the same
time. This is sometimes at a disadvantage because unidentified variables can get involved
Naturalistic observation
The process of observing and classifying, not explaining, behavior of people in a natural setting
(at home, parks, a mall). The people are being observed without interference between the
observer and the one being observed. The bad thing about it is observer bias, it's time consuming,
and you don't have control over the environment.
Observer Bias
When the researcher them self alters or changes the results of the study. For example, a teacher
studying differences in math skills between boys and girls might spend more time teaching boys
because he/she believes that boys are better at math.
Case Study
Kind of the same as naturalistic observation, except a case study has a deeper study on a certain
topic with less people. For example, health. You usually can't replicate these and because of the
small amount of people, it takes away generalization.
Survey
AKA the interactive method, a survey is used to get large amounts of data in a short amount of
time, either though an interview or a questionnaire. These are very inexpensive, however, people
may lie because they know they're a part of an experiment, and it doesn't represent the entire
population. It also leads to more advanced research.
Hawthorne Effect
This is when you know you're being watched so you act differently, usually for the better,
however you may act worse because you're under pressure. For example, a teacher knows they're
, being evaluated so that day they act on their A game and do the best they can in front of the
interviewer.
Cohort Effect
When an entire group of people get eliminated from an experiment. For example, either young,
old, skinny, fat, tall, short, etc.
Social Desirability Bias
This is when the person who is being observed acts how they think the observer wants them to
act. For example, if someone is being experimented on for the use of drugs, however they are in
a room with fat people, they may think it's for weight loss and try to lose weight because that's
what they think it's all about.
Experimenter Bias
This is when the experimenter messes with the results of the experiment in order to make the
outcome how they wanted it to be. The experimenter's actions influence the outcome.
Selection Bias
This is when the proper form of randomness is not achieved. In order to fix this one may want to
use a random number generator instead of trying to randomize it themselves because everyone
may not be getting a fair chance of being selected.
Sampling Bias
This is when the group of people you experiment on do not represent the topic that's being
experimented.
Correlational Research
The process of examining how variables are naturally related to the real world. This only shows
the relation between two things, not how they were caused (Cannot determine causality, only
correlation. For example, you can't say less sleep causes more stress or vice versa). This is good
because you can explore relations in a natural environment, and provide a base for future
experiments.
Causality Problem
This shows the relation between two things, not how they were caused. You cannot determine
causality, you can only determine correlation. For example, you can't say less sleep causes more
stress or vice versa.
Directionality Problem
This refers to the possibility that Variable A is causing changes in Variable B, or that Variable B
is causing changes in Variable A. It could go in either direction.
Third Variable Problem
When two variables appear to be related to each other but there is another unknown variable (the
third variable) that is the real source of the link between the other two variables. For example,
you're getting less sleep because of more stress, but could that be caused by your new job?
Correlational Coefficients
The numerical relationship between the variables. The scale goes from -1.00 to +1.00. An
example of positive correlation (0 to +1.00) is time spent studying and grades. An example of
negative Correlation (0 to -1.00)
is the time spent playing video games and grades.
Scatterplots
The visual representation on the variables and how they correlate with each other.
Experimental Research
