1) What works well about this matrix?

Is the logic clear? Can the research questions be answered by the methods suggested?

2) What doesn't work so well?

What elements might be missing? What is not clear?

3) How do the suggested methods complement each other?

• The matrix draws your eye directly to the objectives

• the titles of each column are clear - you can tell exactly where you will find each piece of information

What could be improved:

• It's a bit confusing that the overall objectives and the research questions are in rows while the rest of the matrix is in a column. It makes it hard to follow how the research questions relate to the themes, subquestions, etc. We would suggest to either separate the research questions into their own matrix, or to add them as columns in between the research themes and the subquestions.

• The research themes could be where the objectives currently are

• The data analysis methods column is fairly empty, so it's unnecessary to have it in two places

How do the methods complement each other?

• It's a nice mix of research methods (interviews (qualitative), measurements (quantitative), surveys (qualitative))

1. Lists specific "inputs," or resources/people which they intend to use

2. Lists critical assumptions

What doesn't work as well:

1. "Verifiable indicators" seems like its getting ahead of itself

2. Plans for triangulation are unclear

• Nice that research themes are split up with sub questions - this makes it organised.

• both qualitative and quantitative methods - scientific and social science elements

• yes - research questions could be answered with methods

• broad scope - good for triangulation!

2)

• Broad scope could also be a limitation based on resources available

• No predicted conclusions

• missing a lot of data analysis methods especially for qualitative data

3)

• Mix of qualitative and quantitative methods gives for holistic analysis of research questions

helpful for the people involved because of details

What doesn't work well?

Too many columns, not too clear for viewer; output should be in the end

• good that inputs are stated

• structured clearly

• sub research questions allow for in depth analysis of variables

  
  

  2)

• no expected outcomes/conclusions

• lacking analysis methods

  
  

  3)

• technical methods combined with community involvement

• both qual and quan methods :)

1. Little elaboration of methods

2. Some yes/no questions that could be rephrased to better fit

3. Lack of hypothesis and anticipation of findings

2) it's not clear which data collection method is linked to which data is required, nor is it clear which subquestion is linked to data method. It also lacks data analysis methods throughout the matrix

3) the participant observation and semi-structured interviews complement each other well in the sense that they might catch different dimensions. They have a good use of both qualitative and quantitative methods

• Input column is a really nice way to represent the tools they will use to support data collection

• It's nice that everything is in a columns structure rather than a mix of columns and rows

What could be improved

• There are both sub research questions and sub sub research questions - they could have instead made the sub research questions their research themes

• The main research question could have been a title since it represents the entire matrix

• Data required is vague - terms are not defined and the methods don't explain how they will carry out the data collection

• The numbering system in the methods section isn't defined/isn't in a particular order - could be described in a report that this was extracted from, but in this context it's confusing

How do the methods interact with each other?

• Both have quantitative and qualitative methods, but more heavily focused on quantitative (GPS, slope assessment, etc.)

• The qualitative methods are a bit broad compared to quantitative

-Yes, the research question can be answered

2) -The "output" meaning is not clear, it could be "Specifics objectives"

-It is very repetitive

-Not necessary to have verifiable indicators and sources and means of verification

-It's 5 pages long, it could be shorter

3)Many overlap, it doesn't seem to be a logical connection.

Data required could be more specific - “knowledge about the government” is vague

More basic structure than the others - less specific

No inputs section

Data collection methods are sometimes vague - “interview, questionnaire”

• break down RQ and sub-RQs well

• very detailed (a bit too much)

• varied research methods linked to the RQs and sub-RQs

What doesn't work well:

• too much information

• unsure what is meant by a 'full time student'; number of hours required would be better

• the flow doesn't quite work

  • should have input and indicators before outputs

• some of the 'activities' are too broad (i.e. EIA as a method is very broad)

How do the methods complement each other:

• nice mix of qualitative and quantitative

  • gives an idea of the human emotions/lived experiences as well as the scientific environmental impacts

2. Could be improved Parts

   Exclusion of the government policies, necessity of the sub-sub column?, overall objectives missing, some vague questions

• good that outputs are stated

• involving people from both sides of debate - ie. tourist, local authorities etc.

• nice that critical assumptions are included - good to highlight this in case they don't hold up (could include a plan B?)

• Verification to create more efficient analysis

2)

• less visually pleasing - lots of text makes it less digestable

• Overall objective too broad

• assuming tourism is a good thing - biased

• methods poorly shown - ie. triangulation??

3)

• Using lots of methods but quiet unclear

• GIS and mapping - good for quantitative analysis

Data required is specific - uses, terms etc.

Methods are clear though they have the same 

Inputs talk specifically about tools that they need but not about the labour required

No analysis sector

Overall objective missing

1. The layout with main objective and question at the top

2. "Research themes" as a broad category with questions within it

What doesn't work as well:

1. The "data analysis" section being mostly blank --> what is data (even qualitative) without analysis?

• It's nice that they have an important and critical assumptions section - different from the others we've seen

• Very detailed inputs

• Specific/precise with what they are looking for

What could be improved

• This could be a lot more concise - 5 pages is quite long for a table

• The headings don't follow to the next page and the last page has many empty cells

• Overall objective could be the title, especially because there is a separate column for immediate objective

How do the methods complement each other

• They're using a variety of quantitative and qualitative methods, but it's also too many methods for one study

• They are specific about what collection methods they will use, which shows how the data could interact

2) Less is more, but it lacks explanation and directives to fully understand what direction the research will be taking, how they will aproach the interviews...

3) A little lack of diversity in methods, as qualitative data backed up by quantitative data would allow for a better understanding of what factors influence farmer's decision-making.

Inputs section is good

Sources and means of verification is interesting to include

Unclear number system out of context (on activities)

• easy to understand (e.g. methods)

Bad:

• "data required" should be more specific & detailed (e.g. with variables)

• no need for inputs

• no sub sub sub sub sub... research questions = then RQ is too broad

• add conclusions

2) The data required section is a bit vague (e.g. "degree of relief" - from what?). The sub research question section is a bit lacking in depth/detail, break up the research question in a way that does not necessarily show depth and understanding of the issue at hand. Lacks focus. GIS might be more applicable than GPS. Also, the Input section is a bit unnecessary?

3) The methods do not necessarily complement each other, need for bigger variety, methods to back up the data required and their sub-questions

- best out of the three examples

- clear to follow

CONS:

- analysis methods are not clear, why some are missing?

assumptions column is nice :) It leads to good work organization and prevents omissions of important tasks

2. What doesn't work so well?

1. Messy layout/ visual issues

2. Inputs are too detailed - it's good for a working scope but not for a matrix. It makes it less clear with that much text

3. Column of "Verifiable indicators" contains not specyfic and measurable pieces of evidence

4. Overall titles of columns are messy and not in logical order, sometimes they don't match their meaning with text below

3. How do the suggested methods complement each other?

There is overlap with different methods for multipurpose tasks - it doesn't seem very thoughtful

- well organized and clear to understand

CONS:

- lack of basic info (where, which communities...)

- everything is well describe

- inputs are detailed

CONS:

- too long

- repetitive

- too complex

• separation btw. data collection & data analysis

• clear, specific RQ

• good introduction with the overall objective & overall RQ

Bad:

• add data analysis method for all rows

• add hypothesis or conclusions

2) The main goal and the objectives are not stated very clearly. The RQ does not include anything about the scope or limitations. Some word choices are too vague: 'social conditions', 'bearing' etc. No hypotheses, variables or conclusions stated. The tools are mention but the use is not really clear. 'Farmers' are all in one group, maybe they should divide them into types of sizes of their farm

3) It is difficult to getthe full picture of how the methods compliment each other based on this matrix

2) hard to follow, very hard to read backwards, not too much information, Input section is excessive, not a clear method section

3) they do not

2) It lacks a definition of "social group"

-The data required is vague

-They can use Household surveys for analyzing the economical part

-The 4th quesiton can be a sub sub question of the 2nd, because both analyze perceptions of farmers in different areas

3) The field observations complement the semi-structured interviews. They have very clear tools and methods.

2) Cons: The organization of the framework is not intuitive;

3) Methods: too much focus on qualitative and less on quantitative methods

2) Cons: unclear terminology (degree of relief? Bearing?), broad terminology (ex. 2b); inputs are confusing,and they repeat

3) Methods: unclear, just interviews, tools not defined in their purpose, other method not explained or clear, no accessible for the reader

2) Cons: repetition in inputs, too long, not necessary info, people are not inputs, considerations are more relative to management less on the research itself

3) Methods: well complemented through triangulation BUT needs to be defined

• well organized

• clear structure

• chronology

• sub-questions relate well to the overall question

what works less well

• no hypothesis

• no anticipated conclusions

• some of the data lacks a plan for analysis

Clear overall descriptions and everything is logical and nicely organised in the table

2. What doesn't work so well?

There's no critical assumptions. "Inputs" colums also could be skipped, or at least more consistent with methods

3. How do the suggested methods complement each other?

It's a bit complicated to get a picture of what the method will be used for

2) -It needs to be finished

-It needs a conclusions/outputs column

3)