Hi Nick

Both of them are mixed research designs. However, many studies have used the second one 'Quant-Qual design' which I think is more appropriate. This may explain by the fact that when someone ask a quantitative questions, usually followed by interviews 'qualitative questions or methods' in order to deeply understand some interesting responses and results that have been recorded from quantitative data. While the first one 'Qual-Quant' may be sometimes meaningless especially when you use a validated survey from the literature and you do not need to create a new one.

Good Luck

HI, Nick,

I think it depends entirely on the extant research in your field. If reliable and valid instruments exist, but the quantitative findings are inconsistent, then the quantitative methodology is missing something. In this case a QUAL-QUANT approach is warranted. You need to find in the qualitative data something new that you can test quantitatively. This approach is also preferred in those instances in which there is very little known about the area of research. The priority on qualitative methods presumes that you need to discover what the quantitative methods have missed.

I believe it is harder to justify the QUANT-QUAL approach. Why bother with the qualitative analysis if the quantitative instruments are providing reliable, valid, and consistent findings?

Hi Nick,

Both Mansour and Peter have presented good points. At the 'end of the day' - it really does depend on what you want to achieve. Mixed methods is a commendable paradigm - but there are many different version (both sequential and simultaneous). The attached chapter (although the previous edition of the latest text) may assist. 

Thank you for your kind contributions. Clearly this is an issue of some debate and it reinforces that whatever path I pursue I need to have rigorous justification for having done so. 

The best advice is to start with a research goal, rather than research design. Both the exploratory sequential (qual -->QUANT) and explanatory sequential (QUANT --> qual) designs are widely used in Mixed Methods Research, but as you note in your descriptions of them, they serve very different purposes.

I also agree with Peter that it can be difficult to justify the QUANAT --> qual design in advance, since it implies that there will be poorly understood aspects of your quantitative results, and thus a need for further qualitative research. Of course, that may indeed be the case, but how can you predict that in advance?

Hi Nick,

mixed methods is a solid approach and as a qualitative researcher first it would depend on your institution on if they would find quant->qual an issue at all.  I see great value in adding the richness of the data you can get from qualitative and do not believe it is an indication at all on the quality of the quant findings.  I am currently on such a project where we have indicated that points of interest identified within the quantitative analysis will be further explored through the use of a qualitative methodology. 

There are also practical issues in terms of the scope of a doctoral project - you would have to clearly describe and defend your choice in this approach and from a qualitative perspective the use of interviews does not clearly articulate the qualitative approach you would want to take on.  There are multiple approaches to qualitative and many of these are chosen to support the research question - so that really always comes first as noted previously.

Creswell and Poth have a great book on an introduction to qualitative approaches but they do miss one - phenomenography - which I find often ties very well with a quantitative perspective. 

 Hi Nick

I found the use of the QUAL-QUANT (sequential) approach challenging,  useful and extremely rewarding.  I incorporated the use of the Delphi Technique/ methodology in an attempt to determine opinions among individuals in a given field.  The Delphi technique is particularly useful as the methodology requires that you get controlled feedback on both the qualitative and quantitative data collected during the research from the research subjects. There may be agreement or disagreement among the participants but all this information can be collected and used appropriately to support of refute certain arguments.  Hope this has helped...

Dear Nick

I agree with many colleagues who have given their opinion. The design to choose depends on the state of knowledge in your research topic. If it is a mature area, choose a confirmatory sequential design (QUANT-QUAL). If it is an area with few antecedents choose an exploratory sequential design (QUAL-QUAN). While mixed-method studies generate synergy with data integration, it is a very demanding path. You must develop qualitative and quantitative skills.

Greetings,

Jorge

You can download my workbook on qualitative and quantitative methods for social sciences at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2407141 This provides advantages and disadvantages of each. I do advocate mixed-methods.

I always say you need to ask yourself the question "what is the best way to resolve this research problem?" What type of data collection activities will this likely involve? This should guide your research design. As already indicated by others' responses, what is already known in the field will play a part in your final design. Start with the ideal then tweak it if necessary to make it achievable within possible time constraints without compromising rigour, trustworthiness etc.

The driver for decision making should be your research question and goal. Leaving other discussion points aside, it would be nice to think that if a new survey instrument is to be developed, that its content is informed by qualitative research, literature in the field or both. Please note that some responses above refer to a mix of upper and lower case QUANT-Qual or vice versa and these have important implications in MM for the priority/status afforded to each. Regarding the case for QUANT-QUAL, sometimes you may want to drill down below the surface and gain insight into what the figures and stats really mean for people. 

Qual.  Marketing skills & benefits can be statistically measured globally for professional service providers in their profit & losses / shares on the FTE  

Quant will be individual client/customer choice and preference for what constitutes loyalty to a service provider (adds meaning to stat data)

Agree with Merylin driver will be research question and goal.  Cross-cultural comparison???  is ultimate goal customer loyalty or marketing skills of provider.  Research question or hypothesis?  Good luck with Phd

Though there is support for both approaches, u need to find it out by urself by answering the following (hypothetical scenario): 

Which one makes more sense to u? 

1. After a party at ur home, discovering that half of the friends didn't like the food and half liked. Then going to them and asking what they like, what they don't etc. If it excites u, then quanti-quali method is suggested for u.

2. Asking first the friends what they like, what they don't. Then offering a party and discovering that two third liked it and one third didn't. If this makes better sense to u, then choose quali-quanti method. You can see here that more satisfaction/fulfilment is coming to society (ur more friends are happy here as compared to first scenario) from ur research in this way. 

This way, think carefully about the research problem first, and then decide which approach serves the society/humanity better...TQ...

To what populations do you wish to generalize your study's findings? This will determine the degree with which your study can be parochial or representative of larger populations or groups.

Health care services and clients are decidedly variable...diverse in many ways. Recognizing this at the levels of patients/communities and also the type of health service by medical discipline, setting, community, accessibility, etc. is an critical step in your research design and data collection strategy.

Although this thread is like three (3) years old now, my contribution is not likely to be useful for the candidate but for the benefits of others who may want to use mixed-methods research design. Just like the preceeding responses, using mixed-methods design is not all about ordinarily hybriding quantitative and quantitative methods (QAUNT + QUAL). It is a complex design that has numerous typologies, approaches and frameworks that are utilised at different levels of research. From my perspective, If you must use mixed-methods design, you must first of all aquaint yourself with and be vast in the several designs under each of quantitative and qualitative methods in observational research. It is then you can transfer such knowledge to mixed-methods design.

Dealing with mixed-methods design thereafter, you need to have the knowledge of order, priority and integration. The order is which one is coming first and second, quantitative or qualitative method? The priority is which one are you giving priority, that is, of utmost importance to your study. Showing order and priority brings about the use of the formula:

QAUNT + qual = quantitative is first with higher priority than qualitative which is second with lower priority.

2. quant+ QUAL = quantitative is first with lower priority than qualitative second with higher priority

3. quant + qual = quantitative is first and qualitative is second with both having the same priority

4. qual + quant = qualitative is first and quantitative is second with same priority.

The integration deals with the type, time and level of study and when and where the mix is coming in. The questions of:

1. Is my quantitative aspect descriptive and cross-sectional or analytical and cohort longitudinal while my qualitative aspect historical or narrative?

2. Are data collection and analysis in my study to be done at same time (concurrent) or in succession (sequential)?

3. Is the mix coming up during data collection, analysis and/or interpretation? Hence, am I going for merging, building, connecting or embedding?

5. Should the analysis, interpretation and discussion of quantitative and qualitative data be triangulated?

It is when all of these are considered and worked out, have you been able to use mixed-method design.

The word in double asterisks shows an error being corrected. I have wanted to write qualitative. Thanks!

Although this thread is like three (3) years old now, my contribution is not likely to be useful for the candidate but for the benefits of others who may want to use mixed-methods research design. Just like the preceeding responses, using mixed-methods design is not all about ordinarily hybriding quantitative and qualitative** methods (QAUNT + QUAL). It is a complex design that has numerous typologies, approaches and frameworks that are utilised at different levels of research. From my perspective, If you must use mixed-methods design, you must first of all aquaint yourself with and be vast in the several designs under each of quantitative and qualitative methods in observational research. It is then you can transfer such knowledge to mixed-methods design.

Dealing with mixed-methods design thereafter, you need to have the knowledge of order, priority and integration. The order is which one is coming first and second, quantitative or qualitative method? The priority is which one are you giving priority, that is, of utmost importance to your study. Showing order and priority brings about the use of the formula:

QAUNT + qual = quantitative is first with higher priority than qualitative which is second with lower priority.

2. quant+ QUAL = quantitative is first with lower priority than qualitative second with higher priority

3. quant + qual = quantitative is first and qualitative is second with both having the same priority

4. qual + quant = qualitative is first and quantitative is second with same priority.

The integration deals with the type, time and level of study and when and where the mix is coming in. The questions of:

1. Is my quantitative aspect descriptive and cross-sectional or analytical and cohort longitudinal while my qualitative aspect historical or narrative?

2. Are data collection and analysis in my study to be done at same time (concurrent) or in succession (sequential)?

3. Is the mix coming up during data collection, analysis and/or interpretation? Hence, am I going for merging, building, connecting or embedding?

5. Should the analysis, interpretation and discussion of quantitative and qualitative data be triangulated?

It is when all of these are considered and worked out, have you been able to use mixed-method design.

I never knew how much interest I have in this topic until I stumbled on it again. I need to add that various classifications on the design and its models are contained in literature. All the thanks to Cresswell (2006) for the chapter contribution that provides much clarity on it. Cresswell indeed came up with four mixed design typologies which are (1) exploratory, (2) explanatory, (3) triangulation (dynamic), and (4) embedded. Although, I'm much inclined to three typologies which are the (1) exploratory, (2) explanatory and (3) triangulation design typologies, I really love the additional typology of embedded design. It is either a single-time frame (concurrent) or double-time frame design which could hardly be distinguished from other types of design. The basic idea and difference is that in embedded design, one method is just engrafted in the other method to address an or some objectives of the study or set to answer a or some research questions, thus serving a secondary or supplementary role. Depending on the purpose and time frame, one method might seem to gain priority in terms of weighing. Mixed methods design is quite a complex but beautiful approach to use in research when well understood. As I stated in my previous post, I prefer that users understand each of quantitative and qualitative methods in isolation before venturing into the hybrid. This is because in the mixed-methods design, for instance explanatory or exploratory, one might be restricted by virtue of the substructure of each method based on area and time of research.

Reference

Cresswell (2006). Choosing a mixed Methods Design. Sage Publications https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.sagepub.com/sites/default/files/upm-binaries/10982_Chapter_4.pdf&ved=2ahUKEwjw3sTJyob0AhWiS_EDHZglAyQQFnoECD4QBQ&sqi=2&usg=AOvVaw0GFWPm63fPj-hqOIG8gW3A.