Posted at 10.17.2018
There have been two main research strategies for calculating resilience; the variable-based procedure and the individual-based methodology. The variable-based approach does not directly assess resilience as a build instead it talks about the relationship between parameters. Specifically, it examines risk and defensive factors and habits of stress and competence associations thus inferring resilience based on habits and statistical organizations (Luthar, 1999). The purpose of this process is to spell it out the interaction between variables and it frequently uses moderators and mediators to describe the effects of protecting factors on outcomes.
The individual-based way on the other side consists of the "isolation of any subset of individuals based on risky and high competence" (Luthar, 1999). It uses categorical variables based on level of risk and can directly regulate how many people can show up into the "resilient" category. Therefore, this approach is able to provide descriptive data on these individuals so you can look at who they are, what defends them from risk and makes them different than all of those other sample or inhabitants.
These approaches differ in the types of research questions they can answer and in their limits. One is not necessarily better than the other although, the variable-based strategy is the greater traditional and widespread approach in the resiliency books. Luthar & Cushing (1999) offered some perception into the issues with measurement using these solutions in resilience research. For the variable-based strategy the first restriction they identified was the "lack of home elevators the actual range of individuals who confronted high risk and were highly experienced within the test. " Their point being that there could be few individuals in the sample who actually meet the stated criteria for resilience. This might also become a concern because of the variant within each test. The people who are exhibiting resilient characteristics may be considered in the top 10% on both risk and competence but this take off is only significant with regards to the other individuals for the reason that study's particular test which might or may well not be representative of the populace. The second limitation recognized by Luthar and Cushing (1999) on the variable-based strategy is the "instability of results involving such connections effects" meaning it's instability as a measure of the resilient process because of the small result sizes typically associated with connections results in statistical models. They believe that to be credible these kinds of findings would have to be replicated across many reports using similar options and examples.
For the individual-based procedure one area of concern is the amount of variability within the technical specs used for categorizing high risk individuals as resilient. A number of examples of the various requirements researchers have placed for labeling resilient individuals include using the top third of distributions, top 16% of adjustment scores, and operating much better than the mean within the sample. The next problem identified is at the degree of domains that the individuals were required to be working well in. For example, does one label an individual resilient if they're high functioning in a single or two domains but doing inadequately in another? Some researchers go through the overall scores while others only take a look at specific domains in order to categorize resilient individuals. These discrepancies is seen as a restriction to this strategy. A further matter is that though it can be seen as a durability that the individual-based strategy can identify the number and characteristics of the subset of people classified as resilient and provide a descriptive accounts; one must take care not to assume these "resilient" individuals will continue to be resilient without end or in the face of other types of adversity.
Each of the strategies lends themselves to different kinds of research designs. The individual-based methodology requires that the constructs involved be categorical. For instance, the occurrence vs. absence of pathology is a dichotomous categorical adjustable. But the variable-based approach can be used to analyze continuous options of risk and competence which can be more frequently utilized by resilience analysts.
Examples of the variable-based way can be found in Marshall's (2000) article analyzing peer influence on adolescent alcoholic beverages use. Another example would be the Luthar et al. (1993) article where they conducted a variable-based analysis of stress and sociability predicting depression. The Vitaro et al. (1996) article is an excellent example of an individual-based approach in their analysis of personal and familial characteristics of resilient sons of male alcoholics. The Bolger (2003) evaluation of peer connections between maltreated children is another example of an individual-based strategy.
Questions # 4
In simple terms resilience can be explained as, "successful adaptation despite risk and adversity" (Masten, 1994 as cited in Kumpfer, 1999). It can be referred to as a dynamic process of positive adaptation that involves protective factors at the individual, familial, and contextual levels. In learning the resilience literature one realizes that this is a complicated construct that can be defined and assessed in various ways, consequently one must be cautious in the interpretation and generalization of studies form any given study. Resilience is plainly not a uni-dimensional domain. The three main issues to consider when examining the multidimensional character of resilience are as follows:
Resilience might not be a representation of global adaptation across all domains thus high ratings in one site do definitely not translate to high scores in another domain. Some findings may even reveal that some high-risk children show competence in a few domains but show problems in other areas. This can be observed in Luthar's (1993) review of interior city adolescents. He discovered that out of 25 children classified as "resilient" based on positive adaption in a single domain 15 of them were actually exhibiting extremely low competence in another site. For that reason, I urge insurance policy producers to consider the restriction of using the domains of high school graduation and occupation to conclude global competence or resilience.
Resilience is not a static state of being (Luthar, 2000). All individuals show fluctuation as time passes within various modification domains. Luthar et al. (2000) claims that, "Individuals at high risk rarely maintain regularly positive adjustment over the long term". Therefore because individuals show high results in one domains at one point in time this will not guarantee that they can maintain high results in that same domain over time. Risky individuals may be confronted with new vulnerabilities or recently effective protective factors may no more be there thus one must be critical in examining long-term resilience from a mix sectional design that could merely indicate a resilient trajectory. Vitaro (1996) conducted a longitudinal study of sons of male alcoholics and implemented them from era 6 through 14. This analysis showed that children revealed resilience and problem behaviors in various domains as they aged. It exhibited how considering different era periods permits examination of fads and can help identify when maladjustment could occur and prevention attempts could be made before this time around.
Variables that provide as a protecting factor may not be steady across all results. O'Donnell's (2002) analysis of metropolitan children exposed to community violence found that outcomes were 3rd party and acquired different predictors with regards to the level of risk factors. O'Donnell discovered that positive support from parents and universities were positively associated with resilience in children subjected to assault and these organizations increased as the child's degree of exposure to assault increased. She discovered that different types of support have different effects on children and that the effects varied based on degree of exposure to violence. Especially that peer support was adversely associated with resilience in the results domains of substance abuse and delinquency.
1a) Although some research workers use the terms moderator and mediator interchangeably there are some important distinctions between these two types of factors. Moderators take place when the partnership between two variables is modified with a third variable. The modifying can either offset the impact or exacerbate the impact of the relationship between X and Y variables. A moderator variable can be qualitative or quantitative and it affects the course or durability of the relationship between the indie and dependant factors (Baron & Kenny, 1986). A moderator effect is said to occur when the relationship between these two parameters is reduced or reversed when the moderator variable is added. So, the relationship between two variables changes as a function of the moderator adjustable.
The mediator variable can be an interviewing variable that means it is so the romance between adjustable X and variable Y can be accounted for fully or partially by the third mediating variable. Regarding to Barron & Kenny (1986) a mediator variable must meet three conditions:
Variations in levels of the independent changing must account for large servings of the deviation in the mediator
Variation in the mediator must take into account large helpings of the variant in the dependant variable
When the mediator variable is controlled for then a previously significant marriage between the 3rd party and dependant variable becomes significantly less significant
So, to establish mediation there must be a strong romantic relationship between the mediating variable and the self-employed changing as well as the mediating varying and the dependant invariable. Thus, a variable is called a mediator when it accounts for the relation between your unbiased and dependant varying. Moderator variables specify when certain results will keep true and mediators specify how or why such results occur (Baron & Kenny, 1986). Mediators make clear the device or underlying procedure for how X relates to Y.
1b) Typically you might test for a mediating changing when there is a strong correlation between your impartial or predictor varying and the dependant or outcome variable. In contrast, moderator factors are released when you find an unexpectedly vulnerable relationship between your predictor and outcome variable. This can occur when one has subpopulations within the same test or one may find a marriage between two variables in one setting up but not another.
2) Protective factors were defined by Kumpfer (1999), as "operations that are predictive of successful life version in high-risk children (populations)". These protecting factors offset risk, buffer or lessen risk and negative effects. Garmezy (1983) defined them as "those features of persons, conditions, situations, and happenings that appear to temper (mediate) predictions of pathology established upon an individual's at- risk position. " Some researchers argue that protective factors can only be significant in the presence of risk therefore Sameroff (1999), offered an improved phrase to spell it out the positive pole of any risk factor; promtoive factors. These promotive factors can be found in both high risk and low risk populations and have direct main results on positive results or lessening of negative effects. This is in contrast to protective factors which have an interactive impact and actually lower the chance factor itself. Promotive factors aid in positive outcomes whatever the risk factor (no risk factor is essential), nevertheless they also do not actually offset the risk factor. Precisely the same variable can be considered a protective and promotive factor, but protective factors would generally have "no result in low risk populations or be magnified in the occurrence of risk variables" (Gutman, 2002).
3) Conceptual Models
Alcohol Usage of Adolescent Girls
Drug Using Peer Group Affiliation
Moderation: Maternal support has a moderating effect on the alcohol use of adolescent females.
Promotive Factor Effect: DARK-COLORED Adolescents
# of risk factor
# of college absences