Please write a paper in response to the article attached

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Question Description

Purpose: The purpose of this assignment is to expose you to current research in psychopathology so that you can see how concepts in class are discussed and applied by modern researchers.

You will use the following skills to complete this assignment:

  • 1)Find recent empirical research. ( I have attached the article you should use)
  • 2)Write clear and brief short answers to a question.
  • 3)Evaluate the strengths and weaknesses of a published empirical article.
  • 4)Identify how the findings apply to topics discussed in class.

You will use the following knowledge to complete this assignment:

  • Write a 2 page paper that provides a brief summary of the article in your OWN words (watch out for plagiarism).
    1. Page 1
      • i.Methodology
      • ii.Results
      • iii.Main Conclusion
    2. Page 2
      • i.Your general opinion of the quality of the article
      • ii.A critique of the article’s methodology (including major flaws or drawbacks)
      • iii.A future study you would conduct in this area based on the findings in the article (i.e., what would be the next logical step in research-based on the article’s findings).

Criteria:

You will be successful in this activity if you can: (a) write a succinct, thoughtful summary of the article that you read, (b) write clearly so that I can understand how you are thinking about this topic, (c) demonstrate your ability to evaluate the quality of a published empirical article, and (e) write a response of high quality (e.g., APA-style, proper grammar, spelling, etc.). Please note, I am evaluating your ability to creatively apply these concepts and then explain your thinking.

All papers should be turned in electronically via Webcampus by Friday at midnight of the corresponding week. The papers should be approximately 2 double-spaced pages long in 11 or 12 point, Times New Roman or Calibri font, and 1 inch margins.

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Journal of Abnormal Psychology 2015, Vol. 124, No. 2, 256 –265 © 2015 American Psychological Association 0021-843X/15/$12.00 http://dx.doi.org/10.1037/abn0000028 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms Eiko I. Fried Claudi Bockting University of Leuven University of Groningen and Utrecht University Retha Arjadi Denny Borsboom University of Groningen University of Amsterdam Maximilian Amshoff Angélique O. J. Cramer and Sacha Epskamp University of Groningen University of Amsterdam Francis Tuerlinckx Deborah Carr University of Leuven Rutgers University Margaret Stroebe University of Groningen and Utrecht University Spousal bereavement can cause a rise in depressive symptoms. This study empirically evaluates 2 competing explanations concerning how this causal effect is brought about: (a) a traditional latent variable explanation, in which loss triggers depression which then leads to symptoms; and (b) a novel network explanation, in which bereavement directly affects particular depression symptoms which then activate other symptoms. We used data from the Changing Lives of Older Couples (CLOC) study and compared depressive symptomatology, assessed via the 11-item Center for Epidemiologic Studies Depression Scale (CES-D), among those who lost their partner (N ⫽ 241) with a still-married control group (N ⫽ 274). We modeled the effect of partner loss on depressive symptoms either as an indirect effect through a latent variable, or as a direct effect in a network constructed through a causal search algorithm. Compared to the control group, widow(er)s’ scores were significantly higher for symptoms of loneliness, sadness, depressed mood, and appetite loss, and significantly lower for happiness and enjoyed life. The effect of partner loss on these symptoms was not mediated by a latent variable. The network model indicated that bereavement mainly affected loneliness, which in turn activated other depressive symptoms. The direct effects of spousal loss on particular symptoms are inconsistent with the predictions of latent variable models, but can be explained from a network perspective. The findings support a growing body of literature showing that specific adverse life events differentially affect depressive symptomatology, and suggest that future studies should examine interventions that directly target such symptoms. Keywords: bereavement, depressive symptoms, latent variable model, loneliness, networks Supplemental materials: http://dx.doi.org/10.1037/abn0000028.supp Major depressive disorder (MDD) is a highly prevalent disease (Kessler, Chiu, Demler, Merikangas, & Walters, 2005), and the majority of patients diagnosed with depression suffer from se- verely impaired functioning (Kessler et al., 2003). Experiencing an adverse life event, in turn, is a well-established predictor for developing depression (Hammen, 2005; Mazure, 1998), and de- This article was published Online First March 2, 2015. Eiko I. Fried, Faculty of Psychology and Educational Sciences, KU Leuven—University of Leuven; Claudi Bockting, Department of Clinical Psychology and Experimental Psychopathology, University of Groningen and Department of Clinical and Health Psychology, Utrecht University; Retha Arjadi, Department of Clinical Psychology and Experimental Psychopathology, University of Groningen; Denny Borsboom, Department of Psychology, University of Amsterdam; Maximilian Amshoff, Department of Clinical Psychology and Experimental Psychopathology, University of Groningen; Angélique O. J. Cramer and Sacha Epskamp, Department of Psychology, University of Amsterdam; Francis Tuerlinckx, Faculty of Psychology and Educational Sciences, KU Leuven—University of Leuven; Deborah Carr, Department of Sociology and Institute for Health, Health Care Policy & Aging Research, Rutgers University; Margaret Stroebe, Department of Clinical Psychology and Experimental Psychopathology, University of Groningen and Department of Clinical and Health Psychology, Utrecht University. Correspondence concerning this article should be addressed to Eiko I. Fried, KU Leuven—University of Leuven, Research Group of Quantitative Psychology and Individual Differences, Tiensestraat 102, 3000 Leuven, Belgium. E-mail: eiko.fried@gmail.com 256 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. BEREAVEMENT AND DEPRESSIVE SYMPTOMS pression rates are increased in individuals exposed to severe stress (Rojo-Moreno et al., 2002; Shrout et al., 1989). This has been documented in both clinical and community samples (Brown & Harris, 1989; Hammen, 2005). A diagnosis of MDD requires the presence of at least five of the nine DSM-5 criterion symptoms (American Psychiatric Association, 2013). These symptoms are commonly assessed via screening instruments such as the Beck Depression Inventory (BDI; Beck, Steer, & Garbin, 1988) and calculated by summing the number of symptoms one has. The idea underlying sum-scores is that depression symptoms are interchangeable indicators of the same unidimensional underlying disorder. This is called the common-cause hypothesis (Schmittmann et al., 2013), and in statistical models, reflective latent variables are used to describe this direction of causation. In such reflective models, changes in the latent variable (depression) lead to changes in the observed indicators (the symptoms). From this perspective, depression symptoms such as sadness, insomnia, or fatigue covary because they are triggered by the latent disease. Symptoms are regarded as measurements of depression, and aggregated symptoms reflect a person’s position on the latent variable. The common cause for depression is often assumed to reside in the brain of individuals diagnosed with MDD (e.g., Andreasen, 2001). If depression symptoms are understood as passive consequences of an underlying brain dysfunction, then identifying and treating such a common cause is indeed the most logical procedure. In recent years, however, a growing body of evidence has challenged the common cause model for depression. First, the DSM-5 diagnosis for depression encompasses a large number of disparate symptoms such as sadness, insomnia, or appetite problems, and three of the symptoms consist of contrasting features (psychomotor retardation or psychomotor agitation; weight gain or weight loss; insomnia or hypersomnia). This leads to about 1,500 unique symptom profiles that all qualify for the same diagnosis (Østergaard, Jensen, & Bech, 2011), including profiles that do not share a single symptom. For example, one recent paper documented 1,030 unique symptom profiles in 3,703 patients diagnosed with depression (Fried & Nesse, 2015). Although it is possible that a disease causes various syndromes—syphilis, for instance, is often referred to as “the great imposter” for that reason—it is unlikely that it causes many symptomatic opposites. Second, individual depressive symptoms vary with respect to their risk factors (Fried, Nesse, Zivin, Guille, & Sen, 2014), and their underlying biology (Kendler, Aggen, & Neale, 2013; Myung et al., 2012). Some symptoms show greater heritability than others, with heritability factors ranging from 0.0 to 0.35 (Jang, Livesley, Taylor, Stein, & Moon, 2004). Third, the etiology of depressive symptoms is complex and multifactorial, featuring biological, psychological, and environmental influences (Kendler, 2012). Fourth, cross-sectional studies have documented that specific life events such as failing at an important goal or the death of a loved one are associated with particular depression symptom profiles (Cramer, Borsboom, Aggen, & Kendler, 2013; Keller, Neale, & Kendler, 2007; Keller & Nesse, 2005, 2006). Novel network models offer an alternative perspective to the common cause framework. In these approaches, depressive symptoms are not understood as passive and interchangeable indicators of a latent disease, but as distinct entities with autonomous causal power that influence each other (Borsboom & Cramer, 2013; Cramer, Waldorp, van der Maas, & Borsboom, 2010). Symptoms such as insomnia or 257 fatigue do not cluster because of a common cause—they cluster because they influence each other across time. Depression is not conceptualized as latent variable, but is understood to be constituted by the causal associations among symptoms. Here we examine the impact of one specific adverse event—latelife spousal loss— on a variety of depressive symptoms in a prospective study of older bereaved spouses with matched control participants. Losing a loved one is a strong and well-established risk factor for the onset of depressive symptomatology (Zisook & Kendler, 2007; Zisook & Shuchter, 1991), and a large literature has documented the impact of bereavement on psychological functioning, especially among older adults (Carr, Nesse, & Wortman, 2006; Knight & Silverstein, 2014). We aim to address two main questions. First, it is unclear how spousal bereavement affects depressive symptoms. From the perspective that depression is the common cause of its symptoms and thus explains symptom covariation, bereavement should affect a latent depression factor, which in turn should cause the symptoms (i.e., the effect of loss on symptoms is indirect and operates through the latent variable). The alternative hypothesis is that the effects are direct and propagated through a symptom network. In this case, one would expect that the life event triggers specific depressive symptoms which, in turn, activate other symptoms in a causal chain. To compare these competing hypotheses, we used data from the Changing Lives of Older Couples (CLOC) study, a prospective study of spousal loss among older adults (Carr et al., 2006). We fit both latent variable models and network models to the data and compare and discuss the results. Second, the question of whether bereavement is conceptually distinct from MDD has been discussed for decades and remains unresolved. The bereavement exclusion (BE) introduced in the DSM–III (American Psychiatric Association, 1980) conceptualized grief as normal response to loss and not as a mental disorder. The DSM–IV (American Psychiatric Association, 2000) narrowed down the BE substantially in order to avoid false-negatives, and the BE was replaced in the DSM-5 by a footnote that “caution[s] clinicians to differentiate between normal grieving associated with a significant loss and a diagnosis of a mental disorder” (American Psychiatric Association, 2013, p. 161). This decision was based on several systematic reviews documenting very few differences between bereavement-related depression and depression (Kendler, Myers, & Zisook, 2008; Zisook et al., 2012). Others have argued that bereavement is a normal and uncomplicated response to loss, noting that symptoms usually subside within weeks or months of the death; grief persists for a prolonged period of time among only a small minority of bereaved persons (Kersting, Brähler, Glaesmer, & Wagner, 2011). From this perspective, the removal of the BE brings the dangers of misdiagnosing normal sadness as pathological depression and medicalizing a normal condition (Bonanno et al., 2002; Friedman, 2013; Nesse & Stein, 2012; The Lancet, 2012; Wakefield, 1997). Our analysis of symptom dynamics among recently widowed individuals may offer new insights into the question of whether bereavementrelated depression is a distinct condition. Methods Participants Data from the CLOC study (Carr et al., 2006) were analyzed to examine the impact of bereavement on depression symptoms. A This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 258 FRIED ET AL. prospective sample of 1,532 married men and women age 65 or older from the Detroit Metropolitan Area were enrolled. Participants were English-speaking, noninstitutionalized, and able to participate in a 2-hr face-to-face interview. Individuals who lost a spouse during the course of the study were invited to follow-up interviews at 6, 18, and 48 months after their partner’s death. We used data from the first follow-up interview (Wave 1) 6 months after spousal loss. Of the 335 individuals who had lost a spouse, 250 (74.6%) participated in the Wave 1 interview. Bereaved participants were matched regarding age and gender with control participants from the baseline sample who had not lost a partner. Because of the funding constraints, the number of controls at Wave 1 was small (N ⫽ 84). In our analysis, we thus pool control subjects from all three follow-up waves (N ⫽ 280). Outcome Measures Depressive symptoms were measured with the 11-item version of the Center for Epidemiologic Studies Depression Scale (CES-D) on each measurement occasion (Kohout, Berkman, Evans, & Cornoni-Huntley, 1993); this scale is an abbreviated version of the original 20-item CES-D (Radloff, 1977). For each item, participants indicated the frequency with which it had occurred during the past week. Response categories were “hardly ever,” “some of the time,” or “most of the time.” The 11 CES-D items are (abbreviated names used in the remainder of this text in brackets): “I felt depressed” (depr), “I felt that everything I did was an effort” (effort), “My sleep was restless” (sleep), “I was happy” (happy), “I felt lonely” (lonely), “People were unfriendly” (unfr), “I enjoyed life” (enjoy), “My appetite was poor” (appet), “I felt sad” (sad), “I felt that people disliked me” (dislike), and “I could not get going” (getgo). Because the behavior of skewed polytomous items in networks, such as CES-D symptoms, is not well understood, we dichotomized item-scores into an absent (0) and present (1) code. Such networks of binary variables can then be studied using the Ising model (van Borkulo et al., 2014). For the nine negative items, “hardly ever” was coded as absent symptom, whereas “some of the time” and “most of the time” were coded as present symptoms. Because the two items enjoy and happy are reverse-coded in the CES-D (where a high value indicates less frequent depressive symptoms), we dichotomized them accordingly. “Hardly ever” and “some of the time” were coded as being absent, and “most of the time” as being present. We reversed the two positive items in analyses of sum-scores. Statistical Analysis Because of item-specific missing data on any of the 11 CES-D items, nine participants in the widowed group and six participants in the control group were excluded. This leaves 241 bereaved and 274 nonbereaved participants in the analytic sample. We compared the widowed and the control groups regarding their overall symptom load (the CES-D sum-score) at baseline and at follow-up, using Welch two sample t tests; these tests adjust the number of degrees of freedom when the variances of the compared groups are not equal to each other. Furthermore, we used multivariate analysis of variance (MANOVA)1 to investigate whether individual symptoms differed across groups. We then assessed two competing hypotheses that offer different explanations for the ways that bereavement affects depressive symptoms. First, the common cause perspective predicts that a latent depression variable explains symptom covariation. As such, bereavement should affect a latent depression factor, which in turn should cause the symptoms: The effect of loss on symptoms is indirect and operates via a latent variable. To test this assumption we estimated two multiple indicators multiple causes (MIMIC) models (Jöreskog & Goldberger, 1975). MIMIC models contain a reflective latent variable (depression), items that indicate the presence of the latent variable (11 depressive symptoms), and one or more variables that have an impact on the latent variable (bereavement). We set up the first model (Model 1) so that the spousal loss was only allowed to affect the latent variable. This was then compared to a nested Model 2 in which loss was allowed to directly affect symptoms (not mediated by the latent factor). If Model 2 fit the data significantly better, this means that the common cause framework (Model 1) does not describe the data well. Consistent with previous publications (Fried et al., 2014; Jones, 2006), we estimated Model 2 in an iterative process. In a first step, bereavement was allowed to have direct effects on all symptoms except for one symptom for purposes of identification. In a second step, nonsignificant paths were removed until only significant estimates remained. The weighted least squares means and variance adjusted estimator was used to fit the models, and models were compared with a ␹2 difference test. Model fit was examined using the root mean square error of approximation (RMSEA; ⱕ .06 indicating a good fit) and the comparative fix index (CFI; ⱖ .95 indicating a good fit) (Hu & Bentler, 1999). Second, the network approach offers an alternative explanation in which the effects of loss on symptoms are propagated through a symptom network. To explore this hypothesis, we constructed a network through a causal search algorithm. In networks, each node represents a symptom, and the connections (called “edges” in the network literature) between nodes can be understood as direct influences. Consistent with the previous analysis, we integrated spousal loss into the model to examine whether it is connected to the network, and if so, to which symptoms. The network was fitted using an Ising model (van Borkulo et al., 2014) via the R-package IsingFit. An Ising model is a probabilistic model in which the joint distribution over K binary variables (11 items and the loss variable) is represented using threshold parameters (related to the marginal probability of endorsement of any individual item) and pairwise association parameters (related to the associations between the variables). An unconstrained Ising model for our data has 12 threshold parameters and (12 ⴱ 11)/2 ⫽ 66 pairwise association parameters to be estimated. Of main interest are the pairwise associations that are represented as a network. These pairwise association parameters are similar to partial correlation coefficients for continuous normally distributed variables: They are direct associations between nodes controlling for all other associations. More pairwise association parameters in the model lead to a more complex model (with possibly many spurious 1 Instead of the MANOVA we also used a logistic regression approach to better account for the binary nature of the symptom variables. Since the results were essentially unchanged, we report the conceptually simpler MANOVA. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. BEREAVEMENT AND DEPRESSIVE SYMPTOMS connections). For this reason, the method employed here uses an estimation procedure with a penalty approach (i.e., eLasso based on the Extended Bayesian Information Criterion or EBIC; for further details, see Ravikumar, Wainwright, & Lafferty, 2010) to identify only the relevant relationships between variables. A detailed explanation of the Ising model, the estimation procedure, and its properties can be found elsewhere (van Borkulo et al., 2014). Results of the causal ...
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Running head: RESPONSE TO THE ARTICLE

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Response to the article
This article titled, “From Loss to Loneliness” was written by Fried et al. in 2015 in the
Journal of abnormal psychology. This article is about research on how spousal bereavement may
lead to intensive depressive. The authors seek to answer two questions. The first one is how
depressive symptoms are affected by the spousal loss. The second question is to find out whether
bereavement depression is distinct from Major depressive disorder (Fried et al., 2015). In order
to find the answers to the two research question mentioned above, the methodology of
experience sampling was used, and ...

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