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The rise of online access panels has profoundly changed the market research landscape. Often presented by their owners as very powerful tools, they nevertheless raise important scientific questions, particularly regarding the representativeness of the samples they produce and, consequently, the validity of the information they provide. In this paper, we present an innovative approach, based on deep learning and sentiment analysis techniques, to assess in real time the representativeness of an online panel sample. The idea is to measure the extent to which the opinions of an online panel converge with opinions on social networks. To validate the proposed method, we conducted a case study on the emerging discussion on Coronavirus disease (COVID-19) vaccination. The results not only proved the representativeness of online panel sample, but also demonstrated the feasibility and effectiveness of our approach.

Despite its importance in terms of survey participation, the literature is sparse on how face-to-face interviewers differentially affect specific groups of sample units. This paper demonstrates how an alternative parametrization of the random components in multilevel models, so-called separate coding, delivers valuable insights into differential interviewer effects for specific groups of sample members. In the example of a face-to-face recruitment interview for a probability-based online panel, we detect small interviewer effects regarding survey participation for non-Internet households, whereas we find sizable interviewer effects for Internet households. We derive practical guidance for survey practitioners to address differential interviewer effects based on the proposed variance decomposition.

Recent years have seen a growing number of studies investigating the accuracy of nonprobability online panels; however, response quality in nonprobability online panels has not yet received much attention. To fill this gap, we investigate response quality in a comprehensive study of seven nonprobability online panels and three probability-based online panels with identical fieldwork periods and questionnaires in Germany. Three response quality indicators typically associated with survey satisficing are assessed: straight-lining in grid questions, item nonresponse, and midpoint selection in visual design experiments. Our results show that there is significantly more straight-lining in the nonprobability online panels than in the probability-based online panels. However, contrary to our expectations, there is no generalizable difference between nonprobability online panels and probability-based online panels with respect to item nonresponse. Finally, neither respondents in nonprobability online panels nor respondents in probability-based online panels are significantly affected by the visual design of the midpoint of the answer scale.

Declining response rates worldwide have stimulated interest in understanding what may be influencing this decline and how it varies across countries and survey populations. In this paper, we describe the development and validation of a short 9-item survey attitude scale that measures three important constructs, thought by many scholars to be related to decisions to participate in surveys, that is, survey enjoyment, survey value, and survey burden. The survey attitude scale is based on a literature review of earlier work by multiple authors. Our overarching goal with this study is to develop and validate a concise and effective measure of how individuals feel about responding to surveys that can be implemented in surveys and panels to understand the willingness to participate in surveys and improve survey effectiveness. The research questions relate to factor structure, measurement equivalence, reliability, and predictive validity of the survey attitude scale. The data came from three probability-based panels: the German GESIS and PPSM panels and the Dutch LISS panel. The survey attitude scale proved to have a replicable three-dimensional factor structure (survey enjoyment, survey value, and survey burden). Partial scalar measurement equivalence was established across three panels that employed two languages (German and Dutch) and three measurement modes (web, telephone, and paper mail). For all three dimensions of the survey attitude scale, the reliability of the corresponding subscales (enjoyment, value, and burden) was satisfactory. Furthermore, the scales correlated with survey response in the expected directions, indicating predictive validity., ZIS - The Collection of Items and Scales for the Social Sciences

A growing trend in UX research is the use of Online Panels (OPs), namely people enrolled in a web platform who have agreed to participate regularly in online studies and/or in the execution of simple and repetitive operations. The effect of the participation of such “professional respondents” on data quality has been questioned in a variety of fields (e.g., Psychology and Marketing). Notwithstanding the increasing use of OPs in UX research, there is a lack of studies investigating the bias affecting usability assessments provided by this type of respondents. In this paper we have addressed this issue by comparing the usability evaluations provided by professional respondents commonly involved in debugging activities, non-professional respondents, and naive people not belonging to any OP. In a set of three studies, we have addressed both the effect of expertise and type of task (debugging vs. browsing) on the usability assessments. A total of 138 individuals participated in these studies. Results showed that individuals who performed the debugging test provided more positive usability ratings regardless of their skills, conversely, professional respondents provided more severe and critical ratings of perceived usability than non-professionals. Finally, the comparison between the online panelists and naive users allowed us to better understand whether professional respondents can be involved in usability evaluations without jeopardizing them.

The Living Lab on Media Content and Platforms (LLMCP) has developed the LLMCP LisPan project, with the objective of studying the habits and behaviors of Internet users, through an innovative online panel of users using web tracking technology. It was possible to analyze overt online behavior for a sample panel of 70 college students, for six months, and to compare this analysis with their answers to a survey, which additionally was carried out. Results confirm extensive Internet use, but with differences between Friday, Saturday and Sunday, compared to the rest of the week, and the existence of periods of differing intensity, throughout the day. Differences between an “online” dimension of Internet use (search and use of online content) and a “connected” dimension (online interaction with other users) have also been found. It has also been possible to conclude that three-quarters of the participants over-report the frequency of their Internet access. O Living Lab on Media Content and Platforms (LLMCP) desenvolveu o projeto LLMCP LisPan, com o objetivo de estudar hábitos e comportamentos dos utilizadores da Internet, através de um inovador painel online de utilizadores com recurso a tecnologia de web tracking. Foi possível analisar o comportamento online efetivo de um painel de 70 alunos do ensino superior, durante seis meses, e comparar essa análise com as respostas a um inquérito por questionário, para a mesma amostra. Os resultados confirmam o uso extensivo da Internet, mas com diferenças entre sexta, sábado e domingo, em relação ao resto da semana, e a existência de períodos de intensidade diferente, ao longo do dia. Foram, também, encontradas diferenças entre uma dimensão “online” de utilização da Internet (pesquisa e utilização de conteúdo online) e uma dimensão “conectada” (interação online com outros utilizadores). Foi, ainda, possível concluir que três quartos dos participantes sobre-estimam a frequência do seu acesso à Internet.

The outbreak of COVID-19 has sparked a sudden demand for fast, frequent and accurate data on the societal impact of the pandemic. This demand has highlighted a divide in survey data collection: Most probability-based social surveys, which can deliver the necessary data quality to allow valid inference to the general population, are slow, infrequent and ill-equipped to survey people during a lockdown. Most non-probability online surveys, which can deliver large amounts of data fast, frequently and without interviewer contact, however, cannot provide the data quality needed for population inference. Well aware of this chasm in the data landscape, at the onset of the pandemic, we set up the Mannheim Corona Study (MCS), a rotating panel survey with daily data collection on the basis of the long-standing probability-based online panel infrastructure of the German Internet Panel (GIP). The MCS has provided academics and political decision makers with key information to understand the social and economic developments during the early phase of the pandemic. This paper describes the panel adaptation process, demonstrates the power of the MCS data on its own and when linked to other data sources, and evaluates the data quality achieved by the MCS fast-response methodology. © 2021 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.

The Center for Socio Political Data (CDSP) created the ELIPSS web panel in 2012. Its probability-based sample and scientific purpose made the project the first of its kind in France. Its mobile web dimension made it the first in Europe. The CDSP’s IT Team conceived and developed several survey panel tools for ELIPSS. For example, to support multi-channel interactions between the panel members and the panel managers, a set of features managing phone calls, electronic and traditional mail, text messages, and in-app notification messages were developed and integrated to the ELIPSS application suite over time. These interactions were captured in a massive set of raw data. We’ll begin the workshop with a brief tour of the ELIPSS in-house application system. One of the challenges we dealt with was an important HR turnover. We’ll give a feedback on the best practices we implemented in order to render the processes more fluid and assist the CDSP teams capture and releasethe data, DDI metadata and paradata of the project in due time. A DDI metadata creation exercise, to describe a paradata file, will end the first block of this session. The workshop will continue with a paradata visualization experiment. As previously mentioned, the ELIPSS application suite recorded the online and offline interactions between the panel managers and the panelists. Thus, a massive set of raw data were collected. We’ll provide further insight on the curation and DDI-C metadata creation process we went through to manage a ready-to-use paradata set. We’ll then invite participants to join a collaborative Jupyter Notebook to analyze and visualize it. No previous knowledge of the DDI standard is required to participate. This workshop is based on an experience of more than a decade of producing data and metadata collection tools and repositories at the CDSP.

Anketiranje v okviru spletnih panelov je postalo ena glavnih metod zbiranja podatkov. Njihova uporabnost narašča iz leta v leto, saj se uporablja na različnih področjih raziskav. V svojem diplomskem delu sem s pomočjo prebrane literature predstavil spletne panele, nato pa sem se osredotočil na analizo časa, ki ga panelisti porabijo za izpolnitev anketnega vprašalnika. Predvsem me zanima, ali obstaja razlika v času izpolnjevanja ankete pri panelistih z različnimi družbeno-demografskimi lastnostmi. Za družbeno-demografske lastnosti sem uporabil spremenljivke spol, starost in dosežena izobrazba, poleg tega pa sem v raziskavo vključil tudi spremenljivki, ki merita, koliko truda ter pozornosti so panelisti na osnovi lastne ocene namenili izpolnjevanju ankete. Na primeru ankete v okviru komercialnega spletnega panela sem ugotovil, da je čas, ki so ga panelisti porabili za izpolnitev vprašalnika, povezan s spolom, starostjo, trudom in pozornostjo, ne pa tudi z izobrazbo. Ugotovitve so pomembne za preučevanje problematike prehitrega izpolnjevanja vprašalnika in morebitnih posledicah na kakovost anketnih podatkov. Surveying using online panels has become one of the main methods of data collection. Their usefulness is increasing from year to year and they are used in various fields of research. In the thesis, I present online panels in general and then focus on the analysis of time that panelists spend to fill out a questionnaire. I am particularly interested in whether there is a difference in time taken to complete the survey for panelists with different socio-demographic characteristics. For socio-demographic variables I used gender, age, and education. I also included variables that measure how much effort and attention the panelists put into the survey, based on their own estimation. Using data from a survey within a commercial online panel, I found out that the time spent by the panelists for completing the questionnaire was correlated with gender, age, effort and attention, but not with education. Results are important for further studying the issue of speeding and its consequences for survey data quality.

Plain English summary Evidence shows that public and patient involvement in research has a positive effect on its quality and end-results. Thus, public and patient involvement in all stages of research is becoming commonplace. There are limited detailed examples however, that describe how to make this possible, especially for those doing PhD research. Doctoral researchers are often new to research practice or have limited experience and are often bound by strict time and financial constraints. It is also not usually a requirement of the award to involve public and patients in their research. Hence, they may not feel confident or motivated to involve or engage with public and patients during their research. We, four doctoral researchers, share examples from our own research studies that have included different approaches to public and patient involvement. Two studies formed public and patient advisory groups who helped design the research questions, data collection tools and recruitment methods. One enlisted the help of an online public and patient panel from a local hospital. A different study worked with patients from an established group to help define key medical words. We did face some challenges, such as the need to develop good group work skills and to apply for grants to cover reimbursement, but we all found it beneficial to involve patients in our studies. We noticed a positive effect on each study’s progression and an improvement in our own self-esteem. In addition, having public and patient involvement helped reduce the isolation we felt as doctoral researchers. Thus, we strongly encourage more doctoral researchers to involve public and patients in their studies. Abstract Public and patient involvement (PPI) has been shown to have a positive impact on health and social care research. However, adequate examples describing how to operationalise effective PPI, especially in doctoral studies, are lacking. Hence, doctoral researchers new to research, or those with limited experience, can be discouraged from facilitating PPI in their research. This paper aims to describe and discuss in detail the approaches used by four doctoral researchers to incorporate PPI at different stages of their research studies from study design to disseminating findings. We aim to inform other doctoral researchers about the challenges and limitations relating to PPI that we faced. Through these, we share pragmatic recommendations for facilitating PPI during doctoral studies. The description of four case studies demonstrated that PPI could be incorporated at various stages during doctoral research. This has had a beneficial impact on our research study progression, researcher self-esteem and lastly, helped alleviate researcher isolation during doctoral studies.

Launched in 2012, ELIPSS (Longitudinal Internet Studies for Social Sciences) is the first French online representative panel dedicated to the social sciences research community. Each panel member is provided with a touch screen tablet and a 4G connection. The project has now hit its stride with around 2500 panel members and 70 surveys conducted so far. The Center for Socio-Political Data (CDSP) is responsible for the implementation of this project within the DIME-SHS (Data, infrastructures and survey methods in the humanities and social sciences) consortium. The CDSP IT team designs the software tools that collect data and paradata from the ELIPSS panel. These tools range from web services facilitating fieldwork management to a tailor-made Android application for collecting data directly from devices. First, designing an Android application collecting data for research purposes represents a real challenge. Very few guidelines are publicly available except for designing market-oriented mobile applications. Second, for the ELIPSS project, conflicting objectives have to be reconciled. One survey is fielded monthly and tight deadline are regularly to be met. This presentation will provide a concrete view of the design process of a mobile application for data and paradata collection from an online panel. We’ll also provide a feedback on how to collaborate with a device manufacturer, how to deal with confidentiality concerns in data and paradata collection, how to address ergonomy issues when designing questionnaires, and generally how to address complex research questions.

Questa tesi si colloca nell’ambito della “survey methodology” e indaga il concetto di qualità dei dati nelle indagini campionarie condotte sui membri dei panel online. Nel tempo tali indagini hanno implementato diverse tecniche per superare varie sfide. In particolare, i costi elevati, la necessità di ottenere dati in tempi brevi e i problemi di copertura della popolazione, hanno portato allo sviluppo dei panel online a partire dagli anni ‘90. I panel online sono gruppi di persone che, tramite iscrizione volontaria, hanno acconsentito a partecipare a indagini condotte via web in cambio di una ricompensa. Questi panel hanno il vantaggio di ridurre tempi e costi dedicati alla raccolta dati e di fornire una lista di membri da cui estrarre campioni di rispondenti. L’obiettivo principale di questa tesi è valutare la qualità dei dati raccolti attraverso un panel online non probabilistico italiano, confrontando le stime ottenute da un’indagine web condotta sui membri del panel con quelle di un’indagine ISTAT di tipo probabilistico che adotto come “gold standard”. In particolare mi focalizzo sulle distorsioni generate i) dalla mancata copertura Internet della popolazione italiana, ii) dal meccanismo di auto-selezione dei membri del panel e dei partecipanti all’indagine web, iii) dal fenomeno della non risposta (confronto rispondenti e non rispondenti all’indagine web), e iv) dall’errore di misurazione che può manifestarsi durante la compilazione del questionario (confronto delle stime campionarie con l’indagine di riferimento). Inoltre applico un sistema di pesi per eliminare le distorsioni. Utilizzo tre fonti di dati. La prima sono i dati di profilazione dei membri del panel, solitamente non forniti ai ricercatori, che uso per studiare: i) la rappresentatività del panel rispetto alla popolazione Internet e alla popolazione generale e ii) quella dei rispondenti all’indagine web rispetto ai membri del panel e iii) le differenze tra le caratteristiche di rispondenti e non rispondenti. Il secondo dataset contiene i dati raccolti con l’indagine web su un campione di membri del panel. L’ultima fonte è il dataset dell’indagine di riferimento. Il mio studio ha un impatto rilevante nell’ambito della ricerca sull’indagine campionaria sia in Italia che all’estero. Infatti è il primo studio condotto in Italia che usa i dati raccolti da un’indagine sui membri di un panel online non probabilistico e che verifica la qualità dei dati raccolti con questo metodo confrontando le stime ottenute con un benchmark. La rilevanza del mio studio per la ricerca internazionale sui panel online non probabilistici consiste nell’approfondire i risultati finora ottenuti in merito a tre temi di ricerca poco studiati. In primo luogo, da quanto mi risulta, solo tre pubblicazioni hanno studiato le differenze nelle caratteristiche socio-demografiche dei membri di un panel, della popolazione Internet e/o della popolazione generale. Inoltre, un solo studio europeo ha valutato le differenze tra rispondenti e non rispondenti in un’indagine condotta su un panel. Infine, ridotto è il numero di ricerche che ricorrono al confronto con un’indagine “gold standard” per studiare la qualità dei dati raccolti tramite panel online non probabilistici. I risultati del mio studio hanno evidenziato che le stime ottenute dall’indagine condotta sul panel sono distorte rispetto ai benchmark, ma l’entità della distorsione varia molto in funzione della stima considerata (ad es., le stime su variabili comportamentali producono una distorsione più elevata rispetto a quelle sulle caratteristiche socio-demografiche). Inoltre, l’applicazione dei pesi è efficace nel correggere le distorsioni nelle stime, ma non nell’eliminarle completamente. This thesis sits in the survey-methodology field and explores the challenging concept of data quality when using online panels as a sample source in the survey industry. Over time, sample surveys implemented different techniques to overcome various challenges. In particular, high costs, need for timely data delivery, and undercoverage (in the case of telephone surveys) are the main drivers of the development of online panels in survey research, from the 1990s on. Online panels are large pools of registered people who agreed to take part in web-based research in exchange for some form of incentive. They have the advantages of i) reducing both costs and time devoted to data collection and delivery, and ii) providing a sampling frame of registered individuals who consented to participate in surveys. The main objective of this thesis is to empirically assess the quality of primary data collected from an Italian non-probability online-panel survey, comparing the estimates obtained from this web survey to those obtained from a probability-based survey conducted by the Italian National Institute of Statistics (ISTAT), used as benchmarks. In particular, I focused on i) undercoverage and self-selection bias in different samples (i.e., the Internet population, panel members, and web survey respondents), ii) nonresponse bias, comparing the characteristics of web survey respondents to those of nonrespondents, iii) data quality, comparing the estimates obtained from the web survey to those obtained from a probability-based reference survey, and iv) a weighting adjustment to remove bias. I used three data sources. The first is data of all the members of the Italian non-probability online panel, recorded in the panel archive. Using this valuable source (because companies usually do not provide panellists’ data to their clients), I was able to study i) the panel members’ representativeness in comparison to both the Internet and the general population, ii) the web survey respondents’ representativeness in comparison to the panel members, and iii) the differences between the characteristics of web survey respondents and those of nonrespondents. The second dataset comes from my primary data collection, conducted on a sample of Italian adults selected from the panel. The last source is the dataset from a probability-based reference survey. The impact of my study is relevant to both the Italian and the international survey research fields. In particular, this is the first study in Italy that uses panel survey data, and compares the panel survey estimates to those from a probability-based reference survey, to assess the quality of data collected from a non-probability online panel. The relevance of my study for international research on non-probability online panels consists in broadening the current findings on three under-researched topics. Firstly, to the best of my knowledge, there are only three publications that have assessed the differences in the demographic and socio-economic characteristics of panel members, the Internet population and/or the general population. Secondly, there is only one European study on the assessment of the differences between study respondents and nonrespondents. Lastly, there are only a few studies that use a gold standard to assess data quality in non-probability online panels. The results from my study showed that i) the online panel survey estimates substantially deviate from the benchmarks, but the magnitude of bias varies widely according to the estimate (i.e., estimates from behavioural variables produce higher distortion than estimates from socio-demographics), and ii) weighting does correct variations in the data, but does not remove the overall bias (that is higher for the behavioural variables than for the socio-demographics).

While online panels offer numerous advantages, they are often criticized for excluding the offline population. Therefore, some probability-based online panels have developed offline population inclusion strategies. Two dominant approaches prevail: providing internet equipment and offering an alternative survey participation mode. We investigate the impact of these approaches on two probability-based online panels in Germany: the German Internet Panel, which provides members of the offline population with internet equipment, and the GESIS Panel, which offers members of the offline population to participate via postal mail surveys. In addition, we explore the impact of offering an alternative mode only to non-internet users versus also offering the alternative mode to internet users who are unwilling to provide survey data online. Albeit lower recruitment and/or panel wave participation probabilities among offliners than onliners, we find that including the offline population has a positive long-term impact on sample accuracy in both panels. In the GESIS Panel, the positive impact is particularly strong when offering the alternative participation mode to non-internet users and internet users who are unwilling to provide survey data online.

Since 2009 the Netherlands has conducted extensive online screening surveys to establish the number, trends and demographic profile of recreational anglers, resulting in a large dataset of almost 500.000 data records between 2009 and 2017. Participation in both marine and fresh water recreational angling were analysed using general linear models (GLM). Results showed a steady decline in the participation rate in fresh water angling. The participation rate in marine angling was smaller, and declined from 2009 to 2011, but remained similar afterwards. Analysis of demographics (age, gender, education and region) showed that males were overall much more likely to participate in recreational angling than females. Additionally, the age distribution differed for marine and fresh water. In marine water young adult males (age group (25,45]) had the highest participation rate, whereas in fresh water the youngest age group, (5,15], had the highest participation rate, closely followed by young adult males (age group (25,45]). Additionally, lower educated persons were more likely to participate in recreational angling than higher educated persons. This study provides more insight in the culture of Dutch recreational angling. Furthermore, the participation model can be used to predict future angling participation.

People’s willingness to share data with researchers is the fundamental raw material for most social science research. So far, survey researchers have mainly asked respondents to share data in the form of answers to survey questions but there is a growing interest in using alternative sources of data. Less is known about people’s willingness to share these other kinds of data. In this study, we aim to: 1) provide information about the willingness of people to share different types of data; 2) explore the reasons for their acceptance or refusal, and 3) try to determine which variables affect the willingness to perform these additional tasks. We use data from a survey implemented in 2016 in Spain, in which around 1,400 panelists of the Netquest online access panel were asked about their hypothetical willingness to share different types of data: passive measurement on devices they already use; wearing special devices to passively monitor activity; providing them with measurement devices and then having them self-report the results; providing physical specimens or bodily fluids (e.g. saliva); others. Open questions were used to follow up on the reasons for acceptance or refusal in the case of the use of a tracker. Our results suggest that the acceptance level is quite low in general, but there are large differences across tasks and respondents. The main reasons justifying both acceptance and refusal are related to privacy, security and trust. Our regression models also suggest that we can identify factors associated with such willingness., methods, data, analyses, Vol 13, No 2 (2019)

Declining response rates worldwide have stimulated interest in understanding what may be influencing this decline and how it varies across countries and survey populations. In this paper, we describe the development and validation of a short 9-item survey attitude scale that measures three important constructs, thought by many scholars to be related to decisions to participate in surveys, that is, survey enjoyment, survey value, and survey burden. The survey attitude scale is based on a literature review of earlier work by multiple authors. Our overarching goal with this study is to develop and validate a concise and effective measure of how individuals feel about responding to surveys that can be implemented in surveys and panels to understand the willingness to participate in surveys and improve survey effectiveness. The research questions relate to factor structure, measurement equivalence, reliability, and predictive validity of the survey attitude scale.The data came from three probability-based panels: the German GESIS and PPSM panels and the Dutch LISS panel. The survey attitude scale proved to have a replicable three-dimensional factor structure (survey enjoyment, survey value, and survey burden). Partial scalar measurement equivalence was established across three panels that employed two languages (German and Dutch) and three measurement modes (web, telephone, and paper mail). For all three dimensions of the survey attitude scale, the reliability of the corresponding subscales (enjoyment, value, and burden) was satisfactory. Furthermore, the scales correlated with survey response in the expected directions, indicating predictive validity.

BACKGROUND: Public and stakeholder engagement can improve the quality of both research and policy decision making. However, such engagement poses significant methodological challenges in terms of collecting and analysing input from large, diverse groups. OBJECTIVE: To explain how online approaches can facilitate iterative stakeholder engagement, to describe how input from large and diverse stakeholder groups can be analysed and to propose a collaborative learning framework (CLF) to interpret stakeholder engagement results. METHODS: We use 'A National Conversation on Reducing the Burden of Suicide in the United States' as a case study of online stakeholder engagement and employ a Bayesian data modelling approach to develop a CLF. RESULTS: Our data modelling results identified six distinct stakeholder clusters that varied in the degree of individual articulation and group agreement and exhibited one of the three learning styles: learning towards consensus, learning by contrast and groupthink. Learning by contrast was the most common, or dominant, learning style in this study. CONCLUSION: Study results were used to develop a CLF, which helps explore multitude of stakeholder perspectives; identifies clusters of participants with similar shifts in beliefs; offers an empirically derived indicator of engagement quality; and helps determine the dominant learning style. The ability to detect learning by contrast helps illustrate differences in stakeholder perspectives, which may help policymakers, including Patient-Centered Outcomes Research Institute, make better decisions by soliciting and incorporating input from patients, caregivers, health-care providers and researchers. Study results have important implications for soliciting and incorporating input from stakeholders with different interests and perspectives. Language: en

RESUMEN: Las empresas de electrodomésticos afianzadas en mercados maduros de bienes de consumo enfrentan un momento clave en el que los avances tecnológicos están cambiando rápidamente la forma en cómo los usuarios interaccionan con los productos, y en donde los nuevos modelos de negocios del mundo digital son grandes fuentes de ingresos económicos. Para adaptarse y trazar la ruta de la innovación líderes del mercado como BSH electrodomésticos abordan líneas de I+D enfocadas en comprender la interacción de sus clientes con sus productos, con el objetivo de esbozar los desarrollos tecnológicos y digitales que respondan a las necesidades actuales y futuras de los consumidores. Las tecnologías de información y comunicaciones se han convertido en los grandes surtidores de datos e información para tal fin. Las empresas utilizan todo lo que tienen a mano para definir sus líneas de actuación y marcar el rumbo de sus organizaciones hacia la transición digital. Este trabajo contempla la integración de diversas áreas de la ingeniería para la captura y almacenamiento de señales, el data mining, el automatismo de la estadística y el know-how de encimeras de gas. Lo anterior será integrado en un panel online que permitirá analizar a través de indicadores estadísticos la interacción de los usuarios con las encimeras de gas y el rendimiento de estos productos en el uso doméstico. ABSTRACT: Home appliance companies well-established in mature consumer goods markets face a key time when technological advances are rapidly changing the way users interact with products, and where new business models in the digital world are major sources of revenue. To adapt and trace the path of innovation, market leaders such as BSH Household Appliances approach lines of R&D focused on understanding the interaction of their customers with their products, with the aim of outlining the technological and digital developments that respond to the current and future needs of consumers. Information and communication technologies have become the major suppliers of data and information for this purpose. Companies use everything they have at hand to define their lines of action and set the course for their organisations towards the digital transition. This work contemplates the integration of different areas of engineering for the capture and storage of variables, data mining, the automation of statistics and the know-how of gas cooktops. The above will be integrated into an online panel that will allow the analysis of user interaction with gas appliances and the performance of these products in domestic use through statistical indicators. Máster en Empresa y Tecnologías de la Información

У статті розглянуто сучасні тенденції маркетингових досліджень в інтернет-середовищі. Доведено, що розвиток інтернет-технологій та поширення Інтернету на всі сфери життя людини призводять до переміщення маркетингових досліджень споживацької поведінки в інтернет-середовище. Проаналізовано переваги та недоліки онлайн опитувань в Інтернеті. Виявлено напрями застосування онлайн панелей та онлайн спільнот. Наведено методи формування вибірки для онлайн панельного дослідження. Систематизовано фактори, за якими приймається рішення щодо можливості застосування онлайн панелей. В статье рассмотрены современные тенденции маркетинговых исследований в интернет-среде. Доказано, что развитие интернет-технологий и распространение Интернета на все сферы жизни человека приводят к перемещению маркетинговых исследований потребительского поведения в интернет-среду. Проанализированы преимущества и недостатки опросов в Интернете. Выявлены направления применения онлайн панелей и онлайн сообществ. Приведены методы формирования выборки для онлайн панельного исследования. Систематизированы факторы, по которым принимается решение о возможности применения онлайн панелей. The aim of the article is to identify and to study trends of application and forming of sampling selection of participants, control of survey quality and methods of conducting online panels and online communities. The article provides the analysis of the modern tendencies of marketing researches on the Internet. The development of Internet technologies and the spread of the Internet to all spheres of human life lead to the relocation of marketing research of consumer behavior to the Internet. The structure of the Ukrainian population in accordance with the structure of the Internet audience leads to the representativeness of samples of marketing research on the Internet. We analyzed the types of respondents' recruiting for online panels and online communities, their advantages and disadvantages. We revealed areas of application of online panels and online communities. Online panels are applicable in two cases: the target group that consists exclusively of Internet users and the target group amounting not less than 50% of Inter net users. Exceptionally in these cases, we can extrapolated the results of panel studies to the entire population. Online communities are used for qualitative research and there are no strict requirements for the population and sample for them. Online communities are classified into three groups: project, syndicated and permanent. Each group is characterized by the goals of its creation, the volumes of samples being generated and the methods of operation. We analyzed and systematized methods of quality control online survey. Quality control of the survey consists of three stages: monitoring during the interview, data quality control, and panel management. At each stage, a system of indicators is formed, which confirms the quality of the survey. We considered further perspectives of Internet research, which don`t need Internet surveys. An increase in information about consumers (geolocation, activity on the Internet, profiles in social networks) can contribute to this.

Evidence-based insight on the effectiveness of reminders in web-based data collection in online panels is scarce. Thirty-eight studies were conducted in three different online panels to examine the effect of reminders on response rates, retention rates, and two facets of response quality (i.e., item omissions and response nondifferentiation). The studies were summarized using a meta-analytical approach including moderator tests ( N = 246,040). Results indicated that reminders in web-based data collection increase response (i.e., from 49.5% to 65.6%, ESpd = .152). However, reminders somewhat reduce retention (i.e., from 98.0% among prereminder respondents to 97.2% among reminded respondents, ESpd = −.008), which might be explained by differences in motivation and computer literacy between unreminded and reminded respondents. No differences were found between prereminder respondents and reminded respondents in item omissions and response nondifferentiation.

One of the methods for evaluating online panels in terms of data quality is comparing the estimates that the panels provide with benchmark sources. For probability-based online panels, high-quality surveys or government statistics can be used as references. If differences among the benchmark and the online panel estimates are found, these can have several causes. First, the question wordings can differ between the sources, which can lead to differences in measurement. Second, the reference and the online panel may not be comparable in terms of sample composition. Finally, since the reference estimates are usually collected face-to-face or by telephone, mode effects might be expected. In this article, we investigate mode system effects, an alternative to mode effects that does not focus solely on measurement differences between the modes, but also incorporates survey design features into the comparison. The data from a probability-based offline-recruited online panel is compared to the data from two face-to-face surveys with almost identical recruitment protocols. In the analysis, the distinction is made between factual and attitudinal questions. We report both effect sizes of the differences and significances. The results show that the online panel differs from face-to-face surveys in both attitudinal and factual measures. However, the reference surveys only differ in attitudinal measures and show no significant differences for factual questions. We attribute this to the instability of attitudes and thus show the importance of triangulation and using two surveys of the same mode for comparison., methods, data, analyses, Vol 9, No 1 (2015)