Which method would you use to find a cause and effect relationship?

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Cause and effect refers to a relationship between two phenomena in which one phenomenon is the reason behind the other. For example, eating too much fast food without any physical activity leads to weight gain. Here eating without any physical activity is the “cause” and weight gain is the “effect.” Another popular example in the discussion of cause and effect is that of smoking and lung cancer. A question that has surfaced in cancer research in the past several decades is, What is the effect of smoking on an individual's health? Also asked is the question, Does smoking cause lung cancer? Using data from observational studies, researchers have long established the relationship between smoking and the incidence of lung cancer; however, it took compelling evidence ...

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Descriptive research refers to the measurement of behaviors and attributes through observation rather than through experimental testing.

Explain when descriptive research is useful

Key Takeaways

Key Points

• Descriptive studies do not test specific relationships between factors; however, they provide information about behaviors and attributes with the goal of reaching a better understanding of a given topic.
• Descriptive research is a useful method of gathering information about rare phenomena that could not be reproduced in a laboratory or about subjects that are not well understood.
• Descriptive research has the advantage of studying individuals in their natural environment, free from the influence of an experiment ‘s artificial construct.
• The most common type of descriptive research is the case study, which provides an in-depth analysis of a specific person, group, or phenomenon. While their findings cannot be generalized to the overall population, case studies can provide important information for future research.

Key Terms

• case study: Research performed in detail on a single individual, group, incident, or community, as opposed to (for example) a sample of the whole population.
• hypothesis: A tentative conjecture explaining an observation, phenomenon, or scientific problem that can be tested by further observation and/or experimentation.

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 is distinct from correlational research, in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

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 research can be used to see if two variables are related and to make predictions based on this relationship.

Interpret results using correlational statistics

Key Takeaways

Key Points

• There are some instances where experimental research is not an option for practical or ethical reasons. In these situations, correlational research can still be used to determine if two variables are related.
• Correlations can be used to make predictions about the likelihood of two variables occurring together.
• Correlation does not imply causation. Just because one factor correlates with another does not mean the first factor causes the other or that these are the only two factors involved in the relationship. Only an experiment can establish cause and effect.

Key Terms

• causation: The act by which an effect is produced; in psychological research, the assumption that one variable leads to another.
• negative correlation: A relationship between two variables such that as one increases the other decreases. On a graph, a negative correlation will have a negative slope.
• positive correlation: A relationship between two variables such that as one increases or decreases the other does the same. On a graph, a positive correlation will have a positive slope.

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.

A positive correlation, such as.8, would mean that both variables increase together. You might expect to see a positive correlation between high school GPA and college GPA—in other words, that those students with high grades in high school will also tend to have high grades in college.

A negative correlation, such as -.8, would mean that one variable increases as the other increases. You might expect to see a negative correlation between the amount of partying the night before a test and the score on that test—in other words, that more partying relates to a lower grade.

Correlational Strength

It 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.

Correlations of varying directions and strengths: Panels (a) and (b) show the difference between strong and weak positive linear patterns—the strong pattern more closely resembles a straight line. The same is true for panels (c) and (d)—the strong negative linear pattern more closely resembles a straight line than does the weak negative pattern. Finally, comparing panels (a) and (c) shows the difference between positive and negative linear patterns—a positive linear pattern slopes up (both variables increase at the same time), and a negative linear pattern slopes down (one variable decreases while the other increases).

Statistical Significance

Statistical 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.

The strength of correlational research is its predictive capabilities. With a large sample size, you can use one variable to predict the likelihood of the other when there is a strong correlation between the two. For instance, you could take two measurements from 1,000 families—whether the father is an alcoholic and whether a son is an alcoholic—and calculate the correlation. If there is a strong correlation between the two measurements, it will allow you to predict, within certain limits of probability, what the chances are that the son of an alcoholic father will also have a problem with alcohol.

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.

An excellent example used by Li (1975) to illustrate the “third variable” problem is the positive correlation in Taiwan in the 1970’s between the use of contraception and the number of electric appliances in one’s house. Of course, using contraception does not induce you to buy electrical appliances or vice versa. Instead, the third variable of education level affects both.

Another popular example is that there is a strong positive correlation between ice cream sales and murder rates in the summer. As ice cream sales rise, so do murder rates. Is this because eating ice cream makes us want to murder people? The actual explanation is that when the weather is hot, more people buy ice cream, but they also go out more, drink more, and socialize more, leading to an increase in murder rates. Extreme temperatures observed in the summer also have been shown to increase aggression. In this case, there are many other variables at play that feed the correlation between murder rates and ice cream sales.

Experimental research tests a hypothesis and establishes causation by using independent and dependent variables in a controlled environment.

Compare the role of the independent and dependent variable in experimental design

Key Takeaways

Key Points

• Experiments are generally the most precise studies and have the most conclusive power. They are particularly effective in supporting hypotheses about cause and effect relationships. However, since the conditions in an experiment are artificial, they may not apply to everyday situations.
• A well-designed experiment has features that control random variables to make sure that the effect measured is caused by the independent variable being manipulated. These features include random assignment, use of a control group, and use of a single or double-blind design.
• An experimenter decides how to manipulate the independent variable while measuring only the dependent variable. In a good experiment, only the independent variable will affect the dependent variable.

Key Terms

• dependent variable: The aspect or subject of an experiment that is influenced by the manipulated aspect; an outcome measured to see the effectiveness of the treatment.
• independent variable: The variable that is changed or manipulated in a series of experiments.
• random assignment: Random assignment of subjects to experimental and control conditions is a process used to evenly distribute the individual qualities of the participants across the conditions.

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.

For example, a psychologist may be interested in the impact of video game violence on children’s aggression. The psychologist randomly assigns some children to play a violent video game for 1 hour and other children to play a non-violent video game for 1 hour. Then the psychologist observes the children socialize afterwards to determine if the children in the “violent video game” condition behave more aggressively than the children in the “non-violent video game” condition. In this example, the independent variable is video game group. Our independent variable has two levels: violent video games and non-violent video games. The dependent variable is the thing that we want to measure—in this case, aggressive behavior.

Independent and Dependent Variables

In 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 Design

The 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:

• Identifying a question and performing preliminary research to determine what is already known
• Creating a hypothesis
• Identifying and defining the independent and dependent variables
• Determining how the independent variable will be manipulated and how the dependent variable will be measured

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 Principles

A 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 Groups

Control 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 Assignment

To 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 Bias

To 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.

Counterbalancing

When 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.

Although experimental research can often answer the causality questions that are left unclear by correlational studies, this is not always the case. Sometimes experiments may not be possible or ethical. Consider the example of the studying the correlation between playing violent video games and aggressive behavior. It would be unethical to assign children to play lots of violent video games over a long period of time to see if it had an impact on their aggression. Additionally, because experimental research relies on controlled, artificial environments, it can at times be difficult to generalize to real world situations, depending on the experiment’s design and sample size. If this is the case, the experiment is said to have poor external validity, meaning that the situation the participants were exposed to bears little resemblance to any real-life situation.