qualitative and quantitative analysis

QUANT VS QUAL ROLE OF THEORY IN RESEARCH

= deductive (quant); inductive (qual)

QUANT VS QUAL EPISTEMOLOGICAL ORIENTATION

= positivist (quant); interpretivist (qual)

QUANT VS QUAL ONTOLOGICAL ORIENTATION

= objectivist (quant); constructivist (qual)

QUANT VS QUAL DATA TYPE

= numbers/statistics (quant); words/meanings (qual)

QUANT VS QUAL DATA QUALITY

= concrete/reliable data (quant); rich/deep data (qual)

QUANT VS QUAL POV

= researchers (quant); participants (qual)

QUANT VS QUAL RESEARCHER INVOLVEMENT

= distant (quant); close (qual)

QUANT VS QUAL THEORY AND CONCEPTS

= tested in research (quant); developed through research (qual)

QUANT VS QUAL STRUCTURE

= highly structured (quant); semi-structure/unstructured (qual)

QUANT VS QUAL END GOAL

= generalizability (quant); contextual understanding (qual)

QUANT VS QUAL SIZE OF SOCIAL PHENOMENON

= macro (quant); micro (qual)

QUANT VS QUAL FOCUS

= behaviour (quant); meaning (qual)

QUANT VS QUAL SETTING

= artificial (quant); natural (qual)

MIXED METHOD RESEARCH

 = allows researchers to use a diversity of methods to combine inductive and deductive approaches -> draws on the strengths and offsets the limitations of exclusively quantitative and qualitative research

FOUR APPROACHES TO MIXED METHOD RESEARCH

1. Triangulation design

2. Embedded design

3. Explanatory design

4. Exploratory design

TRIANGULATION DESIGN

 = the most common approach; gaining different but complementary data on the same phenomenon -> implement them at the same exact time at equal weight

TRIANGULATION STRENGTHS (2) AND CHALLENGES (2)

Strengths:

• Intuitive 

• Efficient -> as we are doing both as the same time

• Team approach if desired -> can create collaborative communities

Challenges: 

• Requires effort and expertise 

• Potential contradictory results

EMBEDDED DESIGN

= one data set provides support or plays a secondary role in a study based primarily on the other data type -> useful when needing to embed a qualitative component with quantitative design (e.g. experimental design) and therefore one of the datasets plays a supplemental role

EMBEDDED DESIGN STRENGTHS AND CHALLENGES

Strengths 

• Efficient

• Logistically more manageable 

• May be appealing to funding

Challenges

• Identifying the primary and secondary purposes

• Can be difficult to integrate results 

EXPLANATORY (SEQUENTIAL DESIGN)

= two phase mixed method design - take what we learn from qualitative data to explain/build on quantitative results

• Useful when researchers want to form groups based on quantitative results and then follow up with those groups with qualitative research

EXPLANATORY STRENGTHS AND LIMITATIONS

Strengths

• Straightforward to implement and report results

• Appeals to quantitative researchers 

Limitations 

• Time consuming -> lots of steps for data collection

• Same participants for both phases or different participants?

• REB approval can be difficult

EXPLORATORY DESIGN

= two phase process where the first method (qualitative) helps to develop/inform the second method (quantitative)

• Used when measures or instruments are available, variables are unknown, or no guiding framework or theory

EXPLORATORY DESIGN STRENGTHS AND LIMITATIONS

Strengths

• Straightforward to implement and report results

• Appeals to qualitative researchers

Limitations 

• Time consuming

• Some participants for both phases or different participants?

• REB approval can be difficult