The present results are compatible with the hypothesis that emotional abuse leads to DSH via self-hatred, but the cross-sectional nature of the study precludes any causal conclusions. Self-hatred and CM in the form of emotional abuse may be distinguishing characteristics of female patients with DSH in psychiatric settings. The DSH-group had significantly more positive attitudes towards DSH than the healthy comparison group. The effect of emotional abuse was mediated by self-hatred. This study aimed to test whether patients with DSH report more CM, more negative attitudes towards oneself, and more positive attitudes towards DSH than a clinical and a healthy comparison group, and whether the effects of CM are mediated by negative attitudes towards oneself.įemales with DSH and psychiatric disorders (n=34), females without DSH but with psychiatric disorders (n=31) and healthy female individuals (n=29) were compared regarding DSH, CM, attitudes towards the self and attitudes towards self-harm.įemales with DSH reported more emotional abuse and more self-hatred as compared to both comparison groups. Childhood maltreatment (CM) and attitudes both towards oneself and towards DSH may be of importance for the development of DSH. It does not store any personal data.Deliberate self-harm (DSH) is common in clinical populations. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category 'Performance'. This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category 'Other. The cookies is used to store the user consent for the cookies in the category 'Necessary'. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category 'Functional'. The cookie is used to store the user consent for the cookies in the category 'Analytics'.

These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. Hope you found this guide helpful! And last but not least, Groups of observations with small distances among them are assumed to represent clusters such as market segments. The basic idea is illustrated below.Ĭluster analysis is based on distances among observations -often people. Groups of variables that correlate strongly are assumed to measure similar underlying factors -sometimes called “constructs”.

cluster analysis for finding groups of observations (“clusters”).įactor analysis is based on correlations or covariances.factor analysis for finding groups of variables (“factors”) and.The 2 main types of classification analysis are Classification AnalysesĬlassification analyses attempt to identify and describe groups of observations or variables. Keep in mind, however, that the analyses listed below don't prove causality. Depending on the contents of an analysis, Prediction analyses sometimes quietly assume causality: whatever predicts some variable is often thought to affect this variable. The simplest example is simple linear regression as illustrated below. Prediction tests examine how and to what extentĪ variable can be predicted from 1+ other variables. Overview Association Measuresĭiscriminant analysis or ordinal regression analysisĭiscriminant analysis or nominal regression analysis “Independent samples” means that subsamples don't overlap: each observation belongs to only 1 subsample. “Between-subjects” tests are also known as “ independent samples” tests, such as the independent samples t-test. Note that the subpopulations are represented by subsamples -groups of observations indicated by some categorical variable. The best known example is a one-way ANOVA as illustrated below.

a parameter (population mean, standard deviation or proportion) or.One-way ANOVA (means) Levene's test (variances)īetween-Subjects Tests - Quick Definitionīetween-subjects tests examine if 2+ subpopulations Independent samples t-test (means) Levene's test (variances) Kruskal-Wallis test (mean ranks) Median test for 2+ independent medians Mann-Whitney test (mean ranks) Median test for 2+ independent medians

Point biserial vs point measure