Descriptive ResearchDescriptive research refers to the measurement of behaviors and attributes through observation rather than through experimental testing. Show
Learning Objectives Explain when descriptive research is useful Key TakeawaysKey Points
Key Terms
Research studies that do not test specific relationships between variables are called descriptive studies. These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations
designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive Research: While descriptive research cannot be generalized beyond the specific object of study, it can help psychologists gain more information about a topic, and formulate hypotheses for future experiments. Descriptive research can be used to gain a vast, if often inconclusive, amount of information. It has the advantage of studying individuals in their natural environment without the influence of the artificial aspects of an experiment. This approach can also be used to document rare events or conditions that could not be reproduced in a laboratory. Case Studies One important kind of descriptive research in psychology is the case study, which uses interviews, observation, or records to gain an in-depth understanding of a single person,
group, or phenomenon. Although case studies cannot be generalized to the overall population (as can experimental research), nor can they provide predictive power (as can correlational research), they can provide extensive information for the development of new hypotheses for future testing and provide information about a rare or otherwise difficult-to-study event or condition. Correlational ResearchCorrelational research can be used to see if two variables are related and to make predictions based on this relationship. Learning Objectives Interpret results using correlational statistics Key TakeawaysKey Points
Key Terms
Correlational studies are used to show the relationship between two variables. Unlike experimental studies, however, correlational studies can only show that two variables are related—they cannot determine causation (which variable causes a change in the other). A correlational study serves only to describe or predict behavior, not to explain it. In psychological research, it is important to remember that correlation does not imply causation; the fact that two variables are related does not necessarily imply that one causes the other, and further research would need to be done to prove any kind of causal relationship. Positive and Negative Correlations The attributes of correlations include strength and direction. The strength, or degree, of a correlation ranges from -1 to +1 and therefore will be positive, negative, or zero. Direction refers to whether the correlation is positive or negative. For example, two correlations of.78 and -.78 have the exact same strength but differ in their directions (.78 is
positive and -.78 is negative). In contrast, two correlations of.05 and.98 have the same direction (positive) but are very different in their strength. Although.05 indicates a relatively weak relationship,.98 indicates an extremely strong relationship between two variables. A correlation of 0 indicates no relationship between the variables. Correlational StrengthIt is extremely rare to find a perfect correlation between two variables, but the closer the correlation is to -1 or +1, the stronger the correlation is. Statistical SignificanceStatistical testing must be done to determine if a correlation is significant. Even a seemingly strong correlation, such as.816, can actually be insignificant due to a variety of factors, such as random chance and the size of the sample being tested. With smaller sample sizes, it can be easy to obtain a large correlation coefficient but difficult for that correlation coefficient to achieve statistical significance. In contrast, with large samples, even a relatively small correlation of.20 may achieve statistical significance. Benefits of Correlational Research An experiment is not always the most appropriate approach to answering a research question. Sometimes it is not possible to carry out a true experiment for practical or ethical reasons because it is impossible to manipulate the independent variable. If a researcher was to look at the psychological effects of long-term ecstasy use, it would not be
ethical to randomly assign participants to a condition of long-term ecstasy use. An experiment is also not feasible when examining the effects of personality and individual differences since participants cannot be randomly assigned into these categories. Correlational research allows a researcher to determine if there is a relationship between two variables without having to randomly assign participants to conditions. Limitations of Correlational Research A correlational study serves only to describe or predict behavior, not to explain it. Always remember that correlation does not imply causation. Since there is no random assignment to conditions, a researcher cannot rule out the possibility that there is a third variable affecting the relationship between the two variables measured. Even if there is no third variable, it is impossible to tell which factor is
influencing the other. Only experimental research can determine causation. In the above example, while a research could predict the likelihood of an alcoholic father having an alcoholic son, they could not describe why this was the case. Experimental ResearchExperimental research tests a hypothesis and establishes causation by using independent and dependent variables in a controlled environment. Learning Objectives Compare the role of the independent and dependent variable in experimental design Key TakeawaysKey Points
Key Terms
Experimental research in psychology applies the scientific method to achieve the four goals of psychology: describing, explaining, predicting, and controlling behavior and mental processes. A psychologist can use experimental research to test a specific hypothesis by measuring and manipulating variables. By creating a controlled environment, researchers can test the effects of an independent variable on a dependent variable or variables. Independent and Dependent VariablesIn an experimental study, the independent variable is the factor that the experimenter controls and manipulates. This variable is hypothesized to be the cause of a particular outcome of interest. The dependent variable, on the other hand, depends on the independent variable, and will change (or not) because of the independent variable. The dependent variable is the variable that we want to measure (as opposed to manipulate). In a simple experiment, a researcher might hypothesize that cookies will make individuals complete a task quicker. In one condition, participants will be offered cookies if they complete a task, while in another condition they will not be offered cookies. In this case the presence of a reward (receiving cookies or not) is the independent variable, and the time taken to complete the task is the dependent variable. Effect of a Reward: Effects of receiving a cookie as a reward (independent variable) on time taken to complete task (dependent variable). As shown in the figure, participants who received a cookie took much less time to complete the task than participants who did not receive a cookie. An experiment can have more than one independent variable. A researcher might decide to test the hypothesis that cookies will make individuals work harder only if the task is easy to begin with. In this case, both the presence of a reward and the difficulty of the task would be independent variables. Experimental DesignThe purpose of an experiment is to investigate the relationship between two variables to test a hypothesis. By using the scientific method, a psychologist can plan and design an experiment that will answer the research question. The basic steps of experimental design are:
The Scientific Method: The scientific method is the process by which new scientific knowledge is gained and verified. First you must identify a question and, after some preliminary research, form a hypothesis to answer that question. After designing an experiment to test the hypothesis and collecting data from the experiment, a scientist will draw a conclusion. The conclusion will either support the hypothesis or refute it. The scientist will then either reformulate the hypothesis or build upon the original hypothesis. The scientific method cannot prove a hypothesis, only support or refute it. Experimental Design: Important PrinciplesA poorly designed study will not produce reliable data. There are key components that must be included in every experiment: the inclusion of a comparison group (known as a "control group"), the use of random assignment, and efforts to eliminate bias. When a study is designed properly, the only difference between groups is the one made by the researcher. Control GroupsControl groups are used to determine if the independent variable actually affects the dependent variable. The control group demonstrates what happens when the independent variable is not applied. The control group helps researchers balance the effects of being in an experiment with the effects of the independent variable. This helps to ensure that there are no random variables also influencing behavior. In an experiment monitoring productivity, for instance, it was hypothesized that additional lighting would increase productivity in factory workers. When workers were observed in additional lighting they were more productive, but only because they were being watched. If a control group was also observed with no additional lighting this effect would have been obvious. Random AssignmentTo minimize the chances that an unintended variable influences the results, subjects must be assigned randomly to different treatment groups. Random assignment is used to ensure that any preexisting differences among the subjects do not impact the experiment. By distributing differences randomly between the conditions, random assignment lowers the chances that factors like age, socioeconomic status, personality measures, and other individual variables will affect the overall group's response to the independent variable. Theoretically, the baseline of both the experimental and control groups will be the same before the experiment starts. Therefore, if there is a difference in the behavior of the two groups at the end of the experiment, the only reason would be the treatment given to the experimental group. In this way, an experiment can prove a cause-and-effect connection between the independent and dependent variables. Blinding and Experimenter BiasTo preserve the integrity of the control group, both researcher(s) and subject(s) may be "blinded." If a researcher expects certain results from an experiment and accordingly unknowingly influences the subjects' responses, this is called demand bias. If the experimenter inadvertently interprets the information in a way that supports the hypothesis when other interpretations are possible, it is called the expectancy effect. To counteract experimenter bias, the subjects can be kept uninformed on the intentions of the experiment, which is called single blinding. If the people collecting the information and the participants are kept uninformed, then it is called a double blind experiment. By using blinding, a researcher can eliminate the chances that they are inadvertently influencing the outcome of the experiment. CounterbalancingWhen running an experiment, a researcher will want to pay close attention to their design to avoid error that can be introduced by not balancing the conditions properly. Consider the following example. You are running a study in which participants complete a task of pressing button A with their left hand if they see a green light and pressing button B with their right hand if they see a red light. You find support for your hypothesis that red stimuli are processed more quickly than green stimuli. However, an alternative explanation is that people are faster to respond with their right hand simply because most people are right-handed. The solution to this problem is to "counterbalance" your design. You will randomly assign 50% of your participants to respond to the red stimulus with their right hand (and green with their left) and assign the other 50% to respond to the red stimulus with their left hand (and green with their right). In this manner, you are anticipating and controlling for this extra source of error in your design. Strengths and Weaknesses of Experimental Research One of the main strengths of experimental research is that it can often determine a cause and effect relationship between two variables. By systematically manipulating and isolating the independent variable, the researcher can determine with confidence the independent variable's
causal effect on the dependent variable. Another strength of experimental research is the ability to assign participants to different conditions through random assignment. Randomly assigning participants to conditions ensures that each participant is equally likely to be assigned to one condition or another, and that there are no differences between experimental groups. Licenses and AttributionsCC licensed content, Shared previously
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Which of the following are reasons that researchers might use correlational research multiple select question?What are the following reasons that researchers might use correlational research? To use one variable to predict the value of another variable. to investigate real-world events. To conduct research in situations where it would not be ethical to carry out an experiment in another way.
Which of the following is a key feature of a correlational research study?However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical.
Which of the following is an example of correlational research?Which of the following is an example of correlational research? a study in which the researcher looks for a relationship between people's neighborhood demographics and their level of prejudice.
Why can't correlational research tell us two variables go together quizlet?correlation does not prove causation because a correlation doesn't tell us the cause and effect relationship between two variables. We don't know if x causes y or vice versa, or if x and y are cause by a third variable. The only thing a correlation tells us is the association or link between variables.
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