For example, with a 0.5 correlation coefficient, for every 1.0 unit increase in variable a, you will see a corresponding 0.5 unit increase in variable b.

This week is a broad overview of the different types of research questions that scientists can use. For this assignment, prepare a paper to briefly assess the three different types of research questions that scientists typically ask. Then, provide a research question about a topic of interest that fits within each of these three categories (one descriptive, one associative/correlational, and one causal/experimental question).
For example, if you want to examine when and where people smoke during the workday at your office, this would be a more descriptive question. You might also want to examine whether or not the frequency of smoke breaks throughout the day impacts employees’ productivity – or their ability to get the job done; this is more of an associative research question. Finally, you might want to determine if there is a causal relationship between smoking and the incidence of lung cancer. Here, you would need to perform carefully controlled experimental research with white mice in a laboratory setting to determine whether or not there is a higher rate of lung cancer among mice exposed to cigarette smoke than those who are not. In case you are curious, if you check the warning labels on cigarette boxes, they used to warn that there is an association between smoking and lung cancer. However, with the overwhelming abundance of conclusive scientific evidence based on carefully controlled scientific data, the warning labels now state that smoking causes cancer. This is a good reason to quit smoking immediately if you haven’t done so already!
In your three examples of research questions, be sure to describe the types of conclusions that can be drawn when asking these questions, discuss the differences between qualitative and quantitative approaches to research, and where each research question fits within this dichotomy.
Length: 4-6 pages, not including title and reference pages
References: Include a minimum of 3 scholarly resources.
The completed assignment should address all of the assignment requirements, exhibit evidence of concept knowledge, and demonstrate thoughtful consideration of the content presented in the course. The writing should integrate scholarly resources, reflect academic expectations and current APA standards, and adhere to Northcentral University’s Academic Integrity Policy.Research Questions Determine the Research Design
This week, the type of research question you want to ask is determined by the type of relationship you expect to uncover. Basically, there are three different types of questions that you can ask when conducting a research study: descriptive, associative, or causal ones.
Descriptive Research Questions
The first type of question to ask is the most basic, and it is one where you are seeking to describe a variable or quantify the average value of the variable. With a descriptive research question, you are not looking to assess or determine relationships between variables (Gravetter & Foranzo, 2018). For example, you might want to find out more about what types of behaviors are involved with shooting a basketball. This would be considered a descriptive research question because you are simply looking to describe or identify the key behaviors that are typically displayed when shooting a basketball properly.
Similarly, you might want to assess the numeric value of a variable such as the proper angle of the hand when releasing the shot (roughly 45%), how quickly the shooter flicks their wrists (less than one second), how high they jump when shooting (6-12 inches), or the force with which the ball is released into the air (velocity). This is more in line with quantitative descriptive research and is also sometimes what you want to determine with a descriptive research question too!
Associative Research Questions
The second type of research question is one that examines the relationship between two variables without implying any sort of cause and effect connection. With an associative research question, you are beginning to assess or quantify the relationship between two variables numerically. You are not just describing it as you would if you were asking more of a descriptive question (as in the example provided above). For example, if there is an association between flicking the wrist quickly while simultaneously planting the feet firmly on the ground and consistently making the 3-point shot, this would be considered an associative relationship. Associative relationships are often assessed through correlational research and something that statisticians call a correlation coefficient (Gravetter & Foranzo, 2018).
When conducting correlational research, a statistic called a correlation coefficient is used to represent the degree of the relationship, or the direction and size of the association between these two variables (Gravetter & Foranzo, 2018). When describing correlation coefficients, there are two key attributes to focus on: the direction of the relationship (either positive or negative) and the magnitude or strength of the relationship. First, you will examine the direction of the relationship.
A positive correlation means that both variables are moving in the same direction, and they seem to be connected or associated with one another (Gravetter & Foranzo, 2018). As one variable increases, so does the other. A few examples would include the number of hours spent studying and grades, exercise frequency and physical fitness assessments, time spent cultivating friendships and healthy relationships (to name a few). Try to think of a few more examples that would fit here. Alternatively, a positive correlation can also mean that as one variable decreases, so does the other. This can seem counterintuitive due to the wording, but it basically means that both variables are moving in the same direction, even if they are both decreasing. Some examples here would include reaction time and skill as a driver, or temperature and time spent jogging outdoors. Try to come up with a few more of your own examples here to help you better understand how this works.
On the other hand, with a negative correlation, the variables are moving in opposite directions (Gravetter & Foranzo, 2018). More specifically, as one variable decreases, the other increases (or vice versa). Some examples would be eating unhealthy food and weight loss, amount of time spent playing video games and grades on final exams, temperature outside, and ice cream consumption, just to name a few. Again, try to think of a few more examples for further clarity.
In addition to the direction of the relationship as detailed by the two types of correlations or associations between two variables (positive/negative), it is also possible to measure the association’s strength or magnitude (Gravetter & Foranzo, 2018). Generally, the closer the association is to 1.0, the stronger the relationship. With a perfect 1.0 correlation coefficient, whenever you see a 1 unit increase in one variable, you will also see a corresponding 1 unit increase in the other. As the relationship weakens, you will start to see less of this perfect one to one correspondence. For example, with a 0.5 correlation coefficient, for every 1.0 unit increase in variable A, you will see a corresponding 0.5 unit increase in variable B. It is important to note that with correlational research, the variables of interest must be quantitative measures that meet the statistical requirements for correlations to be computed.
In psychological research, these correlation coefficients are deemed to have different levels of strength or importance, with higher numbers being viewed as more important or value to focus in on. More specifically, when interpreting the size of these relationships in psychology, Cohen (1988) suggests that coefficients in the .10 range or less are considered small, whereas those in the .30 range are considered moderate, and those in the .50 range or above are considered large effects.
One of the biggest drawbacks with associative questions and correlational research is the inability to say that variable A caused or led to the effect observed in variable B. Novice researchers frequently make this type of error in logic when drawing conclusions, so be on the lookout for this erroneous leap in thinking (Gravetter & Foranzo, 2018). A more truthful or accurate conclusion would be something like variable A and variable B seem to be connected or associated, with increases in A corresponding with increases in B (in the case of a positive correlation). However, this is not necessarily a cause-and-effect type of relationship.
For example, in the case of ice cream consumption and murder rates, you can note that there is a strong positive correlation or association. You cannot, however, state that one leads to or causes the other to occur. In this example, the true underlying cause is the temperature outside – with hotter weather leading to more physical and overt aggression – which often leads to higher murder rates (Aronson et al., 2019), as well as an increase in the amount of ice cream that people consume when it is hot outside. There is no direct cause-and-effect relationship here between murder and ice cream!
Note that correlation does NOT lead to the ability to establish causal relationships, and it is perhaps one of the most common errors in research, so be on the lookout for this mistake. Repeat this aloud 10 times: correlation does not mean causation. Believing that correlation leads to causation is the core of many superstitious beliefs and behaviors (e.g., athletes wearing lucky socks or not allowing a black cat to cross in front of you.). All of these are examples of correlational research – not causal factors by any means concerning the likelihood of future positive events in your life! This is what is called a ‘spurious’ or fake association, and it often leads to many problems in thinking and behaving. If you ever find yourself feeling this way about another person or situation, etc., remind yourself to use logic and reason and that you were born with a sound mind.
Watch the video in the required materials this week for more on this topic.
Cause and Effect Research Questions
The third and final type of research question that scientists ask is whether or not there is a cause and effect relationship between two variables. In other words, does eating ice cream cause people to commit murder? Does taking a 5 to 10-minute break every hour increase your ability to focus and get the work done? Does vaccine A lead to greater immunity and fewer side effects than vaccine B? With these types of relationships, you have an independent variable that is manipulated or intentionally controlled by the researcher. The researcher must have the ability to randomly assign subjects to different levels or conditions of this independent variable (Gravetter & Foranzo, 2018). Also, with experimental or causal research, you will need a dependent variable – where the outcome of the change in the independent variable is measured and assessed. Try to think of some causal questions that would fit here, and be sure to reach out to your professor if you are still unclear as to how this differs from associative and descriptive questions.
Aronson, E., Wilson, T. D., & Akert, R. M. (2019). Social psychology (8th ed.). Pearson.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.
Gravetter, F. J., & Forzano, L. B. (2018). Research methods for the behavioral sciences (6th ed.). Cengage Learning.

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