Develop a measurement plan

What is in a measurement plan?

The measurement plan provides an overview of:

• the hypotheses, with or without prioritisation of which hypotheses are most important to investigate;
• the outcome measures to be assessed;
• the data source, indicating where or from whom measurements will be taken or information will be collected;
• the procedure for carrying out the measurement plan;
• and the risks, external factors, or preconditions that may affect the feasibility of the measurement plan.

By taking all of these factors into account, you will arrive at a practical and well-thought out measurement plan that can be immediately implemented.

Outcome measures and data sources

Often, a hypothesis cannot be tested directly. For example, if the hypothesis is that an intervention reduces feelings of loneliness among older people, the concept of 'loneliness' needs to be translated into one or more outcome measures. Loneliness can then be measured quantitatively using standardised questionnaires, or through qualitative methods such as observations or interviews.

Different sources and methods

In some cases, information can be obtained from existing sources, such as the electronic client record (ECR). However, it is often necessary to collect new information in a targeted way. This can be done in different ways, for example:

• conversations with users of digital care applications;
• analysis of (log) data from technology;
• questionnaire-based research;
• on-site observation (shadowing).

Additional considerations for quantitative research

Quantitative research ensures that the data collected have a uniform structure. This allows datasets to be linked. Data collection requires:

• a data management plan;
• data processing agreements with end users;
• compliance with the requirements of the General Data Protection Regulation (GDPR);
• approval by a (medical) research ethics committee ((M)REC).

Procedures and risks

Evaluate the activities required to carry out the measurement plan. Try to identify risks in the approach and consider alternatives where possible. This may involve, among others, choosing a different research method for certain effects. You may also decide to accept that some effects cannot be measured or can only be measured to a limited extent.

Consider whether it is worthwhile to collect additional knowledge, given the time and resources required. Reflect on this when describing your hypotheses. At this stage, you will have a clearer view because you have worked out the method for collecting this knowledge. If there is uncertainty about efficiency or feasibility, it is advisable to align this in advance with the key stakeholders.

A template for an observation tool (in Dutch) can be found in the download section of the Value Palette Framework.