What Type of Claim Is Likely to Make a Prediction About Behavior?

Descriptive Inquiry

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

Learning Objectives

Explain when descriptive research is useful

Key Takeaways

Fundamental Points

• Descriptive studies do non test specific relationships between factors; yet, they provide information about behaviors and attributes with the goal of reaching a meliorate understanding of a given topic.
• Descriptive inquiry is a useful method of gathering information nigh rare phenomena that could not exist 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 'southward artificial construct.
• The most common blazon of descriptive research is the case study, which provides an in-depth assay of a specific person, grouping, or miracle. While their findings cannot be generalized to the overall population, instance studies can provide important data for futurity research.

Key Terms

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

Research studies that practice 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 course a hypothesis, especially when in that location is not any existing literature in the area. In these situations designing an experiment would exist premature, equally the question of interest is not even so clearly defined as a hypothesis. Oftentimes a researcher will brainstorm with a non-experimental approach, such as a descriptive study, to gather more than information nearly the topic before designing an experiment or correlational study to address a specific hypothesis.

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

Descriptive Research: While descriptive research cannot be generalized beyond the specific object of study, it tin can assistance psychologists gain more data about a topic, and codify hypotheses for hereafter experiments.

Descriptive research can exist used to gain a vast, if oft inconclusive, amount of information. It has the reward of studying individuals in their natural surround 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 written report, which uses interviews, ascertainment, or records to gain an in-depth understanding of a single person, group, or phenomenon. Although instance studies cannot be generalized to the overall population (as tin can experimental research), nor can they provide predictive power (as tin can correlational inquiry), they tin provide extensive information for the evolution of new hypotheses for future testing and provide information about a rare or otherwise hard-to-study issue or condition.

Correlational Research

Correlational research tin be used to meet if ii variables are related and to make predictions based on this relationship.

Learning Objectives

Interpret results using correlational statistics

Key Takeaways

Cardinal Points

• There are some instances where experimental research is not an option for practical or upstanding reasons. In these situations, correlational research can however exist used to determine if ii variables are related.
• Correlations can exist used to make predictions near the likelihood of 2 variables occurring together.
• Correlation does not imply causation. Just because ane gene correlates with another does non mean the starting time factor causes the other or that these are the only ii factors involved in the human relationship. Only an experiment can establish crusade and effect.

Key Terms

• causation: The act past which an effect is produced; in psychological inquiry, the assumption that one variable leads to another.
• negative correlation: A relationship between two variables such that equally i increases the other decreases. On a graph, a negative correlation will take a negative slope.
• positive correlation: A relationship between ii 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 2 variables. Different experimental studies, all the same, correlational studies can merely prove that two variables are related—they cannot decide causation (which variable causes a change in the other). A correlational written report serves simply to describe or predict behavior, not to explicate it. In psychological research, it is of import 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 farther inquiry would need to exist done to evidence any kind of causal relationship.

Positive and Negative Correlations

The attributes of correlations include strength and management. The strength, or degree, of a correlation ranges from -i to +i and therefore will be positive, negative, or zero. Direction refers to whether the correlation is positive or negative. For example, 2 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 take the same direction (positive) but are very different in their force. Although.05 indicates a relatively weak relationship,.98 indicates an extremely strong relationship betwixt 2 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 schoolhouse GPA and college GPA—in other words, that those students with high grades in high school will also tend to take high grades in college.

A negative correlation, such equally -.eight, would mean that one variable increases as the other increases. You lot might wait to meet a negative correlation between the amount of partying the night before a exam and the score on that test—in other words, that more partying relates to a lower form.

Correlational Forcefulness

It is extremely rare to discover a perfect correlation between 2 variables, only the closer the correlation is to -one or +one, the stronger the correlation is.

Correlations of varying directions and strengths: Panels (a) and (b) show the difference between potent and weak positive linear patterns—the strong pattern more closely resembles a straight line. The aforementioned 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 upwardly (both variables increase at the aforementioned fourth dimension), and a negative linear pattern slopes downwardly (ane variable decreases while the other increases).

Statistical Significance

Statistical testing must be done to determine if a correlation is significant. Fifty-fifty a seemingly strong correlation, such every bit.816, can really be insignificant due to a variety of factors, such equally random chance and the size of the sample being tested. With smaller sample sizes, it can exist easy to obtain a big correlation coefficient but difficult for that correlation coefficient to reach statistical significance. In contrast, with big samples, even a relatively small correlation of.20 may achieve statistical significance.

Benefits of Correlational Research

An experiment is non always the well-nigh appropriate approach to answering a inquiry question. Sometimes information technology is not possible to conduct out a true experiment for applied or upstanding reasons because it is impossible to manipulate the contained variable. If a researcher was to look at the psychological effects of long-term ecstasy apply, it would not be ethical to randomly assign participants to a status of long-term ecstasy use. An experiment is likewise non 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 decide 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 lot tin can employ one variable to predict the likelihood of the other when there is a stiff correlation between the two. For case, you could have two measurements from one,000 families—whether the father is an alcoholic and whether a son is an alcoholic—and calculate the correlation. If at that place is a strong correlation between the 2 measurements, it will let you lot to predict, within certain limits of probability, what the chances are that the son of an alcoholic father will also have a problem with booze.

Limitations of Correlational Research

A correlational study serves merely to describe or predict beliefs, not to explicate it. Always remember that correlation does not imply causation. Since there is no random assignment to conditions, a researcher cannot dominion out the possibility that there is a third variable affecting the human relationship between the 2 variables measured. Even if at that place is no 3rd variable, it is incommunicable to tell which gene is influencing the other. Only experimental research tin determine causation. In the above case, while a research could predict the likelihood of an alcoholic male parent having an alcoholic son, they could non describe why this was the example.

An fantabulous example used by Li (1975) to illustrate the "tertiary variable" problem is the positive correlation in Taiwan in the 1970's betwixt the employ of contraception and the number of electrical appliances in 1's house. Of course, using contraception does non induce you to buy electrical appliances or vice versa. Instead, the tertiary variable of education level affects both.

Another pop case is that there is a potent positive correlation between ice cream sales and murder rates in the summer. As water ice foam sales rise, so practice murder rates. Is this because eating ice cream makes u.s.a. want to murder people? The actual explanation is that when the weather is hot, more than people buy water ice cream, but they also go out more, potable more, and socialize more than, leading to an increase in murder rates. Extreme temperatures observed in the summer as well accept been shown to increment aggression. In this case, at that place are many other variables at play that feed the correlation between murder rates and ice foam sales.

Experimental Research

Experimental inquiry tests a hypothesis and establishes causation by using independent and dependent variables in a controlled environs.

Learning Objectives

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

Cardinal Takeaways

Key Points

• Experiments are mostly the most precise studies and have the about conclusive power. They are particularly effective in supporting hypotheses nigh cause and effect relationships. However, since the atmospheric condition in an experiment are artificial, they may not utilize to everyday situations.
• A well-designed experiment has features that control random variables to make sure that the result measured is caused past the contained variable existence manipulated. These features include random assignment, apply of a control grouping, and apply of a single or double-blind design.
• An experimenter decides how to dispense the independent variable while measuring only the dependent variable. In a good experiment, only the independent variable volition touch on the dependent variable.

Key Terms

• dependent variable: The aspect or subject field of an experiment that is influenced past the manipulated attribute; an issue 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 atmospheric condition.

Experimental research in psychology applies the scientific method to achieve the 4 goals of psychology: describing, explaining, predicting, and decision-making behavior and mental processes. A psychologist can utilise experimental enquiry to test a specific hypothesis by measuring and manipulating variables. By creating a controlled environs, researchers can test the effects of an independent variable on a dependent variable or variables.

For instance, a psychologist may be interested in the bear on of video game violence on children's aggression. The psychologist randomly assigns some children to play a tearing video game for 1 hour and other children to play a non-violent video game for 1 hour. And then the psychologist observes the children socialize afterward to determine if the children in the "violent video game" condition comport more aggressively than the children in the "non-trigger-happy video game" condition. In this example, the independent variable is video game group. Our independent variable has 2 levels: violent video games and non-violent video games. The dependent variable is the thing that we want to measure—in this example, aggressive behavior.

Contained and Dependent Variables

In an experimental study, the independent variable is the gene that the experimenter controls and manipulates. This variable is hypothesized to exist the cause of a particular outcome of involvement. The dependent variable, on the other hand, depends on the independent variable, and volition change (or not) because of the contained variable. The dependent variable is the variable that we want to measure out (as opposed to dispense). In a elementary experiment, a researcher might hypothesize that cookies volition make individuals consummate a task quicker. In one condition, participants will be offered cookies if they complete a chore, while in another condition they will not be offered cookies. In this instance the presence of a reward (receiving cookies or non) is the independent variable, and the time taken to complete the task is the dependent variable.

Issue of a Reward: Furnishings of receiving a cookie as a reward (contained variable) on fourth dimension taken to consummate chore (dependent variable). Every bit 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 accept more than than one contained variable. A researcher might determine to examination the hypothesis that cookies volition make individuals work harder simply if the task is easy to begin with. In this case, both the presence of a reward and the difficulty of the task would exist independent variables.

Experimental Pattern

The purpose of an experiment is to investigate the relationship between two variables to exam a hypothesis. By using the scientific method, a psychologist can program and design an experiment that volition 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 place a question and, after some preliminary research, form a hypothesis to answer that question. Later designing an experiment to test the hypothesis and collecting data from the experiment, a scientist will draw a conclusion. The decision volition 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 back up or abnegate information technology.

Experimental Pattern: Important Principles

A poorly designed written report will not produce reliable data. In that location are key components that must be included in every experiment: the inclusion of a comparing group (known as a "control group"), the apply of random consignment, and efforts to eliminate bias. When a written report is designed properly, the simply deviation between groups is the one fabricated 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 contained variable is not applied. The control group helps researchers balance the effects of beingness in an experiment with the furnishings of the independent variable. This helps to ensure that there are no random variables also influencing behavior. In an experiment monitoring productivity, for case, it was hypothesized that additional lighting would increase productivity in manufacturing plant workers. When workers were observed in additional lighting they were more than productive, simply merely because they were being watched. If a control group was besides 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 exist assigned randomly to different treatment groups. Random assignment is used to ensure that any preexisting differences amongst the subjects exercise not affect the experiment. By distributing differences randomly between the conditions, random consignment lowers the chances that factors like age, socioeconomic status, personality measures, and other private variables will touch the overall group's response to the contained variable. Theoretically, the baseline of both the experimental and control groups will be the same before the experiment starts. Therefore, if there is a divergence in the behavior of the two groups at the end of the experiment, the but reason would be the treatment given to the experimental group. In this way, an experiment tin can bear witness a cause-and-event connectedness betwixt the independent and dependent variables.

Blinding and Experimenter Bias

To preserve the integrity of the control grouping, both researcher(s) and subject(due south) may be "blinded." If a researcher expects certain results from an experiment and appropriately unknowingly influences the subjects' responses, this is chosen demand bias. If the experimenter inadvertently interprets the data in a mode that supports the hypothesis when other interpretations are possible, it is called the expectancy consequence. To counteract experimenter bias, the subjects tin can be kept uninformed on the intentions of the experiment, which is called unmarried blinding. If the people collecting the information and the participants are kept uninformed, then information technology is chosen a double bullheaded experiment. Past using blinding, a researcher tin can eliminate the chances that they are inadvertently influencing the upshot of the experiment.

Counterbalancing

When running an experiment, a researcher will want to pay close attention to their design to avert mistake that can be introduced past not balancing the conditions properly. Consider the following example. You are running a study in which participants complete a job of pressing button A with their left hand if they run into a green light and pressing button B with their right hand if they meet a ruby-red light. You find support for your hypothesis that cherry stimuli are processed more quickly than green stimuli. Nonetheless, an alternative explanation is that people are faster to respond with their right manus simply considering most people are right-handed. The solution to this problem is to "counterbalance" your pattern. You will randomly assign 50% of your participants to respond to the red stimulus with their correct hand (and green with their left) and assign the other 50% to answer to the red stimulus with their left manus (and green with their right). In this way, you are anticipating and controlling for this extra source of error in your design.

Strengths and Weaknesses of Experimental Research

One of the principal strengths of experimental inquiry is that it can often decide a crusade and effect relationship between two variables. By systematically manipulating and isolating the independent variable, the researcher can determine with conviction the contained variable'south causal event on the dependent variable. Another strength of experimental enquiry is the power to assign participants to different conditions through random assignment. Randomly assigning participants to conditions ensures that each participant is equally probable to be assigned to one condition or some other, and that there are no differences between experimental groups.

Although experimental research can often respond the causality questions that are left unclear past correlational studies, this is not e'er the example. Sometimes experiments may non be possible or ethical. Consider the instance of the studying the correlation betwixt playing fierce video games and aggressive beliefs. It would be unethical to assign children to play lots of violent video games over a long period of fourth dimension to see if it had an impact on their aggression. Additionally, considering experimental research relies on controlled, artificial environments, it can at times be difficult to generalize to real world situations, depending on the experiment's pattern and sample size. If this is the instance, the experiment is said to have poor external validity, meaning that the situation the participants were exposed to bears niggling resemblance to any real-life situation.

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Source: https://courses.lumenlearning.com/boundless-psychology/chapter/types-of-research-studies/

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