International Journal of Community Well-Being

, Volume 1, Issue 2, pp 101–114 | Cite as

The Happiness Analyzer – Developing a New Technique for Measuring Subjective Well-Being

  • Kai LudwigsEmail author
  • Stephan Erdtmann
Original Research Article


In 2013 the OECD published a guideline for measuring subjective well-being in greater detail to collect data in the quality needed as a basis for efficient decisions to improve subjective well-being and the evaluation of those decisions to enable continuous learning. Unfortunately, many studies do not measure subjective well-being according to this standard, as traditional methods (e.g., paper and pencil or personal interviews) require considerable resources (from both researchers and participants) to capture i) people’s subjective well-being at multiple points in time using general questionnaires; ii) people’s everyday life and everyday feelings; iii) people’s specific feelings in the moment; and iv) a combination of subjective and objective well-being measurements. To resolve this issue, we developed an app as a mobile assessment tool, the “Happiness Analyzer” which is outlined in this paper. This app can be used to measure subjective well-being and community well-being in community projects which has been shown in case studies in Frankfurt or Wuppertal, Germany.


Happiness research Subjective well-being Multi-moment assessment Experience sampling method Day reconstruction method Mobile application 


Rise of SWB Research

In recent years, happiness research has become a prominent topic in academics, politics and the media because of the call for more measures for economic progress apart from only the GDP. Many people have begun to realize that after a certain level, more consumption and higher material well-being do not increase people’s subjective well-being (SWB) (Clark et al. 2008). Thus, it is important to conduct research to understand the subjective well-being levels of different demographic groups and particularly to understand what increases the subjective well-being of certain people and what does not. To accomplish this, substantial correlative data regarding people’s SWB has been collected for many years by, for example, the Gallup World Poll and the European Social Survey. This collection of data represents a generally positive development, but as researchers and practitioners have analysed these data, they have realised that better data is required to understand the underlying mechanisms of SWB (e.g., the precise effect a certain intervention has on different life domains and subjective well-being evaluations), particularly for studies at the micro (individual) and meso (city or corporation) levels. Especially for community projects it seems to be meaningful to measure not only the community well-being of people, meaning their judgement of the community they live in all together as well as their judgement of different community aspects (e.g. health system, infrastructure, etc.), but also the individual SWB in more detail additional to community well-being as shown by Kim and Ludwigs (2017) in a project in Frankfurt, Germany.

In 2013 the OECD published a guideline for measuring SWB in greater detail. These guidelines were developed by some of the most influential scholars in the field and are well cited. Therefore, we decided to develop the tool, this paper describes‚ based on this guideline.

Call for Better Measurement

What the OECD Suggests to Measure SWB

According to the OECD, SWB is a construct consisting of three elements: i) life evaluation – a reflective, cognitive judgement of a person’s life or specific parts of it; ii) affect – a person’s positive and negative emotions and feelings; iii) eudaimonia – according to Aristotle’s 2000-year-old construct, a person’s judgement of his life in terms of meaning and purpose in life (for more details on these definitions, see OECD 2013 pp. 30–32).

What the OECD Suggests to Use to Collect Data on SWB

To measure these different elements, the OECD (2013; pp. 163–171) suggests using six different modules: i) a core module about happiness and life-satisfaction with a single question; ii) an affect module with multiple specific questions; iii) a life evaluation module with multiple specific questions; iv) an eudaimonic well-being module with multiple specific questions; v) a domain evaluation module with multiple specific questions about satisfaction in specific life domains (e.g., health); and vi) an experienced well-being module. The guidelines recommend using the experience sampling method (ESM; Csikszentmihalyi and Hunter 2003), in which people record how they feel and what they are doing, with whom and where at specific moments in time in affective time-use diaries, or the day reconstruction method (DRM; Kahneman et al. 2004), in which people reconstruct their day in episodes (e.g., breakfast from 8 to 9 am) and rate how happy they felt during those episodes. In addition to these modules, the OECD generally advocates for more longitudinal studies instead of cross-sectional studies and for linking SWB data to objective data, including location data, economic variables or biological markers (see OECD 2013; pp. 48, 51, 146 and 149).

The requirements can be ordered according to an onion model, as displayed in Fig. 1, consisting of three layers within a frame. i) General Measurement: A general SWB questionnaire including OECD modules one to five that is designed to obtain a cognitive measurement. ii) Activity-based Measurement: Daily life and daily affective experience measurement employing affective time-use diaries via techniques such as the DRM to comprehensively capture people’s time use to obtain more contextual information. iii) Experience Sampling: Affective measurement in the moment using, for example, the ESM. iv) Objective Markers: Around the subjective layers is an objective frame integrating other objective markers to increase validity, for example, location data, economic variables, and biological markers such as heart rate variability or face emotion recognition as noted above. Unlike the OECD, we separate the DRM and ESM into two different layers. We agree that both methods primarily measure experienced well-being. On the one hand, the DRM provides far more contextual information about a person’s life and activities than the ESM because the DRM collects information about the entire 24 h in a day, rather than simply a few moments. On the other hand, the ESM collects less cognitively biased information because it surveys people’s feelings right in the moment. Together both methods can help to understand subjective well-being in greater detail.
Fig. 1

The onion model for measuring subjective well-being: The figure displays the onion model which is our approach to fulfil the recommendations of the OECD guideline for measuring subjective well-being. More details are explained in the text

Unfortunately, many existing studies do not measure SWB according to this standard because considerable resources are needed (from both researchers and participants) to capture the following information: i) people’s SWB at multiple points in time using general questionnaires; ii) people’s everyday life and everyday life feelings; iii) people’s direct feelings in the moment; and iv) a combination of subjective and objective well-being measurements such as people’s subjective ratings of SWB and their objective stress level indicated by, for example, their hair cortisol levels.

Table 1 displays a set of questions for the subjective well-being layers that are in line with the OECD guidelines.
Table 1

Measures of subjective well-being according the OECD guidelines (2013)



Scale Range

Happiness Core (HC; ESS 2013)

Taking all things together, how happy would you say you are?

0: Extremely unhappy

10: Extremely happy

Life Satisfaction Core (LC; ESS 2013)

All things considered, how satisfied are you with your life as a whole nowadays?

0: Extremely dissatisfied

10: Extremely satisfied

Scale of Positive and Negative Experience (SPANE; Diener et al. 2010)

How often did the interviewed person experience the following emotions in the last two weeks:

0: never

7: always

1: Negative


2: Unpleasant

3: Good

4: Bad

5: Happy

6: Afraid

7: Pleasant

8: Contended

9: Sad

10: Angry

11: Joyful

12: Positive

Satisfaction With Life Scale (SWLS; Diener et al. 1985)

Indicate your agreement which each item:

1: Strongly disagree

1: In most ways, my life is close to my ideal

7: Strongly agree

2: The conditions of my life are excellent

3: I am satisfied with my life

4: So far, I have gotten the important things I want in life

5: If I could live my life over, I would change almost nothing.

Flourishing Scale (FS; Diener et al. 2010)

Indicate your agreement with each item:

1: Strongly disagree

1: I lead a purposeful and meaningful life

7: Strongly agree

2: My social relationships are supportive and rewarding

3: I am engaged and interested in my daily activities

4: I actively contribute to the happiness and well-being of others

5: I am competent and capable in the activities that are important to me

6: I am a good person and live a good life

7: I am optimistic about my future

8: People respect me

Domain Evaluation Questionnaire (DEQ; OECD 2013)

The following questions ask you how satisfied you feel about specific aspects in your life:

0: Not at all satisfied

10: Completely satisfied

1: Standard of Living


2: Health

3: Productivity

4: Personal relationships

5: Safety

6: Community

7: Personal Security

8: Free time

9: Environment

10: Job

Day Reconstruction Method (DRM; Kahneman et al. 2004)

What did you do in this period?

0: Unhappy

Where have you been in this period?

10: Happy

Who was with you in this period?

How did you feel during this episode?

Experience Sampling Method (ESM; Csikszentmihalyi and Hunter 2003)

How do you feel right now?

0: Unhappy

What are you doing right now?

10: Happy

Where are you right now?

Who is with you right now?

Current e-Tools

Due to the rapid proliferation of the Internet and smartphones in particular over the past years, it is possible to collect more data with less resources. In 2009, when the OECD began its work on the guidelines and the shortcomings were already apparent in the academic world, Killingsworth and Gilbert published an iPhone application that used the ESM to track people’s happiness (Killingsworth and Gilbert 2010; The app first performed a general measurement using an app survey and then contacted people a few times per day with brief notifications asking them to rate their happiness on a scale and to explain what they were doing, with whom and where. A similar app was developed by MacKerron in 2011 to construct a happiness map of Great Britain. Both apps reward participants by providing them with graphical feedback in the form of a happiness profile. In addition, both apps use GPS to track participants’ location as an objective marker (MacKerron 2012; Both apps were highly successful; for example, there have been over 60,000 downloads of mAPPiness.

A major restriction of these apps, however, is that they work only on iPhones. Given that not everyone can afford an iPhone, it is difficult to build representative samples using these apps. Additionally, the apps do not capture layer two (the activity-based measurement) and only partially capture the frame (objective markers); thus, they do not solve problem two (comprehensive data about people’s everyday life and everyday life feelings) and only partially address problem four (combination of subjective and objective well-being measurements) through the integration of a GPS module.

In 2012, Veenhoven, Bakker and Oerlemans developed a different approach, the “Gelukswijzer” or “Happiness Indicator” (; Bakker et al. 2016). The Happiness Indicator is a webpage that offers people the opportunity to report their happiness score as often as they wish, and participants are reminded via email to track their happiness. In return, participants receive a happiness profile that illustrates their happiness trend. Additionally, the authors integrated an applied form of the DRM. Because the Happiness Indicator is a webpage and not an iPhone app, more people – especially older people – can use it, and a more representative sample can therefore be collected. Over 80,000 people have now signed up to track their happiness. Unfortunately, analyses of the data collected via this instrument indicated that very few participants reported their happiness score at least once per day and that only a few participants provided their DRM responses daily over a longer period of time, likely because it is not feasible for users to reply to e-mail notifications on a regular basis (Bakker et al. 2016). In addition, the web-based tool does not offer the option of using the ESM. Overall, the Happiness Indicator solves problem one and generally solves problem two, but it is not capable of solving problems three or four. Table 2 summarizes the features of the various tools.
Table 2

Comparison of the features of recent SWB tracking tools and the happiness analyzer


Track your happiness


Happiness indicator

Happiness analyzer

Survey SWB using general questionnaires at multiple points in time

Survey people’s everyday life and everyday life feelings using affective time-use diaries such as the DRM


Survey people’s direct feelings in the moment using, for example, the ESM

Combining other objective and subjective indicators to obtain a more valid measurement




Multiplatform (Web, Android, iOS) for representative sampling

✓ = has the feature; (✓) = feature partially implemented; • = does not have the feature

To overcome the aforementioned problems, we developed the “Happiness Analyzer” based on the OECD guidelines and the onion model to solve problems one, two and three using a multiplatform tool. For problem four, thus far we have conducted evaluations and applied a GPS module, and we are currently experimenting with additional sensors and markers, which will be explained in greater detail at the end of this paper. In part two, we outline the method in more detail. In part three, we explain and discuss the main evaluation results of the most recent evaluation study. In part four, we conclude and outline future prospects.

Method: The Happiness Analyzer

In this section we will outline the method by first summarizing all functionalities of the Happiness Analyzer and then conclude with some screenshots and a screencast of the Happiness Analyzer in Fig. 2.
Fig. 2

The Happiness Analyzer: The figure displays the Happiness Analyzer. In the first screenshot on the left top you see a notification to use the DRM. The second screenshot on the top middle position displays how participants can select an episode of their previous day. The third screenshot on the top right displays the screen that pops-up when an episode is selected. The participant can adapt the episode time, define what he did, where and with whom by selecting from different answering options. As displayed in the fourth screenshot on the middle left the participant can then continue to select more episodes until the whole day is reconstructed. Then the participant can rate how happy he felt during the different episodes, which is displayed in the screenshot in the middle. All results of the DRM and the ESM are displayed in bar-charts to show how happy the participant felt on which day, during which activities, at which locations and in which social setting. The last three screenshots display the ESM where a participant has to rate how happy he feels right now, what he is doing, where and with whom by selecting from different answering options. If the participant wants to add a note, a picture or an audio note he can do this afterwards, which is displayed on the last screenshot on the bottom right position. A screencast can be viewed at


General SWB Measurement

The survey tool administers the questionnaires included in the OECD modules and collects demographics. To measure SWB according to the onion model, the basic version uses a SWB module that requires between six and eight minutes of the participants’ time (see Table 1). This survey as well as the DRM (with e-mail-reminders) can also be done on a website if a participant would prefer to answer the survey and use the DRM on a bigger screen. The ESM can just be used on a smartphone.

Activity-Based Measurement

To measure people’s activities and assess their time use and how happy they are during their activities, the DRM is applied in the app. At 9 pm, participants receive a notification to reconstruct the past day. Additionally, the app offers participants the option to make notes about the day, take a picture or record an audio note after they have reconstructed their day. This feature is not mandatory and can be excluded with minimal efforts by the developers if the third-party researcher does not want to collect this kind of data.

Experience-Based Measurement

To measure people’s subjective well-being in a more affective and detailed manner, the tool can notify participants at specific or random moments. In the standard setting four notifications are sent out randomly to participants between 8 am and 8 pm with a minimum time gap of 2.5 h between two notifications asking them to record i) how happy they are, ii) what they are doing, iii) with whom, and iv) where. The frequency of notifications and the time-frame can be adapted with minimal efforts by the developers depending on the research questions and the wishes of the third-party researcher. Additionally, specific questions or a higher frequency of questions can be asked at a specific point in time. After responding to these questions, participants have the option to make notes about the described moments, take a picture or record an audio note. This feature is not mandatory and can be excluded with minimal efforts by the developers if the third-party researcher does not want to collect this kind of data.

Biological Markers

The survey tool can notify people when they should carry out, e.g., a hair probe or collect other biological markers. With hair probes it is possible to measure for example the cortisol level of a participant, which appears to be a good indicator of well-being (Steptoe et al. 2005).

Graphical Feedback

All the collected data in the ESM and DRM are displayed in real time to the participants in a visual format. This should help raise the participants’ awareness about what makes them happy and what does not. Additionally, this feedback should motivate people to participate longer in a study to build their individual happiness profile. Participants get graphical feedback in bar charts, separated for ESM and DRM, on how happy they were on which day, how happy they are during which activities, how happy they are at which locations and how happy they are in which social setting. Some examples are shown in Fig. 2. If researchers are concerned that this feedback feature could be a reason for biases (e.g. that people act different or become happier because of their increased awareness) the feature can be excluded with minimal efforts by the developers if the third-party researcher does not want to include this feature.

Design and Flexibility

To ensure high response rates and large samples, the design and name of the app can be adapted for every individual study.


To ensure that studies conducted using the Happiness Analyzer are as accessible as possible, the questionnaires and the DRM (with e-mail-reminders) can be answered in the browser on desktop and mobile devices. The ESM can just be used when using a mobile device using the Happiness Analyzer as a native app on iOS or Android.

Cross-Device Sync

The Happiness Analyzer can be used on multiple devices in parallel. When responding to a questionnaire on one device, the data are directly synced to other connected devices (Internet connection required).

Offline Support

Once installed, the native smartphone app does not require a continuous Internet connection. The participant can be notified about all the questionnaires and respond to them without a data connection. An Internet connection is required only at the end of the study to sync the entered data with the server.

Smartphone Sensors

The Happiness Analyzer has access to sensors built into smartphones, such as camera, microphone and GPS. The corresponding data can be retrieved in addition to the information manually entered by the participants while responding to a questionnaire. For example, the GPS information, as well as the camera function was used in a project called “Happy Wuppertal” ( where we collected data from over 2000 citizens in the German city Wuppertal using an adapted version of the Happiness Analyzer. Citizens were able to give feedback for improvement options in their city by sharing their location, making a picture of what they would like to improve and say what they would like to change.


The app has localization support, i.e., the language, date format, and so on adapt to the device language.

Data Export

The data can be exported in many different formats with minimal efforts of the developers and used in various analytical software packages, such as STATA, SPSS, and Excel. This also includes an export of the qualitative data entered as notes, photos or audio notes at the end of the DRM or ESM.

User Identification

To guarantee the anonymity of the study participants, i.e., to ensure no personal information collected via the Happiness Analyzer is linked to the actual person who submitted the data, participants are identified by a cryptographically secure randomly generated usercode that is created on the first use of the app. Thus, any association of the identity used in the app to the actual user of the app, which might be possible using a user-provided identity such as an email address or username, is impossible.

As a second option for user identification, a list of random usercodes can be generated before the study begins, and these generated codes can be transferred to the study participants who then use the codes to log in to the app.

Data Storage

The study data are stored on servers that are directly managed by the Happiness Research Organisation (HRO;, an independent German research institute, and located in Germany. To maintain control of the data and of their physical location and to remain independent of any third party, the HRO chose not to use cloud-based data storage. The information stored for a participant can be deleted at the request of the participant or the third-party researcher. If requested by a third-party researcher data can also be stored on other servers.


In 2013, we had the idea to develop the Happiness Analyzer. As of 2018 we have ran various evaluation studies and applied the tool in different versions for various research studies (as an example see Hendriks et al. 2016). In this section, we want to outline the focus group feedbacks of our last detailed evaluation study with psychology students at the University in Düsseldorf.

To improve the Happiness Analyzer, the app was evaluated in a research study with psychology students at the University in Düsseldorf in November & December 2016 (112 users). The participants were required to use the app for two concurrent weeks, during which they completed the general SWB questionnaire, answered some demographic questions at the beginning of the app and then responded once per day to a DRM notification and 4 times per day to an ESM notification. In addition, we tracked the participants’ location via GPS. As a reward, the participants’ tracking results were displayed graphically in the app to increase their awareness of what makes them happy and what does not. All the app’s features performed well during this study, and we collected the data according to the onion model (see Table 1). The most important feedbacks that we collected in focus groups at the university in Düsseldorf after the study are discussed below.

The participants’ first major critique was that the response options in the DRM needed to be optimized. Over half the participants said that the options were not detailed enough. Precise response options are required to understand people’s everyday life and everyday life feelings in a quantitative manner. To achieve this goal, we will continue to analyze the response options in the free fields, endeavour to adapt the response options to offer greater detail according to specific samples and studies and conduct qualitative research to complete our list of possible activities.

The participants’ second major critique was that the app’s artificial intelligence (AI) was insufficient. The participants wanted the app to learn from and adapt the response options, e.g., when a participant selects commuting, the app should pre-select the option in a vehicle in the where field. We have already developed an idea for an AI that will be able to suggest popular choices, for example, based on the user’s location according to GPS tracking. Each of these steps must be discussed carefully because it is important to avoid the risk of biasing the ratings and choices and to prevent participants from feeling that the app is tracking them too closely. Instead, participants should continue to perceive the app as a private diary that collects data anonymously to build their happiness profile.

The third aspect that requires improvement is the sense of “fun” participants experience when using the app. Even though the drop-out rates were low, and approximately one-quarter of the participants reported that they could imagine using the app for a month or permanently, only one-quarter clearly stated that they fully enjoyed using the app. Although this may have been caused by the intensity of the app used in this study, the aspect of “fun” must be improved. The participants explained that they generally appreciated receiving a notification that asked how happy they were at a specific moment in time. The participants particularly enjoyed maintaining a daily diary. They said that they liked analysing their day in the form of a digital diary, as they occasionally did this with the help of social media such as Facebook or Twitter; however, they preferred to perform the diary activity privately and anonymously. Another “fun function” could be a “decision helper” that would, for example, help a user decide whether to go to the cinema with his best friend or with his girlfriend by analyzing the happiness ratings of his past activities.

In sum, the main app features all performed well in the evaluation, and we were able to produce detailed datasets according to the onion model (apart from objective indicators); however, some future improvements are required to reduce user burden.

Conclusion and Future Prospects

Using the Happiness Analyzer, it is possible to measure SWB according the recommendations of the OECD guideline for measuring subjective well-being (OECD 2013) because the app does not require substantial resources (from researchers or participants) to capture the following: i) people’s SWB at multiple points in time via general questionnaires; ii) data about people’s everyday life and everyday feelings; iii) people’s direct feelings in the moment; and iv) a combination of subjective and objective well-being measurements.

A study in cooperation with the German Socio Economic Panel showed that it seems possible to collect representative datasets using the Happiness Analyzer when quota sampling is used, participants have access to a smartphone and participants are motivated well (Ludwigs et al. forthcoming).

In the future, we will endeavour to reduce user burden, optimize the app’s usability and enhance the tool’s flexibility for adaptation to different studies. Additionally, we plan to build more connections to objective data resources to link the SWB data with other objective well-being data to optimize the frame around the three subjective layers of the onion model. To accomplish this, we plan to develop and test face emotion recognition modules, voice emotion recognition modules, and a smartwatch application to connect the app to smartwatch sensors, as well as different connections to other devices including heart rate variability sensors and smart home devices, such as smart scales, and to other databases.

With the current tool and these future improvements, we hope to offer researchers the ability to conduct studies matching the OECD guidelines with less resources especially for collecting DRM and ESM data. By this we hope to support researchers to have better data to better understand SWB in order to increase the probability of developing more efficient interventions to increase people’s well-being, especially in community projects.


Compliance with Ethical Standards

Conflict of Interest

We hereby confirm that no one of the authors has any conflict of interest with this publication. Additionally, we declare that this research was conducted in line with the Declaration of Helsinki which explains all main rules for human research ethics.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Happiness Research OrganisationDusseldorfGermany

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