Data in Brief 35 (2021) 106813 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Survey data on the impact of COVID-19 on parental engagement across 23 countries ✩ Eliana Maria Osorio-Saez a,∗, Nurullah Eryilmaz a, Andres Sandoval-Hernandez a, Yui-yip Lau b, Elma Barahona c, Adil Anwar Bhatti d, Godfried Caesar Ofoe e, Leví Astul Castro Ordóñez c, Artemio Arturo Cortez Ochoa f, Rafael Ángel Espinoza Pizarro g, Esther Fonseca Aguilar c, Maria Magdalena Isac h, K.V. Dhanapala i, Kalyan Kumar Kameshwara a, Ysrael Alberto Martínez Contreras j, Geberew Tulu Mekonnen k, José Fernando Mejía l, Catalina Miranda m, Shehe Abdalla Moh’d n, Ricardo Morales Ulloa c, K Kayon Morgan o, Thomas Lee Morgan p, Sara Mori q, Forti Ebenezah Nde r, Silvia Panzavolta q, Lluís Parcerisa s, Carla Leticia Paz c, Oscar Picardo t, Carolina Piñeros u, Pablo Rivera-Vargas v, Alessia Rosa q, Lina Maria Saldarriaga l, Adrián Silveira Aberastury w, YM Tang b, Kyoko Taniguchi x, Ernesto Treviño m, Carolina Valladares Celis f, Cristóbal Villalobos m, Dan Zhao a, Allison Zionts y a University of Bath, United Kingdom b The Hong Kong Polytechnic University, Hong Kong c Universidad Pedagógica Nacional Francisco Morazán, Honduras d University of Karachi, Pakistan e Ghana Education Service, Ghana f University of Bristol, United Kingdom g Universidad Nacional de Costa Rica, Costa Rica h KU Leuven, Belgium i University of Colombo, Sri Lanka j Pontificia Universidad Católica del Perú, Peru k University of Tasmania, Australia ✩ Data Availability: Data on the impact of COVID 19 on Parental Engagement across 23 countries (Original data) (Mendeley Data) ∗ Corresponding author. E-mail address: Emos20@bath.ac.uk (E.M. Osorio-Saez). Social media: (E.M. Osorio-Saez), (N. Eryilmaz), (A. Sandoval-Hernandez) https://doi.org/10.1016/j.dib.2021.106813 2352-3409/© 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) 2 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 l Programa Aulas en Paz - Universidad de los Andes, Colombia m Pontificia Universidad Católica de Chile, Chile n State University of Zanzibar, Tanzania o University of Hartford, The United States p Sacred Heart University, The United States q Università Telematica degli Studi (IUL), Italy r University of Yaounde, Cameroon s Universitat Autònoma de Barcelona, Spain t Arizona State University, The United States u Corporación Colombiana de Padres y Madres - Red PaPaz, Colombia v Universidad de Barcelona, Spain w Universidad de la República, Uruguay x Hiroshima University, Japan y Goldsmiths, University of London, United Kingdom a r t i c l e i n f o a b s t r a c t Article history: This data article describes the dataset of the International Received 24 December 2020 COVID-19 Impact on Parental Engagement Study (ICIPES). Revised 24 January 2021 ICIPES is a collaborative effort of more than 20 institutions Accepted 27 January 2021 to investigate the ways in which, parents and caregivers built Available online 1 February 2021 capacity engaged with children’s learning during the period of social distancing arising from global COVID-19 pandemic. Keywords: A series of data were collected using an online survey con- COVID-19 Parental engagement ducted in 23 countries and had a total sample of 4,658 par- Acceptance ents/caregivers. The description of the data contained in this Confidence article is divided into two main parts. The first part is a Socioeconomic status descriptive analysis of all the items included in the survey CFA and was performed using tables and figures. The second part MG-CFA refers to the construction of scales. Three scales were con- structed and included in the dataset: ‘parental acceptance and confidence in the use of technology’, ‘parental engage- ment in children’s learning’ and ‘socioeconomic status’. The scales were created using Confirmatory Factor Analysis (CFA) and Multi-Group Confirmatory Analysis (MG-CFA) and were adopted to evaluate their cross-cultural comparability (i.e., measurement invariance) across countries and within sub- groups. This dataset will be relevant for researchers in dif- ferent fields, particularly for those interested in international comparative education. © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Specifications Table Subject Education, Psychometrics Specific subject area Parental Engagement Type of data Table, Figure, Text How data were acquired Online Survey Data format Raw and Analysed Data, Descriptive Statistics Parameters for data collection Countries, Location: Area, Parent/carer Gender, Parent/carer Age, Parent/carer years of schooling, Family socioeconomic status, Children’s Gender, Children’s Age, Children’s years of schooling, Number of children in the household, Parental engagement in school activities, Parental use of technology for social purposes, Parental use of technology for building capacity, Parental use of technology tools/resources provided by schools/governments. (continued on next page) E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 3 Description of data collection A series of data were collected via online distributed questionnaires in all participating countries (23 countries). The questionnaire was created in an international English version and subsequently translated and adapted to the official languages and localisms of the participating countries. After the first translation, questionnaires were back-translated into English, the equivalence of the questionnaire in the target languages was evaluated and relevant adjustments made. The questionnaires were then distributed through the networks of the participating institutions in each country. The ICIPES target population was parents/caregivers of children between 6 and 16 years old, living with their child and between grade 1 and 13 that represents between 1 and 13 years of schooling, counting from the beginning of Level 1 of the International Standard Classification of Education (ISCED). An intended sample of at least 200 parents was established and countries not reaching this target were flagged. The international English version of the questionnaire can be accessed here: http://dx.doi.org/10.17632/kvvdgvs8zs.2. Due to confidentiality agreements, all details of interviewees’ personal particulars are excluded. Data source location Data were collected from 4658 parents/caregivers across 23 countries (Cameroon, Ethiopia, Ghana, Tanzania Zanzibar, China (Mainland, Hong Kong and Macao), Japan, Belgium, Italy, Spain, Turkey, United Kingdom, India, Pakistan, Sri Lanka, Chile, Colombia, Costa Rica, El Salvador, Honduras, Mexico, Peru, Uruguay, The United States) in 5 regions (Africa, East Asia, Europe, South Asia and America). Data accessibility Repository name: Mendeley Data Repository: http://dx.doi.org/10.17632/kvvdgvs8zs.2 Value of the Data • The database offers first hand valuable information about parental engagement, school sup- port for parents and children, home-schooling and family life balance and parental accep- tance and confidence in the use of technology from 23 countries around the world. • The international database provides a rich environment for examining how parents and care- givers relate to children’s learning in this period of social distancing caused by the global COVID-19 outbreak. • The international database offers data comparable on parental practices during the lockdown across 23 countries and five regions (America, South-Asia, East-Asia, Africa and Europe), al- lowing investigations on aspects of specific relevance in each of these geographic regions. • The international dataset contains scales such as parental engagement, parental acceptance and confidence in the use of technology scale and family socioeconomic status, which allow testing hypothesis about the interactions of these and other variables across and within the participating countries. • The international database involves considerable information for the researchers, analysts, policymakers and education stakeholders to take steps and measures to improve the quality of parental engagement in children’s education during and after the lockdown period. 1. Data Description With the advent of the detection of the first case of COVID-19 in the late of November in China and later in the beginning of March in the other countries, an urgent governance step has been initiated by the Ministries of National Education to carry on various educational activities remotely since schools have experienced compulsory shut downs until the end of April-June, depending in which country you are in, to prevent spreading the virus across countries [17]. The pandemic has shown countless barriers that families face daily in their goal of educating their children. It is a unique historical opportunity for researchers and policymakers to understand all the lessons from this global emergency and work closely with parents/caregivers to support 4 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 them in engaging with children’s learning as they are the best partners in mitigating both short and long-term impacts of COVID-19 on children’s learning. Research connects children social and cognitive development to parents’ educational practices at home [9]. Mostly, to parental practices that have the potential to provide learning experiences for children, such as: reading to children, using complex language, responsiveness and warmth in interactions and conversations, playing with numbers, painting and drawing, learning about numbers and letters and going to the library [5,4,12]. In the current pandemic, parents have spent more time with their children. Moreover, the primary responsibility for enforcing and maintaining young people’s educational engagement lies with them. While there is a substantial body of literature which explores parental engage- ment in education (e.g., [2]), the uniqueness of the current circumstances demands more inves- tigation of how parents are building capacity at home, what activities are they developing with their children, what kind of support they have received from the schools, and how parents have shaped and built their roles and IT skills. The data provided in this study allows researchers to embark on investigations to the above and other related areas and questions. 1.1. Identification variables in the dataset All ICIPES 2020 data files contain several identification variables that provide information to identify the participants’ important characteristics. The variables do not allow identification of individual parents within countries. IDCNTRY This variable indicates the country or participating education system; the data refers to an up to six-digit numeric code based on the ISO 3166 classification, with adaptations reflecting the participating education systems. This variable should always be used as the first linking variable whenever files are linked within and across countries. CNT This variable indicates the participant’s three-letter alphanumeric code, based on the ISO 3166-1 coding, with adaptations reflecting the participating country. CNTPARID This variable indicates the country’s three numeric code, based on the ISO 3166–1 coding, plus a unique identifier for each respondent. REGID This variable identifies the specific region that each country belongs to. There are five geo- graphical regions: 1 Africa, 2 East Asia, 3 Europe, 4 South Asia and 5 America. REG This variable indicates the participant’s three-letter alphanumeric code, based on the ISO 3166- 1 coding, with adaptations reflecting the participating geographical regions. URN This variable identifies the specific questionnaire that was administered to each parent. This number was automatically provided by the Online Surveys tool. In this study, the online survey was conducted with semi-structured questionnaires. Online survey is one of the best ways to reduce the cost when conducting a study, but it is also an effective way to get real data from the online population [13]. A total of 4658 respondents (par- ents) answered questionnaires from the participating countries: Cameroon, Ethiopia, Ghana, Tan- zania, China (i.e., Mainland, Hong Kong, and Macao), Japan, Belgium, Italy, Spain, Turkey, United Kingdom, India, Pakistan, Sri Lanka, Chile, Colombia, Costa Rica, El Salvador, Honduras, Mexico, Peru, Uruguay, the United States. Later, the countries split into five regions: Africa, East Asia, Eu- rope, South Asia, America. Tables 1 to 12 present some characteristics information about coun- tries, regions, and respondents participating in this study. The following section provides information about the procedure followed to construct three scales in ICIPES 2020. E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 5 Table 1 Countries participating in ICIPES 2020. Operational Codes Regions Countries Alpha-3 Numeric Participants(n) Africa (AFR) Cameroon∗ CMR 31 10 381 Ethiopia ETH 57 171 Ghana GHA 65 142 Tanzania TAZ 172 58 East Asia (EAS) China CHN 36 217 376 Japan JPN 35 159 Europe (EUR) Belgium∗ BEL 16 5 819 Italy ITA 83 517 Spain∗ SPA 164 28 Turkey TUR 179 78 United Kingdom GBR 185 191 South Asia (SAS) India IND 77 54 298 Pakistan PAK 131 45 Sri Lanka LKA 165 199 America (AMR) Chile CHL 35 1597 2784 Colombia COL 37 94 Costa Rica CRI 40 155 El Salvador SLV 52 83 Honduras HND 74 246 Mexico MEX 110 244 Peru∗ PER 137 15 Uruguay URY 187 61 USA USA 186 289 N= 4658 4658 ∗ Concerns about the extremely low response rates (less than 10%) for the parents surveys led to a decision not to include the corresponding data in the international database. Table 2 Respondents by Country. Country Frequency Percentage Chile 1597 34.7 China 217 4.7 Colombia 94 2.0 Costa Rica 155 3.4 El Salvador 83 1.8 Ethiopia 171 3.7 Ghana 142 3.1 Honduras 246 5.3 India 54 1.2 Italy 517 11.2 Japan 159 3.5 Mexico 244 5.3 Pakistan 45 1.0 Sri Lanka 199 4.3 Tanzania&Zanzibar 58 1.3 Turkey 78 1.7 United Kingdom 191 4.2 The United States 289 6.3 Uruguay 61 1.3 Total 4600 100.0 6 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 Table 3 Respondents by Region. Region Frequency Percentage Africa 371 8.1 Europe 786 17.1 East Asia 376 8.2 South Asia 298 6.5 America 2769 60.2 Total 4600 100.0 Table 4 Respondents by Location. Location/Area Frequency Percentage Urban 3725 81 Rural 747 16.2 Others 128 2.8 Total 4600 100 Table 5 Respondents by Parent/Carer Gender. Gender Frequency Percentage Mother/Female Guardian 3527 76.67 Father/Male Guardian 1071 23.28 Missing 2 0.04 Total 4600 100 Table 6 Respondents by Parent/Carer years of schooling. Parent/Carer years of schooling Frequency Percentage 0 year 13 0.3 1 year 9 0.2 2 year 3 0.1 3 year 17 0.4 4 year 29 0.6 5 year 82 1.8 6 year 57 1.2 7 year 25 0.5 8 year 78 1.7 9 year 39 0.8 10 year 72 1.6 11 year 33 0.7 12 year 203 4.4 13 year 366 8.0 14 year 179 3.9 15 year 800 17.4 16 year 583 12.7 17 year 858 18.7 18 year 336 7.3 19 year 455 9.9 20 year 79 1.7 21 year 20 0.4 22 year 150 3.3 23 year 48 1.0 24 year 7 0.2 Prefer not to say 3 0.1 Missing 56 1.2 Total 4600 100.0 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 7 Table 7 Respondents by Parent/Carer Age. Parent/Carer Age Frequency Percentage Under 18 years old 32 0.7 18–24 years old 47 1.0 25–34 years old 740 16.1 35–44 years old 2232 48.5 45–54 years old 1329 28.9 55–64 years old 188 4.1 65–74 years old 30 0.7 75 years or older 2 0.0 Total 4600 100.0 Table 8 Respondents by Parent/Carer Main Occupation. Parent/Carer Main Occupation Frequency Percentage Unemployed, househusband, housewife 509 11.1 91 Elementary trades and related occupations /92 Elementary administration and 153 3.3 service occupations 41 Administrative occupations /42 Secretarial and related occupations /61 Caring 747 16.2 personal service occupations /62 Leisure, travel and related personal service occupations /63 Community and civil enforcement occupations¹/71 Sales occupations / 72 Customer service occupations / 81 Process, plant and machine operatives / 82 Transport and mobile machine drivers and operatives 12 Other managers and proprietors/ 31 Science, engineering and technology associate 569 12.4 professionals / 32 Health and social care associate professionals / Protective service occupations / 34 Culture, media and sports occupations / 35 Business and public service associate professionals / 51 Skilled agricultural and related trades /52 Skilled metal, electrical and electronic trades / 53 Skilled construction and building trades / 54 Textiles, printing and other skilled trades 11 Corporate managers and directors / 21 Science, research, engineering and 2520 54.8 technology professionals / 22 Health professionals / 23 Teaching and other educational professionals / 24 Business, media and public service professionals Missing 102 2.2 Total 4600 100.0 Table 9 Parent’s Child Gender. Child gender Frequency Percentage Female 2279 49.5 Male 2303 50.1 Other 18 0.4 Total 4600 100.0 Social cognitive learning theory [3] and the theory of acceptance and use of technology [14–16,1] formed the conceptual framework for these scales. The social cognitive learning the- ory provides a socially appropriate framework for understanding how parents learn to deal with technology at home from their observations and interactions with other parents, teachers and their children. The second explains the factors associated with parental acceptance and confi- dence in the use of technology. Before constructing the three scales, we constructed and implemented normalised weights (also known as senate weights) (SENWT in the dataset) to make sure that when constructing these three scales, all countries are represented equally regardless of their sample sizes. SENWT can also be used when analysing the pooled sample (all countries) to ensure the equal contri- bution of each country to the results. 8 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 Table 10 Parent’s Child Age. Child Age Frequency Percentage 6-year-old 691 15.0 7-year-old 470 10.2 8-year-old 464 10.1 9-year-old 392 8.5 10-year-old 448 9.7 11-year-old 388 8.4 12-year-old 402 8.7 13-year-old 307 6.7 14-year-old 303 6.6 15-year-old 264 5.7 16-year-old 411 8.9 Missing 60 1.3 Total 4600 100.0 Table 11 Parent’s child years of schooling. Child years of schooling Frequency Percentage Pre-school 237 5.2 1 479 10.4 2 516 11.2 3 458 10.0 4 414 9.0 5 464 10.1 6 365 7.9 7 417 9.1 8 352 7.7 9 273 5.9 10 251 5.5 11 178 3.9 12 50 1.1 13 18 0.4 14 1 0.0 Missing 127 2.8 Total 4600 100.0 Table 12 Children in the household. How many siblings living in the same household? Frequency Percentage 0 1482 32.2 1 1676 36.4 2 787 17.1 3 223 4.8 4 214 4.7 5 118 2.6 6 50 1.1 7 47 1.0 8 2 0.0 9 10 1 0.0 Total 4600 100.0 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 9 1.2. Variables 1.2.1. Parental engagement The parental engagement scale was constructed using the following questions: Q21_2, Q21_3, Q22_2, Q22_3, and Q22_6 from the data set. Always, Often, Occasionally, Rarely, Never (from 0 to 4) • Q21_2 Follow my ideas about what my children need to learn • Q21_3 Mix my own ideas with the school’s plan on what my children need to learn • Q22_2 I list and prepare the activities myself before developing them with my child(ren) • Q22_3 My children and I have a set home-schooling timetable. • Q22_6 I develop with my children spontaneous learning activities not necessarily school- related such as cooking, woodwork, online games, physical activities, etc. 1.2.2. Socioeconomic status (SES) Socioeconomic status (SES) was constructed using the following questions: Q5, Q7, Q13N, and Q14. Q5 What do you do in your main job? (e.g., teach high school students, help the cook pre- pare meals in a restaurant, manage a sales team). This was an open question that was recorded into an ordinal variable following the list of occupations described in the one-digit ISCO (Inter- national Standard Classification of Occupations). Q7 In a normal month, what is your total household income? This variable was recorded by grouping the income level reported in deciles of income within each country. Q13N is composed of How many usable devices are there in the house? (Smartphones, tablets or iPads, laptops, desktops). Q14 How many computers per child have you got at home? 1.2.3. Parental acceptance and confidence in the use of technology Parental engagement scale was constructed as a second-order construct, with constructs measuring the parents’ level of parental acceptance and confidence in the use of technology as ‘tools’, ‘for social purposes’ and ‘self- perceived capacity’. The items asked parents about the frequency with which they carry out different activities using technology (response options: Al- ways, Often, Occasionally, Rarely Never), and how confident they felt carrying out these activities (response options: Not at all confident, Slightly confident, Moderately confident, Quite confident, Extremely confident). Parental acceptance and confidence in the use of technology= tool + social + capacity. • tool=Q22_1 + Q24_1 + Q24_5; • social=Q21_4 + Q21_5 + Q21_6 + Q24_12; • capacity=Q24_2 + Q24_3 +Q24_4 + Q24_6 + Q24_7 + Q24_8 + Q24_9 + Q24_10+Q24_11+ Q21_7 1.3. Analytical strategy 1.3.1. Confirmatory factor analysis (CFA) Confirmatory Factor Analysis (CFA) was used to estimate the model for the three scales and for each country using maximum likelihood (ML). Missing data was handled with listwise dele- tion. Model fit was evaluated using the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI) as the goodness of fit statistics, and the root-mean-squared error of approximation (RM- SEA) and the standardized root mean squared residual (SRMR) as residual fit statistics. Accept- able model fit was guided by the cut-offs (CFI > 0.90; TLI > 0.90; RMSEA < 0.10; and SRMR < 0.08) as suggested by [8]. 10 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 Internal Consistency After constructing three scales, in order to evaluate reliability (internal consistency), we used Cronbach’s alpha coefficient [6]. Multi-Group Confirmatory Factor Analysis (MG-CFA) In order to evaluate the extent to which the scales can be validly compared across countries and geographical areas, we ran Multi-Group Confirmatory Factor Analysis (MG-CFA) first for the pooled sample including all participating countries, and later for countries within sub-groups (America, South Asia, East Asia, Africa and Europe) [10]. Here, we adopted the same strategy as [11] and [7] to conduct analysis and to interpret the results (for more information about procedure see these two papers [11] and [7]). 1.4. Important information for potential users The following tables include important information for potential users to be able to interpret the scales correctly. 1.4.1. Parental engagement scale Tables 13 and 14, Fig. 1 Table 13 Confirmatory Factor Analysis Model Fit for engagement scale for all countries. Fit statistics Chi-square df CFI TLI RMSEA SRMR Reliability Engagement(n = 4657) 508.122 5 0.898 0.796 0.147 0.056 0.7 Note. df = degree of freedom; CFI = Comparative Fit index; TLI = Tucker-Lewis index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. Table 14 Confirmatory factor analysis model for engagement scale for each country. Educational System Reliability CFI TLI RMSEA SRMR Degrees of freedom Test statistics n Ethiopia(57) 0.8 0.889 0.779 0.188 0.055 5 35.216 171 Ghana(65) 0.74 0.945 0.889 0.106 0.044 5 12.917 142 Tanzania(172) 0.79 1 1.087 0 0.026 5 2.068 58 China(36) 0.82 0.946 0.892 0.131 0.039 5 23.663 217 Japan(85) 0.7 0.905 0.809 0.135 0.057 5 19.563 159 Italy(83) 0.75 0.954 0.907 0.112 0.044 5 37.611 517 Turkey(179) 0.78 0.884 0.767 0.195 0.069 5 19.774 78 UK(185) 0.74 0.911 0.821 0.141 0.052 5 23.936 191 India(77) 0.71 1 1.183 0 0.031 5 2.02 53 Pakistan(131) 0.84 1 1.004 0 0.05 5 4.791 45 SriLanka(165) 0.8 0.948 0.895 0.129 0.037 5 21.491 199 Chile(35) 0.67 0.869 0.738 0.153 0.072 5 192.119 1597 Colombia(37) 0.5 0.935 0.871 0.073 0.057 5 7.529 94 Costarica(40) 0.69 0.892 0.783 0.142 0.065 5 20.521 155 ElSalvador(52) 0.73 0.852 0.704 0.218 0.098 5 24.72 83 Honduras(74) 0.68 0.707 0.414 0.244 0.113 5 78.059 246 Mexico(110) 0.63 0.762 0.524 0.227 0.101 5 67.954 244 Uruguay(187) 0.65 1 1.018 0 0.05 5 4.607 61 USA(186) 0.73 0.987 0.973 0.049 0.025 5 8.504 289 Note. df = degree of freedom; CFI = Comparative Fit index; TLI = Tucker-Lewis index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 11 Fig. 1. Measurement model for Parental Engagement. 1.4.2. MG-CFA result for parental engagement scale Tables 15–20 Table 15 Confirmatory Factor Analysis for all countries for engagement scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 581.5424 5 0.157354 0.058959 0.891463 0.782926 Configural invariance 607.0634 95 0.149226 0.06181 0.898439 0.796879 Metric invariance 1126.971 167 0.154105 0.106691 0.809603 0.783381 −0.08884 Scalar invariance 1986.75 239 0.173814 0.13809 0.653358 0.724427 −0.15625 Strict invariance 2365.486 329 0.159915 0.153451 0.596091 0.76674 −0.05727 Table 16 Confirmatory Factor Analysis for Africa for engagement scale. 371(4) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 20.17588 5 0.090449 0.029731 0.968512 0.937024 Configural invariance 50.20182 15 0.137756 0.04603 0.927239 0.854477 Metric invariance 55.42492 23 0.10677 0.057237 0.932978 0.91258 0.00574 Scalar invariance 67.30136 31 0.097309 0.065941 0.924966 0.927386 −0.00801 Strict invariance 96.38475 41 0.104515 0.085205 0.885521 0.916235 −0.03945 Table 17 Confirmatory Factor Analysis for Europe for engagement scale. 786(4) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 98.14603 5 0.153952 0.050065 0.910994 0.821987 Configural invariance 81.32066 15 0.129906 0.048239 0.936394 0.872788 Metric invariance 145.07 23 0.142328 0.075245 0.882927 0.847296 −0.053467279 Scalar invariance 197.5008 31 0.143178 0.091356 0.840315 0.845466 −0.042612133 Strict invariance 207.5371 41 0.124513 0.089347 0.84028 0.883132 12 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 Table 18 Confirmatory Factor Analysis for East Asia for engagement scale. 376(3) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 46.41285 5 0.148419 0.051417 0.917672 0.835344 Configural invariance 43.22629 10 0.132942 0.046728 0.933142 0.866284 Metric invariance 54.86896 14 0.12461 0.071612 0.917763 0.882519 −0.015378604 Scalar invariance 112.9605 18 0.167516 0.115977 0.80892 0.787689 −0.108843147 Strict invariance 148.875 23 0.170619 0.128463 0.746714 0.779751 −0.062206288 Table 19 Confirmatory Factor Analysis for south Asia for engagement scale. 279(3) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 27.89402 5 0.124165 0.037326 0.95517 0.910341 Configural invariance 28.30219 15 0.094645 0.037787 0.97069 0.941379 Metric invariance 54.36807 23 0.117371 0.084387 0.930883 0.909847 −0.039806715 Scalar invariance 86.92192 31 0.134987 0.098635 0.876781 0.880755 −0.054102414 Strict invariance 114.4042 41 0.134478 0.102205 0.83826 0.881653 −0.038520836 Table 20 Confirmatory Factor Analysis for America for engagement scale. 2769(9) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 359.2043 5 0.159949 0.071789 0.861349 0.722699 Configural invariance 404.0125 40 0.162148 0.072401 0.858066 0.716132 Metric invariance 496.4607 68 0.134923 0.087906 0.832937 0.803455 −0.025129336 Scalar invariance 670.0427 96 0.131438 0.09858 0.776172 0.813477 −0.056764687 Strict invariance 749.5524 131 0.116798 0.10514 0.758817 0.852713 −0.017355009 1.4.3. Socioeconomic status scale Tables 21 and 22, Fig. 2 Table 21 Confirmatory Factor Analysis Model Fit for SES scale for all countries. Fit statistics Chi-square df CFI TLI RMSEA SRMR Reliability SES(n = 4136) 19.388 2 0.992 0.977 0.046 0.015 0.62 Note. df = degree of freedom; CFI = Comparative Fit index; TLI = Tucker-Lewis index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. Table 22 Confirmatory factor analysis model for SES scale for each country. Educational system Reliability CFI TLI RMSEA SRMR Degrees of freedom Test statistics n Ethiopia(57) 0.5 1 1.055 0 0.013 2 0.443 169 Ghana(65) 0.44 0.979 0.938 0.059 0.04 2 2.751 108 Tanzania(172) 0.51 0.771 0.312 0.181 0.068 2 5.423 52 China(36) 0.46 0.812 0.435 0.154 0.054 2 9.834 166 (continued on next page) E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 13 Table 22 (continued) Educational system Reliability CFI TLI RMSEA SRMR Degrees of freedom Test statistics n Japan(85) 0.46 0.862 0.586 0.139 0.056 2 7.617 145 Italy(83) 0.61 0.949 0.848 0.107 0.035 2 12.271 450 Turkey(179) 0.55 1 1.012 0 0.042 2 1.891 78 UK(185) 0.5 0.942 0.827 0.104 0.044 2 5.24 158 India(77) 0.61 0.98 0.939 0.069 0.049 2 2.509 54 Pakistan(131) 0.55 0.87 0.61 0.205 0.09 2 5.037 36 SriLanka(165) 0.69 0.997 0.991 0.029 0.021 2 2.33 199 Chile(35) 0.65 0.839 0.518 0.224 0.072 2 162.338 1597 Colombia(37) 0.7 0.934 0.803 0.18 0.051 2 7.482 85 Costarica(40) 0.81 0.995 0.984 0.06 0.02 2 3.036 143 Elsalvador(52) 0.75 1 1.085 0 0.006 2 0.075 71 Honduras(74) 0.57 0.99 0.969 0.047 0.025 2 2.981 223 Mexico(110) 0.74 0.987 0.96 0.082 0.024 2 4.787 206 Uruguay(187) 0.59 0.992 0.975 0.047 0.046 2 2.254 58 USA(186) Note. df = degree of freedom; CFI = Comparative Fit index; TLI = Tucker-Lewis index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. Fig. 2. Measurement model for Socioeconomic status. 1.4.4. MG-CFA result for socioeconomic status scale Tables 23–28 Table 23 Confirmatory Factor Analysis for all countries for SES scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 19.38766 2 0.045847 0.015055 0.992326 0.976977 Configural invariance 1233.791 308 0.125733 0.065177 0.827353 0.74103 Metric invariance 1747.675 434 0.126173 0.096277 0.755019 0.739214 −0.07233 Scalar invariance 5079.804 560 0.206031 0.281879 0.157122 0.304626 −0.5979 Strict invariance 7739.431 707 0.228723 0.401474 0 0.143015 −0.15712 14 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 Table 24 Confirmatory Factor Analysis for Africa countries for SES scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 1.754282 2 0 0.014459 1 1.005694 Configural invariance 8.616476 6 0.063059 0.030384 0.980809 0.942426 Metric invariance 13.11633 12 0.029125 0.044798 0.991812 0.987718 0.011003 Scalar invariance 62.44294 18 0.150047 0.114559 0.674022 0.674022 −0.31779 Strict invariance 90.97313 26 0.150953 0.172059 0.523439 0.670073 −0.15058 Table 25 Confirmatory Factor Analysis for Europe countries for SES scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 55.70191 2 0.202624 0.059631 0.802704 0.408112 Configural invariance 198.7235 56 0.122532 0.060901 0.805383 0.708075 Metric invariance 256.2546 74 0.120453 0.080512 0.751479 0.717895 −0.0539 Scalar invariance 339.5859 92 0.125911 0.095679 0.662394 0.691751 −0.08909 Strict invariance 496.084 113 0.14132 0.133366 0.477629 0.611689 −0.18476 Table 26 Confirmatory Factor Analysis for East Asia countries for SES scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 98.42404 14 0.139248 0.084757 0.654562 0.481844 Configural invariance 17.45187 4 0.147061 0.054767 0.83665 0.50995 Metric invariance 20.10864 7 0.10974 0.061244 0.840818 0.727117 0.004168 Scalar invariance 67.61635 10 0.19249 0.12439 0.300348 0.160417 −0.54047 Strict invariance 76.321 14 0.169195 0.142274 0.243218 0.35133 −0.05713 Table 27 Confirmatory Factor Analysis for South Asia countries for SES scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 2.259465 2 0.023596 0.021439 0.997472 0.992416 Configural invariance 8.716322 6 0.076348 0.033266 0.979696 0.939088 Metric invariance 25.51962 12 0.120441 0.070277 0.898943 0.848415 −0.08075 Scalar invariance 52.17098 18 0.156342 0.100213 0.744578 0.744578 −0.15437 Strict invariance 115.2477 26 0.21023 0.220021 0.332889 0.538154 −0.41169 Table 28 Confirmatory Factor Analysis for America countries for SES scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 55.52348 2 0.101279 0.030157 0.963561 0.890683 Configural invariance 184.13 16 0.179503 0.050682 0.898509 0.695528 Metric invariance 277.5462 37 0.141191 0.074167 0.854796 0.811627 −0.04371 Scalar invariance 883.7312 58 0.208936 0.139707 0.501553 0.587492 −0.35324 Strict invariance 2221.39 86 0.275929 0.295485 0 0.28055 −0.50155 1.4.5. Acceptance and confidence scale Tables 29 and 30, Fig. 3 Table 29 Confirmatory Factor Analysis Model Fit for acceptance and confidence scale for all countries. Fit statistics Chi-square df CFI TLI RMSEA SRMR reliability acceptance(n = 4642) 0 0 1 1 0 0 0.78 Note. df = degree of freedom; CFI = Comparative Fit index; TLI = Tucker-Lewis index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 15 Table 30 Standardized factor loadings and intercepts for acceptance and confidence scale for each country. Factor loadings Intercepts Educational system Reliability Tool Social Capacity Tool Social Capacity n Ethiopia(57) 0.7 0.95 0.339 0.759 2.085 2.588 2.063 171 Ghana(65) 0.57 1.932 0.142 0.311 1.603 2.174 1.775 142 Tanzania(172) 0.69 0.761 0.342 0.916 1.822 2.134 2.373 58 China(36) 0.76 0.789 0.494 0.904 3.104 2.637 2.712 217 Japan(85) 0.74 0.701 0.505 0.91 2.651 4.798 2.456 159 Italy(83) 0.77 0.875 0.58 0.744 3.081 3.948 3.006 517 Turkey(179) 0.79 0.874 0.555 0.827 2.431 1.993 2.059 78 UK(185) 0.78 0.898 0.617 0.719 3.581 3.519 3.52 191 India(77) 0.84 0.928 0.681 0.839 2.173 1.907 2.28 48 Pakistan(131) 0.8 0.714 0.753 0.894 1.827 1.513 1.431 45 SriLanka(165) 0.81 0.921 0.542 0.851 2.148 2.285 2.129 199 Chile(35) 0.74 0.857 0.513 0.737 3.554 3.576 3.301 1597 Colombia(37) 0.73 0.98 0.424 0.711 3.032 3.628 2.811 94 Costarica(40) 0.77 0.965 0.517 0.721 2.785 3.118 2.622 155 Elsalvador(52) 0.76 0.793 0.561 0.807 3.599 3.053 3.551 83 Honduras(74) 0.69 0.734 0.465 0.773 3.245 3.429 2.79 246 Mexico(110) 0.82 0.851 0.614 0.891 2.573 3.002 2.725 244 Uruguay(187) 0.66 0.854 0.391 0.682 4.068 4.699 3.232 61 USA(186) 0.75 0.966 0.482 0.723 2.882 2.509 3.225 289 Fig. 3. Measurement model for acceptance and confidence scale. 16 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 1.4.6. MG-CFA result for acceptance and confidence scale Tables 31–36 Table 31 Confirmatory Factor Analysis for all countries for acceptance scale. Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 0 0 0 0 1 1 Configural invariance 0 0 0 0 1 1 Metric invariance 85.40701865 36 0.075422 0.038111542 0.987113 0.979595 −0.01289 Scalar invariance 644.5433347 72 0.181548 0.096383494 0.850658 0.881771 −0.13645 Strict invariance 899.9196701 126 0.159557 0.123705734 0.798131 0.908678 −0.05253 Table 32 Confirmatory Factor Analysis for Africa for acceptance and confidence scale. 371(3) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 0 0 0 0 1 1 Configural invariance 0 0 0 0 1 1 Metric invariance 6.500133592 4 0.071093 0.036222553 0.990338 0.97826 −0.00966 Scalar invariance 28.55747519 8 0.14415 0.077308907 0.92055 0.910619 −0.06979 Strict invariance 39.78122672 14 0.122029 0.095684283 0.900362 0.935947 −0.02019 Table 33 Confirmatory Factor Analysis for Europe for acceptance and confidence scale. 786(3) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 0 0 0 0 1 1 Configural invariance 0 0 0 0 1 1 Metric invariance 4.743893007 4 0.026642 0.020968118 0.998937 0.997609 −0.00106 Scalar invariance 92.85405382 8 0.201206 0.088004477 0.878763 0.863608 −0.12017 Strict invariance 142.4050703 14 0.187101 0.120830664 0.816538 0.88206 −0.06222 Table 34 Confirmatory Factor Analysis for East Asia for acceptance and confidence scale. 376(3) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 0 0 0 0 1 1 Configural invariance 0 0 0 0 1 1 Metric invariance 2.552421949 2 0.03833 0.034734358 0.998282 0.994846 −0.00172 Scalar invariance 98.42126974 4 0.354345 0.172117284 0.706356 0.559535 −0.29193 Strict invariance 124.9045583 7 0.299321 0.233652999 0.633325 0.685707 −0.07303 Table 35 Confirmatory Factor Analysis for south Asia for acceptance and confidence scale. 279(3) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 0 0 0 0 1 1 Configural invariance 0 0 0 0 1 1 Metric invariance 6.521818998 4 0.081896 0.047729416 0.992846 0.983904 −0.00715 Scalar invariance 34.89431204 8 0.189113 0.081810798 0.923706 0.914169 −0.06914 Strict invariance 52.57001258 14 0.171197 0.081061567 0.890584 0.929661 −0.03312 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 17 Table 36 Confirmatory Factor Analysis for America for acceptance and confidence scale. 2769(9) Model Chi-Square df RMSEA SRMR CFI TLI Change (CFI) All groups 0 0 0 0 1 1 Configural invariance 0 0 0 0 1 1 Metric invariance 18.70874018 14 0.031173 0.019873832 0.997861 0.996333 −0.00214 Scalar invariance 191.561156 28 0.129911 0.056235716 0.92569 0.936306 −0.07217 Strict invariance 242.2002961 49 0.106731 0.067470981 0.912224 0.957008 −0.01347 2. Experimental Design, Materials and Design The researchers employed an online survey research design to gather data from 2658 re- spondents from 23 countries all over the world. All countries are Cameroon, Ethiopia, Ghana, Tanzania Zanzibar, China (Mainland, Hong Kong and Macao), Japan, Belgium, Italy, Spain, Turkey, United Kingdom, India, Pakistan, Sri Lanka, Chile, Colombia, Costa Rica, El Salvador, Hon- duras, Mexico, Peru, Uruguay and the United States. The countries then divided into five re- gions which are Africa, East Asia, Europe, South Asia and America. Data were obtained us- ing a semi-structured questionnaire (Appendix). The questionnaire consists of several sections. Section 1 and 2 gathered information about the parents and their child. Section 3 gathered in- formation about the children’s school and their access to the internet. Section 4 gathered infor- mation about the COVID 19 impact in terms of parents’ new role at home. Section 5 gathered information about teaching ideas and practices in terms of home-schooling. The first part is a descriptive analysis of all the items included in the survey and was performed using tables ( see, descriptive part, Tables 1 to 12). The second part refers to the construction of scales (see vari- ables part). Three scales were constructed and included in the dataset: ‘parental acceptance and confidence in the use of technology’, ‘parental engagement in children’s learning’ and ‘socioeco- nomic status’. The scales were created using Confirmatory Factor Analysis (CFA) and Multi-Group Confirmatory Analysis (MG-CFA) was adopted to evaluate their cross-cultural comparability (i.e., measurement invariance) across countries and within sub-groups. All analyses are executed in the R statistical software (R Core Team, 2019), installing lavaan and lavaan.survey packages de- veloped by Rosseel (2012) and Oberski (2014), respectively. Ethics Statement Informed consent was obtained from all individual participants included in the data collec- tion process. The research ethics committee of the University of Bath provided ethical approval EIRA1–5408. CRediT Author Statement Eliana Maria Osorio-Saez and Andres Sandoval-Hernandez: Conceptualization and Method- ology; Nurullah Eryilmaz: Data curation and Data Analysis; Nurullah Eryilmaz and Eliana Maria Osorio-Saez: Writing- Original draft preparation; Andres Sandoval-Hernandez: Supervi- sion; Yui-yip Lau: Reviewing and Editing; Eliana Maria Osorio-Saez, Nurullah Eryilmaz, Andres Sandoval-Hernandez, Yui-yip Lau, Elma Barahona, Adil Anwar Bhatti, Godfried Ofoe Caesar, Leví Astul Castro Ordóñez, Artemio Arturo Cortez Ochoa, Rafael Ángel Espinoza Pizarro, Es- ther Fonseca Aguilar, Maria Magdalena Isac, K.V. Dhanapala, Kalyan Kumar Kameshwara, Ysrael Alberto Martínez Contreras, Geberew Tulu, José Fernando Mejía, Catalina Miranda, Shehe Abdalla Moh’d, Ricardo Morales Ulloa, K. Kayon Morgan, T. Lee Morgan, Sara Mori, 18 E.M. Osorio-Saez, N. Eryilmaz and A. Sandoval-Hernandez et al. / Data in Brief 35 (2021) 106813 Forti Ebenezah Nde, Silvia Panzavolta, Lluís Parcerisa, Carla Leticia Paz, Oscar Picardo, Car- olina Piñeros, Pablo Rivera-Vargas, Alessia Rosa, Lina Maria Saldarriaga, Adrián Silveira Aberastury, YM Tang, Kyoko Taniguchi, Ernesto Treviño, Carolina Valladares Celis, Cristóbal Villalobos, Dan Zhao and Allison Zionts: Data Collection and survey translation and adapta- tion. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal rela- tionships that could have appeared to influence the work reported in this paper. Acknowledgements The authors would like to thank the participants who kindly took part in this study, as well as the Higher Education Institutions and other organizations involved. Also, all the contributors and partners in the 23 countries for their valuable input into the overall study. 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