relationship between ethical intention and ethical behavior. I. Introduction. The current external validity of cross-cultural values and ethics studies. In particular . Dr. Oswald conducts a study examining the relationship between the number of friends . What is the relationship between moderators and external validity?. Most moderator analysis measure the causal relationship between X and A moderation analysis is an exercise of external validity in that the.
Comparing all three correlations, Dr. Oswald will be most able to accurately predict life satisfaction from the experience of daily stress because: Oswald realizes that the women in her study have more friends than the men in her study. This could potentially result in which of the following? Spurious associations due to subgroups Dr. Oswald creates a scatterplot of the relationship between the experience of daily stress and life satisfaction. In doing so, she realizes there are three scores that seem to be very extreme and are nowhere near the other points on the scatterplot.
Specifically, it appears that three people report very high levels of daily stress and very low levels of life satisfaction.
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Which of the following statements IS true? She should consider the scores outliers Dr. Oswald submits her study for publication in a scientific journal. If one of the peer reviewers is concerned about the external validity of her study, which of the following is the most important aspect of Dr.
The random sampling technique used to recruit the participants. Oswald finds that the relationship between the number of friends one has and life satisfaction is stronger for men than for women. Is random assignment affecting the findings? The estimate obtained will be bias-free even when Z and Y are confounded—that is, when there is an unmeasured common factor that affects both Z and Y. Attempts to increase internal validity may also limit the generalizability of the findings, and vice versa.
This situation has led many researchers call for "ecologically valid" experiments. By that they mean that experimental procedures should resemble "real-world" conditions. They criticize the lack of ecological validity in many laboratory-based studies with a focus on artificially controlled and constricted environments.
Some researchers think external validity and ecological validity are closely related in the sense that causal inferences based on ecologically valid research designs often allow for higher degrees of generalizability than those obtained in an artificially produced lab environment.
However, this again relates to the distinction between generalizing to some population closely related to concerns about ecological validity and generalizing across subpopulations that differ on some background factor. Some findings produced in ecologically valid research settings may hardly be generalizable, and some findings produced in highly controlled settings may claim near-universal external validity.
Thus, external and ecological validity are independent—a study may possess external validity but not ecological validity, and vice versa. Qualitative research[ edit ] Within the qualitative research paradigm, external validity is replaced by the concept of transferability.
Transferability is the ability of research results to transfer to situations with similar parameters, populations and characteristics. Some claim that many drawbacks can occur when following the experimental method. By the virtue of gaining enough control over the situation so as to randomly assign people to conditions and rule out the effects of extraneous variables, the situation can become somewhat artificial and distant from real life.
There are two kinds of generalizability at issue: The extent to which we can generalize from the situation constructed by an experimenter to real-life situations generalizability across situations and The extent to which we can generalize from the people who participated in the experiment to people in general generalizability across people  However, both of these considerations pertain to Cook and Campbell's concept of generalizing to some target population rather than the arguably more central task of assessing the generalizability of findings from an experiment across subpopulations that differ from the specific situation studied and people who differ from the respondents studied in some meaningful way.
However, if one's goal is to understand generalizability across subpopulations that differ in situational or personal background factors, these remedies do not have the efficacy in increasing external validity that is commonly ascribed to them. If background factor X treatment interactions exist of which the researcher is unaware as seems likelythese research practices can mask a substantial lack of external validity.
Dipboye and Flanaganwriting about industrial and organizational psychology, note that the evidence is that findings from one field setting and from one lab setting are equally unlikely to generalize to a second field setting. It depends in both cases whether the particular treatment effect studied would change with changes in background factors that are held constant in that study. If one's study is "unrealistic" on the level of some background factor that does not interact with the treatments, it has no effect on external validity.
It is only if an experiment holds some background factor constant at an unrealistic level and if varying that background factor would have revealed a strong Treatment x Background factor interaction, that external validity is threatened. This method does not assume homogeneity of error variances and so it would likely produce estimates different from the previous two.
Continuous Moderator and Categorical Causal Variable An example is that the socioeconomic status moderates the effect of some intervention.
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One key issue is to center the variable of socioeconomic status; i. We may want to determine the effect of X for various levels of the moderator, M, i. In principal, the values of M would be chosen using some sort conceptual rationale. For instance, if IQ were the moderator, we might use genius level and average level to compute the effects of X on Y.
More commonly, the values are one standard deviation above the mean of M and one standard deviation below the mean of M. To obtain these estimates we use either the first or second method described above. Continuous Moderator and Causal Variable One key question is the assumption of how the moderator changes the causal relationship between X and Y.
Normally, the assumption is made that the change is linear: As M goes up or down by a fixed amount, the effect of X on Y changes by a constant amount.
Alternatively, M may have a different type of effect: Threshold — The effect of X on Y changes when M is greater than a certain value; Discrepancy — When X and M are measured using the same units, the absolute difference between X and M is what matters see also Edwards, The key point is that moderation is not always best captured by a product term. If a product term is used, one must assume that both X and M are measured without error, an often dubious assumption.
Latent variables are discussed below. Centering of both X and M is necessary if neither have zero as a meaningful value. To interpret the results and determine simple effects, the effect of X at various levels of M would be measured.
Ideally, the levels of M would be theoretically motivated. If not possible, one might use M at the mean and at plus and minus one standard deviation from the mean. Latent Variables In this case one latent variable interacts with another latent variable.
This is the most complicated case. Kenny and Judd have developed a solution using product indicators of X1M1, X1M2, X2M1, and X2M2, but it is quite complicated with many nonlinear constraints and it requires a large sample size to have sufficient power and the assumption of normality to identify the model. Klein and Moosbrugger have developed a method of estimation that does not require nonlinear constraints and their procedure is described by Marsh, Wen, and Hau Other Issues Three additional issues that are discussed here briefly are repeated measures, multilevel modeling, meta-analysis, moderated mediation or mediated moderation, and mixture modeling.
Repeated Measures All of the above discussion presumes that the design is between participants. In some cases, the design is repeated measures. Judd, Kenny, and McClelland describe moderator analyses in this case. In essence, moderation is indicated by computing a difference score across conditions and determining whether the moderator predicts that difference: Because the difference score measures the effect of X on Y for each person, using it as the outcome variable gives an ideographic measure the causal effect and it is then determined if the moderator predicts that causal effect.
Moderation with repeated measures can also be handled by multilevel modeling. Multilevel Modeling In some situations the data are said to be clustered, and a multilevel model is needed to model the nonindependence due to clustering. For instance, there might be students in classrooms with students being a level 1 and classrooms at level 2.
Sometimes, there are level 1 moderators, these being moderators that vary within the classroom. More typically there are level 2 moderators, these being moderators that vary between classrooms.
Note too that X can be at either level 1 or level 2. If X is measured at level 1, one can determine a generic moderator, that is, measure the extent to which there is variation in the X-Y relationship. Evidence of generic moderation would be obtained if there was variation in the X-Y slopes. Meta-analysis Much of meta-analysis involves the study of moderation. If a variable predicts effect sizes, that variable is moderator.
Moreover, as with multilevel modeling, one can test for a generic moderator by determining if effect sizes vary more than would be expected by sampling error. One of the key tasks in meta-analysis is the understanding or what are the moderators of the effect.
Mediated Moderation and Moderated Mediation In mediated moderation, the moderation disappears when the mediator is introduced. In moderated mediation, the pattern of mediation varies as a function of the moderator. See my mediation page for more information. Papers by Muller, Judd, and Yzerbyt and Edwards and Lambert discuss the relationship between mediated moderation and moderated mediation.
They also present examples of each. Also Preacher, Rucker, and Hayes have developed a macro for estimating moderated mediation click here. Mixture Modeling We can use mixture modeling to search for a "latent" moderator. In such a case, we measure X and Y and then we allow for latent classes which would be the moderator variable. Effect size and power in assessing moderating effects of categorical variables using multiple regression: Journal of Applied Psychology, 90, Testing and interpreting interactions.
The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, Statistical power analysis for the behavioral sciences. Alternatives to difference scores as dependent variables in the study of congruence in organizational research.
Organizational Behavior and Human Decision Processes, 64,