Question

You have been asked, as a part of your college statistics course, to research the impact of clinical depression on an individual’s work performance.

In 750-1000 words, you will be answering and researching the following questions:

1. Which type of method will you use to study this topic? Qualitative vs. Quantitative.

2. Who will be your chosen participants? What considerations must be undertaken in choosing your participants?

3. What sampling method will you use for your study? What considerations must be undertaken in choosing a sampling design? What challenges can occur in choosing the wrong design?

4. How will descriptive statistics be important for your study? How will inferential statistics be important for your study?

5. What challenges can you face when it comes to generalizability, validity, and reliability? Why are these concepts important to a research study?

Someone please help

Answer 1

1.Qualitative in-depth interviews .Qualitative methods are used to provide a “thick description” or depth of understanding to complement breadth of understanding afforded by quantitative methods, elicit the perspective of those being studied, explore issues that have not been well studied, develop conceptual theories or test hypotheses, or evaluate the process of a phenomenon or intervention. Qualitative methods adhere to many of the same principles of scientific rigor as quantitative methods, but often differ with respect to study design, data collection and data analysis strategies. For instance, participants for qualitative studies are usually sampled purposefully rather than at random and the design usually reflects an iterative process alternating between data collection and analysis. The most common techniques for data collection are individual semi-structured interviews, focus groups, document reviews, and participant observation. Strategies for analysis are usually inductive, based on principles of grounded theory or phenomenology. Qualitative methods are also used in combination with quantitative methods in mixed method designs for convergence, complementarity, expansion, development, and sampling. Rigorously applied qualitative methods offer great potential in contributing to the scientific foundation of mental health services research.

2.i will choose the people from the working population .Age between 25 to 60. As these individuals would actually know what it actually to be in clinical depression and work at the same time. In qualitative research your target population are limited in terms of the rationale/objectives of your research. That is, not every individual will have the expert knowledge to respond to your questions. As such, you will as the researcher, need to ask yourself some few questions such as: what am I looking for? who can best respond to my questions? where do I get them? etc. A reconnaissance study will help you answer some of these questions. The first criterion to selecting your respondent could be a purposeful sampling, especially when you know where to locate them (respondents). Where they are difficult to locate, snow balling will be best after you get access to one potential respondent. In short, select respondents for interview base on their level of experience on your research objectives.

3.For my study i will choose snow ball,stratified sampling technique.Choosing to undertake sample-based surveys is based primarily on the recognition that complete enumeration through census-based surveys imposes huge costs that are both unsustainable and unnecessary if the nature and methods of statistical sampling are properly considered. Such considerations include understanding of:

• the reasons for and objectives of sampling.
• the relationship between accuracy and precision.
• the reliability of estimates with varying sample size.
• the determination of safe sample sizes for surveys.
• the variability of data.
• the nature of stratification and its impact on survey cost.
• the risks posed by biased estimates.
• the differences between “boat” and “gear” statistical approaches.

4.Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data. Descriptive statistics are very important because if we simply presented our raw data it would be hard to visualize what the data was showing, especially if there was a lot of it. Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. It relies on Descriptive Statistics where we get to know our data both graphically and with the use of descriptive statistics to explore among others issues of (a)symmetry and (if there is) skewness.

It also relies on Inferential Statistics where with the use of samples we infer for the population This is an important part relying on the use of probabilities and of probability distributions, sampling and sampling distributions, interval estimations and, hypothesis testing. Moreover we have analysis of one or two samples, moving to 3 or more and all these in a parametric or a non-parametric set up depending on the assumptions. We also have experimental designs, quality control and multivariate analysis (with cluster, factor, multiple regression and discriminant analyses; multivariate analysis of variance, conjoint analysis, multidimensional scaling, etc)