The limitations are divided into two sections which deal with the limitations of the resident survey and statistical analyses as well as with the limitations of the interviews and content analysis.
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7.1.1 Resident survey and statistical analyses
First, it must be emphasized that the results would have been more informative and comprehensive by creating more than one dummy variable for the ordinal variables with more than two categories. In this analysis it was not performed because there would have been too many dummy variables which would have complicated the regression analysis. Also, the metric variable Age could have been recoded into a categorical variable to be clearer about which age categories are significantly predicting the Mental Health score. Further regression analyses with these data must control for potential confounding factors with the help of hierarchical analysis and should focus on variables of less spheres.
A considerable limitation is the presence of missing values. In the regression analysis the sample is reduced to 218 cases due to listwise exclusion. Initially, the sample size was 808 cases and therefore 590 cases were not considered. For further analyses one could think about the multiple imputation or maximum likelihood method to replace the missing values. The most missing values have the variables Income, NCD, Resilience, Sense of Community, Mental Health and Age because many participants did not want to give an answer. For example, for the variable Income, 32.5% of the participants did not respond which may be because disclosing the income situation is something very personal for many people. The same may applies for disclosing NCD, resilience and mental health as well as sense of community and the age. Although there are no missing values for the neighborhoods because the participants were not requested to answer where they live, the representation of the neighborhoods with their social status index varies. The neighborhood in Wilhelmsburg with a very low social status index and the neighborhood in Lohbrügge with a low social status index could not reach nearly 150 participants respectively. Furthermore, the descriptive analyses show that there are much more female participants and participants without a migration background, what may can be traced back to selection bias and self-selection bias.
Missing values and biases in single variables can be occurred due to many reasons during the data collection. Reasons can be that participants gave subjective answers, had to disclose private issues, an interviewer or/ and a family member was present or not present, and an interviewer could choose the potential participants from a list based on name and gender. It is conceivable that there were misunderstandings, over- and underestimation, extreme response bias, social desirability bias, recall bias, interviewer bias and non-response bias when non-responding one or more questions
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during the interviews. These biases, except interviewer bias, are also possible by self-completion. Furthermore, there was non-response bias when non-responding the whole questionnaire due to refusal or inaccessibility of the person. Also, selection bias by the interviewers and self-selection bias by the participants may have occurred. It is to emphasize that especially answers to mental health related questions may be biased or were not answered because of stigma as well as attitudes and mental health literacy of the participants. These barriers were found to have a negative effect on help-seeking (see Bonabi et al, 2016; Clement et al, 2015; Schnyder et al, 2017). It is even more probable due to the interview results. Some interview participants said that the residents in the deprived neighborhood Großlohe lack awareness and acceptance regarding mental health and mental ill-health as well as mental health literacy.
Moreover, causal relationships cannot be verified with this study design; this is mainly because the cross-sectional study do not provide information on the chronological sequence of exposure and outcome. For example, a bad sleep quality may lead to a bad mental HRQOL but also a bad mental HRQOL may lead to a bad sleep quality.
7.1.2 Interviews and content analysis
First, it needs to be mentioned that two study participants did not have more than thirty minutes time for the interview. It may have led to not exhaustive results although the participants said that they have nothing more to say. Additionally, one interview was accompanied by disturbances as it took place outside and it started to rain, and people passed by what have influenced the interview flow. Another possible limitation affecting reliable results is that the participants are doing low-threshold work in the neighbourhood under investigation. Therefore, they may have a strong personal and professional interest in improvements, for example regarding financial issues, personnel and equipment, which may have led to biased results. Nevertheless, the participants are experts and know exactly the situation on site and the effect of not telling exactly the truth is considered as very low. Furthermore, the relationship between participant and interviewer can be a limitation but which is also considered as very low. The interviewer knows one participant because during the data collection for the resident survey they have established a trusting relationship. In this case, one can assume that it has resulted in more exhaustive and reliable results than in the other interviews. Furthermore, it needs to be mentioned that, even though the ‘bracketing out’ was conducted successfully in the phase of data collection, there is the likelihood
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that the researcher commented too much on what was said in the interviews or missed to ask further questions on relevant topics. Additionally, there is the likelihood that the subjectivity of the researcher affected the process of data analysis.