Quantitative research is a systematic research of phenomena using methods involving the analysis of numerical data; these methods are usually mathematical, statistical, or numerical (University of Southern Carolina Libraries, 2017). The data is commonly collected via polls, surveys, etc.; after that, it is systematically generalized to a wider population. Usually, the goal is to investigate the relationship between two or more variables, some of which are considered independent variables, or predictors, while the rest are considered dependent variables, or outcomes.
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There are 3 main quantitative research approaches: experimental, quasi-experimental, and non-experimental. The non-experimental, or descriptive, approach usually involves measuring the population only once, and then observing or interpreting the results (Warner, 2013). This is done in case when the researcher is unable to manipulate the variables. For example, when a researcher wishes to find out if there is an association between race/ethnicity and levels of depression, they can use the non-experimental design.
(True) experimental and quasi-experimental designs are similar in that they involve manipulating the predictor variable(s) and measuring the sample several times (often twice) – before and after the intervention. However, in the true experiment, the researcher is able to randomly assign subjects to different groups, and minimize the change in the conditions of the experiment, i.e., ensure that other factors remain constant (Warner, 2013). For instance, to test a new medication in the clinical setting, medics may randomly assigns patients to two groups (experimental and control), and give the first group the new drug, simultaneously providing the other group with a placebo; the rest of the treatment will remain the same.
In a quasi-experiment, the researcher cannot randomly assign participants, and cannot control interfering variables (Warner, 2013). To give a crude example, a researcher comparing a new medication against clinical depression with an old one cannot randomly assign patients to treatment and control groups if e.g. ≈40% of the population are allergic to the new drug (but it might help the others very much nonetheless). Also, if the patients are not treated in the clinical setting, the researcher will not be able to control other variables (such as the changing situation in the family and at work for some participants), and may not even be able to ensure that all the subjects comply with the treatment schedule.
Relationship Between Self-Esteem and Depression
A possible topic of my future research is depression; in particular, the relationship between depression and self-esteem appears to be under-researched. This topic is related to my specialization (General Psychology) because it involves studying the relationship between certain mental states of individuals.
I did not previously do a purposeful literature review, so I had to do a review for this discussion. I used the Google Scholar search engine, as well as several databases such as APA PsycNET, EBSCOhost and ProQuest, to find some articles on depression and related topics (using such search terms as “depression anxiety,” “depression race,” “depression self-esteem”). The university’s library resources were utilized to gain access to the articles. The main problem was to find a gap in the currently existing knowledge of the topic; however, such sections in articles as “Limitations” and “Future research” were very helpful.
The preliminary search showed that the nature of the relationship between self-esteem and depression is currently not firmly established (Sowislo & Orth, 2013), making it a possible topic for future studies. Two main models of this relationship exist: the vulnerability model (low self-esteem makes one vulnerable to depression) and the scar model (depression adversely impacts self-esteem) (Orth & Robins, 2013). Currently, more support exists for the vulnerability model, but the relationship has not been properly confirmed yet (Orth, Robins, Widaman, & Conger, 2014; Sowislo & Orth, 2013).
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Therefore, a possible research problem is the relationship between depression and self-esteem. It is stated that if low self-esteem stimulates depression (the vulnerability model), then enhancing self-esteem might help reduce or prevent depression (Orth & Robins, 2013). Thus, apart from descriptively studying the relationship between depression and self-esteem, a more practical approach can be used to check how interventions for enhancing self-esteem might affect depression.
Orth, U., & Robins, R. W. (2013). Understanding the link between low self-esteem and depression. Current Directions in Psychological Science, 22(6), 455-460.
Orth, U., Robins, R. W., Widaman, K. F., & Conger, R. D. (2014). Is low self-esteem a risk factor for depression? Findings from a longitudinal study of Mexican-origin youth. Developmental Psychology, 50(2), 622-633.
Sowislo, J. F., & Orth, U. (2013). Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychological Bulletin, 139(1), 213-240.
University of Southern Carolina Libraries. (2017). Organizing your social sciences research paper: Quantitative methods. Web.
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.