Although many people believe that the only way to confidently make causal inferences in the psychology world is through true experiments, I believe that observational and other quasi experiments can be just as effective for making cause and effect conclusions. One of the cardinal rules in psychology, of course, is that correlation does not equal causation. While this is true, I strongly believe that when certain conditions are met, we can infer causation even with nonexperimental data. I have learned about many of these conditions in my Advanced Research Methods textbook and completely agree with the author’s opinion on this topic. Basically, causality has a probabilistic meaning rather than a deterministic one. When making causality inferences, we are not saying that one variable directly, immediately, or always results in one particular outcome, but instead, that “this variable increases the probability of this outcome” such that there is more than just a correlational relationship at play (Keith Chapter 1). This concept applies to both quasi and true experiments so long as certain assumptions are met. Though the list of these assumptions is detailed, a few I find most helpful are:
1.) The experiment (quasi or true) is based on previous literature, relevant findings, sophisticated theory, and carefully chosen variables that researchers have reason to believe effect the outcome of interest (Keith Chapter 9).
2.) Time precedence / logic – if we can establish that the “causal” variable occurs before the “effect” variable, it makes causal inferences easier; if we believe that A causes B, it should be much more difficult to think of B causing A.
3.) Obviously, the easier it is to imagine a third variable influencing the outcome of ...
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... of education and expertise. Are people who take up these careers doing so because they love the excitement or because they have a high need for cognition / interest in education? (here, individual differences in a few different personality traits could be source of this correlation; we can not be sure it is only individual differences in sensation seeking.
2.) An individual being interested in things like extreme sports, sky diving, or gambling provides us with no worthy reason to believe that that individual would, for some reason, become interested in the fields of psychology, nursing, etc. The variables (sensation-seeking and profession) might be correlated, but there is no rhyme or reason to believe there must be a relation between the two such that one causes the other; if two variables are unrelated then they are also causally unrelated (Keith Chapter 9).
This essay examines the advantages and disadvantages of using a method primarily for gathering research on human subjects that can be examined for later use. It will give a basic outline of the methods of investigation, their uses and their suitability. I will also look at the scientific method as a whole and examine the criticisms of this method using the writings of Hume and Popper.
There are many different factors to consider that play a part in experimental procedures. Without these variables, researchers would have a hard time making a claim about a particular topic, because they did not consider all sides of the experiment. An example of the variations done in experiments can be seen throughout Solomon Asch’s “Opinions and Social Pressure,”
In a multipart experiment, Hafenbrack et al. (2014) devoted Study 1 to establishing a positive correlation between mindfulness meditation and resisting suck cost bias. However, the popular press article is centered around studies 2, 3, and 4 of the experiment, all of which make causal claims (Bergland, 2014). In his article, Bergland (2014) correctly indentifies the testing of causal hypotheses by Hafenbrack et al. (2014); however, he fails to mention that the first of the four studies makes an association claim and incorrectly categorizes it as a causal claim. While this inaccuracy does not ...
Quasi-experimental designs are experimental designs that do not provide for the full control of extraneous variables. Primarily, the absence of control in this design is due to the lack of random assignment to groups. Quasi-experimental research designs are used in the study of cause and effect by manipulating the independent variable.
Going into details of the article, I realized that the necessary information needed to evaluate the experimental procedures were not included. However, when conducting an experiment, the independent and dependent variable are to be studied before giving a final conclusion.
Experimental research is the one type of research that allows psychologists to make causal statements. It is where the researcher changes one or more variables that may have an effect on some other variables (King, 2016). The hypothesis is a specific expectation about what is going to happen in the experiment (King, 2016). In the research, the hypothesis was that women would perceive fat talk to be more socially acceptable than men (Katrevich et al., 2014). The other elements of experimental method are dependent and independent variables. The independent variable (IV) is the cause of the results, and it is changed by the experimenter to find the effects, but the dependent vari...
Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable;[2] for example, correlation does not imply
The information gained may support or yield opposite results based on predictions being tested. My independent variable would be time and the dependent one would be the enzyme pectinase. I believe the key feature of my experimentation is the control of most factors so that the influence of a single factor can be seen clearly.
In addition to theories causality, the quality of the empirical test is important. A theory that doesn’t measure the independent and dependent variables correctly could cause inadequate methodological quality. Also, it could cause issues with hypothesising. Furthermore, if the theory doesn’t collect enough data from a related, large and diverse sample then the theory is insufficient. All of these pieces correlate to contribute to a sufficient empirical test. For example, if a theory suggests all men who grow up in a violent house hold will commit violent acts in the future, but doesn’t collect data from a large enough population or includes women in the study then their empirical testing could be insignificant, which would lead to the theory not being empirically valid.
My paper is based on an article from the text’s web site (chapter 9) entitled “Lack of sleep ages body’s systems.” The basic claim of the article is that sleep deprivation has various harmful effects on the body. The reported effects include decreased ability to metabolize glucose (similar to what occurs in diabetes) and increased levels of cortisol (a stress hormone involved in memory and regulation of blood sugar levels). The article also briefly alludes (in the quote at the bottom of page 1) to unspecified changes in brain and immune functioning with sleep deprivation.
Cause and effect order, information is arranged to show causes or conditions and the effects or results of those causes or conditions.
According to Jimenez-Buedo (2011), it is difficult to make a valid reference that there is a causal relationship when conducting an experiment in a laboratory-style setting. Jimenez-Buedo (2011) also states that both internal and external validity are being inferred without adequate evidence to support the claims being made in many cases. Jimenez-Buedo (2011) also states that generalization of results in the case of external validity should not be taken lightly. In other words, it appears that she feels that neither internal nor external validity should be inferred in many cases associated with experiments that are done in a laboratory setting versus the real world. This appears to mean that in all circumstances Jimenez-Buedo (2011) favors conducting experiments that are as representative as possible of the real world in order to be able to validate the results and in order to infer a causal or generalizable relationship.
order of influence an environment has on someone is from furthest to closest (1979). With that in
d. high scores on the x variable are associated with low scores on the y variable.
In this ordered domain, the relationships between cause and effect exist but separated in time and space and not fully understood. Complicated context contains multiple answers. Rese...