One thing your should think about from the very beginning is the integration of the qualitative and quantitative results. Too, often it seems that people are attracted by the value of having more data from different sources, without enough attention to how to bring that data into meaningful contact.

I tell my graduate students that mixed methods can sometimes be three times as hard as using a single method, because you not only have to do solid research with two different methods, but it also can take just as much effort to integrate what you learn from those different methods.

Of course, there is always the option of "minimal integration" where you simply have separate Results sections for each method, and possibly some Discussion of their mutual implications. This is still a common way of doing things, but I would treat it as a fall back strategy rather than a goal.

One thing your should think about from the very beginning is the integration of the qualitative and quantitative results. Too, often it seems that people are attracted by the value of having more data from different sources, without enough attention to how to bring that data into meaningful contact.

I tell my graduate students that mixed methods can sometimes be three times as hard as using a single method, because you not only have to do solid research with two different methods, but it also can take just as much effort to integrate what you learn from those different methods.

Of course, there is always the option of "minimal integration" where you simply have separate Results sections for each method, and possibly some Discussion of their mutual implications. This is still a common way of doing things, but I would treat it as a fall back strategy rather than a goal.

I recommend that you speak or read the articles of "Marta Castañer", University of Lleida. She is an expert in this topic. 

mixed method is an approach that help to have a more in-depth information and knowledge of the problem as well as provide rich datasets. It also assist for to increase findings reliability and credibility through the triangulation of the difference evidence results. Through this, generalisation of the study findings can be proposed.

However, it is one of approach that may seen to very difficult to manager and require much more analysis and rendition. More time and resources are involved and may be boring to the researcher.

Mixed methods research CAN provide broader, deeper, and/or more useful information: no single method is without its limitations, and different methods can provide complementary information that makes up for the shortcomings of using only one method.

However as noted by David Morgan (above), you do need to plan from the outset HOW you will integrate the findings from the different components of your research.

When considering mixing methods, it is also important to consider whether the different methods reflect different epistemological approaches (e.g. realist versus social constructionist). If so, then there may be challenges because in addition to doing each component well, one must justify what some see as incompatable epistemologies.

One counter to this argument is to emphasise pragmatism, but this is not a simple task

I have also been considering a mixed methodology approach in my research and currently evaluating the pros and cons. I would suggest going through Venkatesh et al. (2013) plus its references on bridging the qualitative-quantitative divide (although this is more for an IS-oriented audience) and Edmondson and Mcmanus (2007) -methodological fit in management field research. There are of course many others and depends on your own field of research.

Michael, If your are doing an IS based research, I would encourage to read materials on Design Science research methodology as it better your understanding.

I have, and if you go through the IS literature you will see that, in addition to DSR, there are purely qualitative, quantitative and studies with more emphasis on one approach, but integrating aspects of the other. So not all IS research is or should be DSR.

For the mixed methods approach several definitions exist: it is a research inquiry that employs both qualitative and quantitative approaches in a mixed methods research work for the purposes of breadth and depth of understanding and partnership (Johnson et. al., 2007). Creswell and Plano Clark, (2011) added that the indispensable premise of mixed method design is that the use of qualitative and quantitative, in rapport, will provide a better understanding of the research problems than the use of either one method alone in a study. This is argued to be one, if not, the most central premise of the pragmatic philosophical reasoning in research today (Tashakkori and Teddlie, 2003; Ihuah and Eaton, 2013). Please you can read these resource for more details.

I suggest we should know about the purpose of using any design, then we can think of the advantages and disadvantages. Here is what we should read : 

Six Mixed Methods Design Strategies (Creswell, 2003)

1. Sequential Explanatory 

Characterized by: Collection and analysis of quantitative data followed by a collection and analysis of qualitative data.

Purpose: To use qualitative results to assist in explaining and interpreting the findings of a quantitative study.

2. Sequential Exploratory

Characterized by: An initial phase of qualitative data collection and analysis followed by a phase of quantitative data collection and analysis.

Purpose: To explore a phenomenon. This strategy may also be useful when developing and testing a new instrument

3. Sequential Transformative

Characterized by: Collection and analysis of either quantitative or qualitative data first. The results are integrated in the interpretation phase.

Purpose: To employ the methods that best serve a theoretical perspective.

4. Concurrent Triangulation

Characterized by: Two or more methods used to confirm, cross-validate, or corroborate findings within a study. Data collection is concurrent.

Purpose: Generally, both methods are used to overcome a weakness in using one method with the strengths of another.

5. Concurrent Nested

Characterized by: A nested approach that gives priority to one of the methods and guides the project, while another is embedded or “nested.”

Purpose: The purpose of the nested method is to address a different question than the dominant or to seek information from different levels.

6. Concurrent Transformative

Characterized by: The use of a theoretical perspective reflected in the purpose or research questions of the study to guide all methodological choices.

Purpose: To evaluate a theoretical perspective at different levels of analysis.

You simply need to consider the power of numbers and words together, and see how you will integrate your results in a meaningful way. Integration of your QUAL and QUANT data is the most important thing as you need to avoid presenting two "separate" data sets (QUAL and QUANT) in one study!!!

By using mixing both quantitative and qualitative research, the researcher gains in breadth and depth of understanding and corroboration, while offsetting the weaknesses inherent to using each approach by itself.