# data[].attributes.headline
Alejandra, Addy, Floor, Nghilinawa, Maozya, Dennis

# data[].attributes.subject
Alejandra, Addy, Floor, Nghilinawa, Maozya, Dennis

# data[].attributes.body
1) Very organized

2) -It needs to be finished

-It needs a conclusions/outputs column

3)

# data[].attributes.body
1. What works well?

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

# data[].attributes.body
what works well:

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

# data[].attributes.body
1) Pros: clear methods for verifying data and indicators, more detailed information about all the research design

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

# data[].attributes.body
1) Pros: intuitive layout

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

# data[].attributes.body
1) Pros: the overall objective is properly narrowed down in subquestions and themes; space for interactions between categories

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

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

# data[].attributes.headline
Alejandra, Addy, Floor, Nghilinawa, Maozya, Dennis

# data[].attributes.subject
Alejandra, Addy, Floor, Nghilinawa, Maozya, Dennis

# data[].attributes.body
1) The logic is clear

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.

# data[].attributes.body
1) nice progression in the immediate objectives (1-3 build on each other)

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

# data[].attributes.body
1) Use of both quant and qual. Simple and straightforward

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

# data[].attributes.body
Good:

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

# data[].attributes.body
1. PRO:

- everything is well describe

- inputs are detailed

CONS:

- too long

- repetitive

- too complex

# data[].attributes.body
1. PRO:

- well organized and clear to understand

CONS:

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

# data[].attributes.body
1. What works well?

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

# data[].attributes.body
1. PRO:

- best out of the three examples

- clear to follow

CONS:

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

# data[].attributes.body
1) Clear how the subquestions and sub sub questions correlate, as such they are easily understood and targeted. the organization gives for easy readability, and reads both backwards and forwards. Good use of numbering in the methods section.

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

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1. What works well?

1. The matrix is pretty extensive and detailed in many ways. They list specific activities that are required.

2. What does not work well?

1. The matrix lacks a hypothesis and no expected conclusions that will be derived from the data collected. Overall there is more qualitative data than quantitative. The attention to detail confuses the casual glance.

3. How do the suggested methods complement each other?

1. The matrix contains too many methods. The suggested methods do however highlight the assumptions which work well with the data methods and collections.

# data[].attributes.body
Good:

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

# data[].attributes.body
Has overall objective and immediate objectives (using research questions might have been better)

Inputs section is good

Sources and means of verification is interesting to include

Unclear number system out of context (on activities)

# data[].attributes.body
1) Matrix is easy to read and well-organised. Research question could have been put as a title to improve the readibility of the matrix

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.

# data[].attributes.body
1. Good Parts

Well structured, combines both quantitative and qualitative methods

2. Could be Improved

Overall objectives could be more clear, expected conclusion could be included, some questions are very vague

# data[].attributes.body
What works well:

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

# data[].attributes.body
What works well:

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?

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Straightforward layout

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

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1)

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[].attributes.body
1. Good Parts

Easy to read, inclusion of both qualitative and quantitate methods

2. Could be improved Parts

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

# data[].attributes.body
What works well:

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

# data[].attributes.body
Good breakdown of research question and objectives from the beginning, clear layout.

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”

# data[].attributes.headline
Alejandra, Addy, Nghilinawa, Floor, Maozya, Dennys.

# data[].attributes.subject
Alejandra, Addy, Nghilinawa, Floor, Maozya, Dennys.

# data[].attributes.body
1)-Very elaborate, everything is there

-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[].attributes.body
What works well

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

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1) the numbering system for the subquestions is also clear. Interesting/good that they write who they want to interview

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

# data[].attributes.body
What doesn't work so well:

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

# data[].attributes.body
1) Works well that the overall objective is clearly stated. Most things are clear.
2) 'Outputs' is placed in the beginning. It is very comprehensive but a bit difficult to grasp. No anticipated conclusion. Heavy in text.
3) The interviews, observations and transect walks compliment the EIAs as all the methods will eventually help the researchers gain a deep understanding of the issue. Maybe it is a bit heavy on the qual-side, but that might be on purpose. A lot of different stakeholders are involved.

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1)

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 :)

# data[].attributes.body
1. They specify activities and inputs for each objective.

2. The "verifiable indicators" session does not present actual indicators, as diagrams or descriptions can not be considered an indicator in itself. It should be more focused on percentages or sets of data.

3. They use less quantitative data, so the said "triangulation" would mainly be between different interviews, then they would just get a bigger perspective from different actors.

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What works well?

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

# data[].attributes.body
1. Good Parts:

Inclusion of verifications, critical assumptions, and triangulations

2. Could be Improved Parts:

Lack of hypothesis, difficult to navigate (too much text)

# data[].attributes.body
1. What works well? The logic in the matrix is apparent. There is a clear distinction between what data is required and what data collection method will be used, which helps visualize how data collection will be conducted.

2. What does not work well?

1. Though there is detail in the data collection method, the matrix fails to highlight data analysis methods that will be utilized to assess the collected data.

2. The data required only uses qualitative data to assess the overall research question; lacking quantitative data like household income to assess the 'economic outcome' research theme. Perhaps the triangulation of data might make the research and data more concrete.

3. How do the suggested methods complement each other?

1. The suggested data methods complement each other well enough. Interviews and questionnaires are paired along with soil fertility measurements as well as nitrate measurements to assess the biophysical outcome of the research questions. The data methods listed lack quantitative data to further solidify the correlation between the variables and outcomes listed.

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1)

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

# data[].attributes.body
What works well:

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

# data[].attributes.body
What works well:

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))

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Example from Choguill (2005)

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In your group, post the following under each matrix column:

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?

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When you've answered the questions for each matrix, please make a schematic research matrix template with the rows/columns you think should be there when conducting interdisciplinary research. Post an image (or a screenshot) of your template in the "Your Templates" section