Ch. 6 Flashcards
Non-experimental research
A research that lacks the manipulation of an independent variable.
Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).
When to Use Non-Experimental Research
• the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., how accurate are people’s first impressions?).
• the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
• the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
• the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).
the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question.
Recall the three goals of science
are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred.
If the goal is to describe or to predict, a non-experimental approach is appropriate.
But the two approaches can also be used to address the same research question in complementary ways.
correlational research
Research that is non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.
the researcher measures two variables with little or no attempt to control extraneous variables and then assesses the relationship between them.
Correlation is also used to establish the reliability and validity of measurements.
the terms independent variable and dependent variable do not apply to this kind of research.
Another strength of correlational research is that it is often higher in external validity than experimental research.
As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality.
In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity.
Extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory.
If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory.
variables can be quantitative or categorical.
Observational research
Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.
Cross-sectional studies
Studies that involve comparing two or more pre-existing groups of people (e.g., children at different stages of development).
there is no manipulation of an independent variable and no random assignment of participants to groups.
cohort effect
Differences between the groups may reflect the generation that people come from rather than a direct effect of age.
longitudinal studies
Studies in which one group of people are followed over time as they age.
are by definition more time consuming and so require a much greater investment on the part of the researcher and the participants.
cross-sequential studies
Studies in which researchers follow people in different age groups in a smaller period of time.
combines elements of both cross-sectional and longitudinal studies.
This design is advantageous because the researcher reaps the immediate benefits of being able to compare the age groups after the first assessment.
Further, by following the different age groups over time they can subsequently determine whether the original differences they found across the age groups are due to true age effects or cohort effects.
Internal Validity of experimental, quasi-experimental, and non-experimental (correlational) research
Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships.
Quasi-experimental research falls in the middle because it contains some, but not all, of the features of a true experiment.
- For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects.
Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control.
many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment.
The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships.
Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals.
Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression).
Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical.
The crucial point is that what defines a study as experimental or correlational is
not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data.
What defines a study is how the study is conducted.
scatterplots
A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.
positive relationship
A relationship in which higher scores on one variable tend to be associated with higher scores on the other.
negative relationship
A relationship in which higher scores on one variable tend to be associated with lower scores on the other.
Pearson’s Correlation Coefficient (or Pearson’s r)
A statistic that measures the strength of a correlation between quantitative variables.
The strength of a correlation between quantitative variables is typically measured using this.
Pearson’s r ranges 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.
When Pearson’s r is 0, the points on a scatterplot form a shapeless “cloud.”
As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line.
Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large.
There are two common situations in which the value of Pearson’s r can be misleading.
Pearson’s r is a good measure only for linear relationships, in which the points are best approximated by a straight line.
It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line.
The other common situations in which the value of Pearson’s r can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as restriction of range.
restriction of range
When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.
There are two reasons that correlation does not imply causation.
directionality problem: The problem where two variables, X and Y, are statistically related either because X causes Y, or because Y causes X, and thus the causal direction of the effect cannot be known.
third-variable problem: Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.
Correlations that are a result of a third-variable are often referred to as spurious correlations. = Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.
Assessing Relationships Among Multiple Variables
Most complex correlational research involves measuring several variables—either binary or continuous—and then assessing the statistical relationships among them.
This approach is often used to assess the validity of new psychological measures.
correlation matrix
Shows the correlation coefficient between pairs of variables in the study.
factor analysis
A complex statistical technique in which researchers study relationships among a large number of conceptually similar variables.
In essence, factor analysis organizes the variables into a smaller number of clusters, such that they are strongly correlated within each cluster but weakly correlated between clusters.
Each cluster is then interpreted as multiple measures of the same underlying construct.
These underlying constructs are also called “factors.”
Two additional points about factor analysis are worth making here.
One is that factors are not categories.
Factor analysis does not tell us that people are either extraverted or conscientious or that they like either “reflective and complex” music or “intense and rebellious” music.
Instead, factors are constructs that operate independently of each other.
The second point is that factor analysis reveals only the underlying structure of the variables.
It is up to researchers to interpret and label the factors and to explain the origin of that particular factor structure.
statistical control
Controlling potential third variables to rule out other plausible interpretations.
partial correlation
A method of controlling extraneous variables by measuring them and including them in the statistical analysis.
It is important to note that while partial correlation provides an important tool for researchers to statistically control for third variables, researchers using this technique are still limited in their ability to arrive at causal conclusions because this technique does not take care of the directionality problem and there may be other third variables driving the relationship that the researcher did not consider and statistically control.
regression
A statistical technique that allows researchers to predict the value of one variable given another.
Typically the variable that is used to make the prediction is referred to as the predictor variable and the variable that is being predicted is called the outcome variable or criterion variable. This regression equation has the following general form:
Y = b1X1
predictor variable
A variable in a regression equation that is hypothesized to be related to (“predicts”) the value of an outcome or criterion variable.
outcome variable or criterion variable
The variable that is being predicted by a predictor variable in a regression equation.
simple regression
A statistical procedure which uses the value of one variable to predict another. Sometimes called “linear regression.”
multiple regression
Involves measuring several variables (X1, X2, X3,…Xi), and using them to predict some outcome variable (Y).
The result of a multiple regression analysis is an equation that expresses the outcome variable as an additive combination of the predictor variables. This regression equation has the following general form:
Y = b1X1+ b2X2+ b3X3+ … + biXi
The regression weights (b1, b2, and so on) indicate how large a contribution a predictor variable makes, on average, to the prediction of the outcome variable.
Specifically, they indicate how much the outcome variable changes for each one-unit change in the predictor variable.
advantage of multiple regression is that it can show whether a predictor variable makes a contribution to an outcome variable over and above the contributions made by other predictor variables (i.e., it can be used to show whether a predictor variable is related to an outcome variable after statistically controlling for other predictor variables).
quantitative research
Research that typically starts with a focused research question or hypothesis, collects a small amount of numerical data from a large number of individuals, describes the resulting data using statistical techniques, and draws general conclusions about some large population.
qualitative research
Research that begins with a less focused research question, collects large amounts of relatively “unfiltered” data from a relatively small number of individuals, describes data using nonstatistical techniques, such as grounded theory, thematic analysis, critical discourse analysis, or interpretative phenomenological analysis and aims to understand in detail the experience of the research participants.
interviews
A qualitative research method to collect lengthy and detailed information from participants using structured, semi-structured, or unstructured sets of open-ended questions.
focus groups
Used in qualitative research which involves small groups of people who participate together in interviews focused on a particular topic or issue.
grounded theory
Researchers start with the data and develop a theory or an interpretation that is “grounded in” those data.
theoretical narrative
A qualitative research method that involves an interpretation of the data in terms of the themes a researcher has identified.
mixed-methods research
Research that combines both quantitative and qualitative approaches.
triangulation
The idea to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results.
observational research
Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.
Naturalistic observation
An observational method that involves observing people’s behavior in the environment in which it typically occurs.
Naturalistic observation
An observational method that involves observing people’s behavior in the environment in which it typically occurs.
disguised naturalistic observation
When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.
undisguised naturalistic observation
Where the participants are made aware of the researcher presence and monitoring of their behavior.
Reactivity
Refers to when a measure changes participants’ behavior.
Hawthorne effect.
In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.
participant observation
Researchers become active participants in the group or situation they are studying.
disguised participant observation
Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.
undisguised participant observation
Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.
structured observation
When a researcher makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation.
coding
A part of structured observation whereby the observers use a clearly defined set of guidelines to “code” behaviors—assigning specific behaviors they are observing to a category—and count the number of times or the duration that the behavior occurs.
case study
An in-depth examination of an individual.
content analysis
A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.
