TITLE: Guidelines to conduct RANAS based socio-hydrological (SH) surveys to understand behaviour
AUTHORS: Soham Adla, Diana Carolina Callejas Moncaleano, Mohammed Faiz Alam, D Daniel, Saket Pande

[DESCRIPTION]
This is a protocol to quantify the determinants of behavioral change (such as technology adoption) in a generic socio-hydrological context, using the RANAS approach to systematic behavior change. This protocol combines survey design, sampling, and data collection with model conceptualization and development.


[BEFORE_START]
Obtain the necessary ethics permissions from your institutions regarding the collection, processing and storage of Personally Identifiable Information (PII) and/or Personally Identifiable Research Data (PIRD). 
PII includes directly identifiable information (e.g., name or email address). 
PIRD includes directly or indirectly identifiable data (e.g., videos, pictures, IP address, gender, age, etc.). 
Undertaking any exercise with human participants (like a survey) entails risks that arise to the participants. Such permissions ensure that these risks (both actual and potential) are accounted for and mitigation measures are in place for each of these risks. An example of these principles and some guidelines are given here (as a reference): https://www.tudelft.nl/over-tu-delft/strategie/integriteitsbeleid/human-research-ethics/hrec-approval-1-application 

[STEPS]
SECTION: Analysing the collected data: conduct statistical analysis
26. Conduct additional inferential statistical analysis to establish relationships between your IVs and your target behaviour (DV).
SECTION: Identify the research question
2. Eligibility check 1: 
Is the research question based on underlying human-water interactions? This protocol deals with SH questions.
Is collecting data from a survey suitable to answer your SH question, as opposed to other approaches (e.g., interviews, focused group discussions, secondary data, web scraping)? You may inform yourself using available resources (Javaid, 2023; Bhat, n.d.).
Can you collect observed and/or self-reported data from the participants? (Abrahamse et al., 2016)
If answers to the above questions are yes, then proceed.
SECTION: Draw a conceptual model which connects the behavioural and/or psychological variables, water system to result in a particular human behaviour
6. Decide the scales and corresponding levels of modelling in terms of spatial, temporal and human organizational scales (Yu et al., 2022).

For an example, if you want to do exploratory research (e.g., Which are the socio-psychological factors influencing behaviour to improve water savings in agriculture? from point 1), the:
Spatial scale can be a plot (patch) - to determine the behaviour that is adopted to save water at the scale of a single plot/farm (e.g., target behaviour of farm land levelling)
Temporal scale can be a cropping season - in case the target behaviour is based on demand side interventions such as adoption of micro-irrigation technology, such as sprinkler irrigation.
Human organizational scale can be that of a community (neighbourhood) - in case the target behaviour is about small scale managed aquifer recharge which requires cooperation and collective action within a community.  
SECTION: Collect data using the survey questionnaire
19. Conduct Trial 2 with key informants (such as an staff from an NGO working with the intended informants) using the current survey, get feedback and revise the survey questionnaire. Trial 2 is more geared towards situating the survey in the local context (e.g., using relevant local terminology, identifying and modifying questions which might be mis-interpreted, identifying and removing sensitive questions)
SECTION: Collect data using the survey questionnaire
21. Conduct the actual survey with the identified survey respondents.
SECTION: Determine the sampling strategy
16. Determine the representative sample size of the target population. Considerations could be statistical power (Duflo et al., 2006; Krejcie and Morgan, 1970), logistical constraints, etc. There are some online resources to compute sample size (e.g., Raosoft, 2004).
SECTION: Determine the sampling strategy
17. Identify the sampling design which is statistically acceptable as well as logistically realistic to capture a representative sample.
SECTION: Design a survey questionnaire
13. Conduct Trial 1 of the survey questionnaire with non-respondents. This trial is for the purpose given in step 13.1.
SECTION: References
27. Abrahamse, W., Schultz, P. W., & Steg, L. (2016). Research designs for environmental issues. In Research Methods for Environmental Psychology. (pp. 53–70). John Wiley & Sons, Ltd.
https://doi.org/10.1002/9781119162124.ch4
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Bhat, A. (n.d.). Data Collection Methods: Sources & Examples. QuestionPro Products. Retrieved August 22, 2023, from https://www.questionpro.com/blog/data-collection-methods/
Chatterton, T. (2016). An introduction to theories of behaviour. In F. Spotswood (Ed.), Beyond behaviour change: “Key issues, interdisciplinary approaches and future directions” (p. 0). Policy Press. https://doi.org/10.1332/policypress/9781447317555.003.0002
Clark, T. (2008). `We’re Over-Researched Here!’: Exploring Accounts of Research Fatigue within Qualitative Research Engagements. Sociology, 42(5), 953–970. https://doi.org/10.1177/0038038508094573
Contzen, N., Kollmann, J., & Mosler, H.-J. (2023). The importance of user acceptance, support, and behaviour change for the implementation of decentralised water technologies. Nature Water, 1–13. https://doi.org/10.1038/s44221-022-00015-y
DiLeonardo, A., Lauricella, T., & Schaninger, B. (2021). Survey fatigue? Blame the leader, not the question. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-organization-blog/survey-fatigue-blame-the-leader-not-the-question
Duﬂo, E., Glennerster, R., & Kremer, M. (2006). Using Randomization in Development Economics Research: A Toolkit. The Abdul Latif Jameel Poverty Action Lab.
https://www.povertyactionlab.org/sites/default/files/research-paper/Using-Randomization-in-Development-Economics.pdf
Fink, A. (2003). How to Manage, Analyze, and Interpret Survey Data. SAGE. 
Jamieson, S. (2004). Likert scales: How to (ab)use them. Medical Education, 38(12), 1217–1218.
https://doi.org/10.1111/j.1365-2929.2004.02012.x
Javaid, S. (2023). Research Data Collection in 2023: Quick Guide & Top 5 Methods. AIMultiple. https://research.aimultiple.com/research-data-collection/
Krejcie, R. V., & Morgan, D. W. (1970). Determining Sample Size for Research Activities.  ducational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308
Lakshminarasimhappa, M. C. (2022). Web-Based and Smart Mobile App for Data Collection: Kobo Toolbox / Kobo Collect. Journal of Indian Library Association, 57(2), Article 2.
Mosler, H.-J. (2012). A systematic approach to behavior change interventions for the water and sanitation sector in developing countries: A conceptual model, a review, and a guideline. International Journal of Environmental Health Research, 22(5), 431–449.
https://doi.org/10.1080/09603123.2011.650156
Raosoft Inc. (2004). Sample Size Calculator by Raosoft, Inc. [Computer software]. Raosoft, Inc.
http://www.raosoft.com/samplesize.html
Steg, L., van den Berg, A. E., & de Groot, J. I. M. (2018). Environmental Psychology. In Environmental Psychology (pp. 1–11). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119241072.ch1
Stern, P. C. (2000). New Environmental Theories: Toward a Coherent Theory of Environmentally Significant Behavior. Journal of Social Issues, 56(3), 407–424. https://doi.org/10.1111/0022-4537.00175
TU Delft. (2016). HREC Approval 1: Application. https://www.tudelft.nl/over-tu-delft/strategie/integriteitsbeleid/human-research-ethics/hrec-approval-1-application
United States Census Bureau. (2000). Census and Survey Processing System (CSPro) [Computer software]. United States Census Bureau. https://www.census.gov/data/software/cspro.html
SECTION: Identify the research question
1. Identify an appropriate socio-hydrological (SH) research question
Try to answer the following questions, to identify the appropriate research question. Each question is given with an appropriate SH example with underlying human-water interactions. 
Do you want to study behaviour within the framework of exploratory research? E.g.: Which are the socio-psychological factors influencing behaviour to improve water savings in agriculture?
Do you want to make a predictive model? E.g.: The objective is to develop a data-based SH model to predict the adoption of water-saving behaviour in agriculture by a farmer or farming communities.
Do you want to intervene using some understanding of behaviour? E.g.: How can you design an intervention based on socio-psychological factors to promote water-saving behaviour in agriculture?
Do you want to assess the impact of an intervention? E.g.: What is the impact of a behavioural intervention aimed at promoting water-saving behaviour in agriculture?
SECTION: Draw a conceptual model which connects the behavioural and/or psychological variables, water system to result in a particular human behaviour
3. Decide on conceptual modelling approach => 
Identify the conceptual pathways of human-water interaction, clearly highlighting target behaviour.
SECTION: Draw a conceptual model which connects the behavioural and/or psychological variables, water system to result in a particular human behaviour
4. Review the literature (see recommended reading below) and identify: 
the appropriate environmental Behavioural** (or intention) theories and 
a corresponding model (which are derived from these theories). 
that is appropriate to answer your research question.

Example of a theory: Theory of Planned Behaviour (Ajzen, 1991) 
Example of model within the theory: RANAS (Mosler, 2012)  
For a comprehensive discussion, please refer to the recommended reading, particularly Stern (2000).
SECTION: Recommended Reading
28. Steg, L., van den Berg, A. E., & de Groot, J. I. M.
(2018). Environmental Psychology. In Environmental Psychology (pp.
1–11). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119241072.ch1
Stern, P. C. (2000). New Environmental Theories: Toward a
Coherent Theory of Environmentally Significant Behavior. Journal of Social
Issues, 56(3), 407–424. https://doi.org/10.1111/0022-4537.00175
SECTION: Design a survey questionnaire
12. If necessary, translate the questionnaire to capture the language and local contexts of the target population.  
SECTION: Design a survey questionnaire
13.2. Conduct a pilot study of the survey with a small representative group, to identify whether the questions are being interpreted reasonably, and to modify/remove questions which may be sensitive (e.g., age, gender, income, personal preferences) 
SECTION: Design a survey questionnaire
12.1. Ensure that the local nomenclature (about your topic) is incorporated into the survey. 
SECTION: Design a survey questionnaire
9. Don't build the survey from scratch! Use templates of pre-existing survey questionnaires to start designing your survey.

They are usually available in the Appendix section of the concerned publication case, or within the text of the authoritative literature about the respective model, or open-source websites which document typical surveys within their scope (e.g.: SIASAR for rural water supply studies in the global south). This helps also in verifiability and standardization for comparative studies.  
SECTION: Collect data using the survey questionnaire
18. Before conducting the survey, do the following.
SECTION: Design a survey questionnaire
14. Pre-survey introduction:
1. Design a transcript to introduce the survey.
2. Design the text for obtaining informed consent.



SECTION: Draw a conceptual model which connects the behavioural and/or psychological variables, water system to result in a particular human behaviour
5. Eligibility check 2: This protocol only describes the RANAS model. Please proceed only if you have chosen the RANAS model.

RANAS is an acronym for Risk- Attitude- Norms - Ability- Self regulation, which is a systematic approach to behavior change interventions (Mosler, 2012). More recently, Trust and Psychological ownership (e.g., of the technology adoption which exemplifies the behavioural change) have also been discussed as important factors to drive behavioural change (Contzen et al., 2023). You are encouraged to have a look at the literature to better inform yourself.
SECTION: Design a survey questionnaire
10. Make a list of survey questions with their appropriate responses.

E.g., Question on personal norms: What is your perception about the importance of saving water in your agricultural farm? 
Answers (Likert scale): Highly unimportant, Unimportant, Neither unimportant nor important, Important, Highly important.  
SECTION: Draw a conceptual model which connects the behavioural and/or psychological variables, water system to result in a particular human behaviour
8. If you did not use the RANAS framework initially, categorize each of the driving behavioural factors (such as risk, future profit, capacity, etc.) into the RANAS factor and sub-factors (Mosler, 2012). 
SECTION: Draw a conceptual model which connects the behavioural and/or psychological variables, water system to result in a particular human behaviour
7. (Recommended) Using the RANAS framework (detailed in Mosler, 2012 and Contzen et al., 2023), identify factors (from risk, attitude, norms, abilities, self-regulation, trust and psychological ownership) and sub-factors (e.g., descriptive, injunctive and personal norms are sub-factors of the norm factor) which are relevant to your research question. In the conceptual model, there are the driving behavioural factors (factors which can influence behaviour), and the behaviour itself. Decide and list the relevant behavioural factors and behaviour within the RANAS template.
SECTION: Design a survey questionnaire
10.1. Make a list on a text editor which contains explicit questions and corresponding answers to address your research question.

Questions can be about perceptions related to the RANAS factors and sub-factors (step 7).
Questions can relate to the target behaviour being investigated (step 3).
Questions can also be about additional variables that are relevant to answer the research question (such as socio-demographic variables, Personally Identifiable Research Data (PIRD). Ask Personally Identifiable Information (PII) only if extremely necessary because this requires stringent ethical clearances. The definitions of these data are explained further in the "Guidelines and Warnings".

Answers can include different types of data (with examples): 
Text: Question - "What is your name?"; Answer - "Jane Doe".
Numeric: Question - "What is the size of your farm (in hectares)"?; Answer - "4 hectares". 
Single choice: Question - "What is your perception about the importance of saving water in your agricultural farm? "; Answer - "Not important".
Multiple-choice: Question - "How do you irrigate your farm?", Answer: "Sprinkler irrigation, Flood Irrigation".
SECTION: Design a survey questionnaire
10.2. Label the questions into different themes, such as demographic, socio-economic, RANAS based perceptions 
SECTION: Design a survey questionnaire
10.3. Code the answers according to the type of variable (e.g., categorical, see Fink, 2003) and indicate the levels for each categorical answer. RANAS based perceptions can be coded into the potential responses using the Likert scale (Jamieson, 2004). One example of a 5-point likert scale which indicates the perceived change in some variable, is 1 = Decreased significantly, 2= Decreased slightly, 3 = No change, 4 = Increased slightly, 5 = Increased significantly.
SECTION: Design a survey questionnaire
11. Decide how you will conduct the survey - with printed paper or digitally.
SECTION: Design a survey questionnaire
11.1. (Recommended) If you choose digital survey, identify and use a tool to design it. Digital surveys can be implemented using tablets, mobile phones, etc.

There are different software, which may either be
Free: Kobo Toolbox, Census and Survey Processing System (CSPro), Google Forms
Licensed: Qualtrics, ArcGIS Survey123, SurveyCTO, Open Data Kit (ODK), Survey Monkey
SECTION: Design a survey questionnaire
11.2. If a digital survey is not feasible or preferable, then use paper based surveys. Don't forget to weather-proof the sheets of paper, and write legibly, and carry stationery (board, extra pens, etc.)
SECTION: Design a survey questionnaire
10.4. (Recommended) For a quantitative analysis, it's better to have more close-ended questions (rather than more open-ended). 

According to SurveyMonkey, close-ended questions are those where the respondent chooses from a list of pre-selected options which can help to generate statistical models, as compared open-ended questions which are more exploratory and can  provide richer qualitative insight but may be difficult to incorporate into models. 

E.g., to ask about personal norms, it is better to ask a close-ended question with Likert scale responses such as in point 10 ("What is your perception about the importance of saving water in your agricultural farm? "), rather than asking the open-ended question of "What do you feel about saving water in agriculture?"
SECTION: Design a survey questionnaire
13.3. Verify the data collection during the pilot (online in case of a digital survey) to check for
1. completeness (i.e., if the information is complete, and all answers have been included
2. accuracy (i.e., all data is being correctly collected)
3. consistency (i.e., data collected is consistent across respondents) 
SECTION: Collect data using the survey questionnaire
18.1. Give a brief introduction statement to describe the survey.
SECTION: Collect data using the survey questionnaire
18.2. Obtain a physically signed or vocal recording of voluntary informed consent.
SECTION: Collect data using the survey questionnaire
20. After testing, modify and finalize one version of the survey and do not change it anymore during the survey. This will help in maintaining internal consistency in the collected data.
SECTION: Analysing the collected data: conduct statistical analysis
22. Data preparation: make or import (from the software) a table for questions with responses, including demographic, socio economic, and RANAS factors, and output behaviour.
SECTION: Analysing the collected data: conduct statistical analysis
23. Data cleaning: clean the data based on guidelines from Fink (2003). Aspects of data cleaning include miscoding, missing responses, data entered incorrectly, etc. (Fink, 2003). This step is time consuming and requires patience and caution. 
SECTION: Analysing the collected data: conduct statistical analysis
25. Conduct Descriptive statistical analysis based on your reading of the literature.
SECTION: Analysing the collected data: conduct statistical analysis
25.1. Explore the data, e.g.: trends, outliers you can start with the descriptive statistics.
Further statistical analysis depends of the research question you need to answer.

Visualization can be done using bar plots, histograms, or tables, etc. 
SECTION: Analysing the collected data: conduct statistical analysis
24. Based on your research question, decide the type and number of dependent variables (DV) and independent variables (IV) for your statistical analysis.

This information is useful to create a codebook for your report (Fink, 2003).
SECTION: Analysing the collected data: conduct statistical analysis
26.1. Choose the method that suits the number and type of the IVs and DVs. Guidelines from Fink (2003) can be useful (Table 4.1) or other literature. 
SECTION: Get the necessary ethical clearances
15. Obtain the necessary ethical clearances and data management strategy (e.g., storage and backup) corresponding to the questionnaire. You can inform yourself using university guidelines such as TU Delft (2016).
SECTION: Get the necessary ethical clearances
15.1. While designing the questionnaire, think carefully about the PII and PIRD which will be collected, and obtain the corresponding ethical clearances or alternatively, modify the questionnaire based on the limits of the clearances you obtain. 
SECTION: Get the necessary ethical clearances
15.2. Different storage sources include: iCloud, Google Drive, Dropbox, your organization's storage, etc.
SECTION: Design a survey questionnaire
13.1. Practise trials (around 10-20) of the survey questionnaire with colleagues and non-colleagues (and not respondents) to balance your study requirements with logistical constraints, such as clear semantic communication of the questions (whether the participant understands your query clearly), survey duration (keep as short as possible), and survey fatigue (the "lack of motivation to participate in assessments"). (DiLeonardo et al., 2021; Clark, 2008)