Results of analysis on quantitative data, descriptive statistics, and group comparisons.

Kin selection and multilevel selection theory are often used to interpret experiments about the evolution of cooperation and social behaviour among microbes. But while these experiments provide rich, detailed fitness data, theory is mostly used as a conceptual heuristic. Here, evaluate how kin and multilevel selection theory perform as quantitative analysis tools. We reanalyse published microbial datasets and show that the canonical fitness models of both theories are almost always poor fits because they use statistical regressions misspecified for the strong selection and non- additive effects we show are widespread in microbial systems. We identify analytical practices in empirical research that suggest how theory might be improved and show that analysing both individual and group fitness outcomes helps clarify the biology of selection. A data-driven approach to theory thus shows how kin and multilevel selection both have untapped potential as tools for quantitative understanding of s...

Four focus groups of 15 individuals each were conducted in greater London and Birmingham in adjacent locales, one diverse, one more homogeneous. Locations were Croydon and Bromley in Greater London, and Lozells and Sutton Coldfield in Greater Birmingham. Participants were paid £30 apiece for their time and recruited by a Recruitment company.

Respondents were asked about perceptions of immigration and residential choice. We explored the 'halo' effect among those in whiter areas living in proximity to diversity, and the 'contact' effect of whites living with minorities in diverse areas. The former is theorised to increase threat perceptions of diversity, the latter to mitigate them.

Questions also explored ethnically motivated 'white flight' or whether social ties and amenities account for ethnic sorting. The link between immigration and issues of fairness, housing, services and employment was also broached.

Locations and dates:

3rd April, East Croydon United Reform Church, 6-7.30pm (diverse area) 8th April, Hayes Village Hall, Bromley, 6-7.30pm (White area)

9th April, Trinity Centre, Sutton Coldfield. 6-7.30pm (White area) 10th April, Lozells Methodist Community Centre, Birmingham, 6-7.30pm (diverse area)

This project advances the hypothesis that ethnic change in England and Wales is associated with white working-class ‘exit,’ ‘voice’, or ‘accommodation’. ‘Voice’ is manifested as a rise in ethnic nationalist voting and anti-immigration sentiment and ‘exit’ as outmigration from, or avoidance of, diverse locales. Once areas reach a threshold of minority population share, however, these initial responses may give way to ‘accommodation’ in the form of decreased ethno-nationalist voting, reduced anti-immigration sentiment and lower white outmigration. In the course of our investigation, we ask the policy-relevant question: do residential integration and minority acculturation calm or fuel white working-class exit and voice? In other words, does contact improve ethnic relations or do ‘good fences make good neighbours’? This research adds to existing scholarship by integrating individual data with a more complex array of contextual variables, blending quantitative methods with focus-group qualitative research.

Data is becoming increasingly ubiquitous today, and data literacy has emerged an essential skill in the workplace. Therefore, it is necessary to equip high school students with data literacy skills in order to prepare them for further learning and future employment. In Indonesia, there is a growing shift towards integrating data literacy in the high school curriculum. As part of a pilot intervention project, academics from two leading Universities organised data literacy boot camps for high school students across various cities in Indonesia. The boot camps aimed at increasing participants’ awareness of the power of analytical and exploration skills, which in turn, would contribute to creating independent and data-literate students. This paper explores student participants’ self-perception of their data literacy as a result of the skills acquired from the boot camps. Qualitative and quantitative data were collected through student surveys and a focus group discussion, and were used to analyse student perception post-intervention. The findings indicate that students became more aware of the usefulness of data literacy and its application in future studies and work after participating in the boot camp. Of the materials delivered at the boot camps, students found the greatest benefit in learning basic statistical concepts and applying them through the use of Microsoft Excel as a tool for basic data analysis. These findings provide valuable policy recommendations that educators and policymakers can use as guidelines for effective data literacy teaching in high schools.

The dataset has three parts; quantitative data, transcripts of Online FGDs and Photovoice Group Discussions, and Photovoice Photographs. Quantitative data includes the outcome variable which consists of nine measures: 1) maintaining a 1-meter distance, 2) avoiding handshakes, 3) avoiding hugs, 4) avoiding public transportation, 5) working/studying from home, 6) avoiding gatherings and crowds, 7) postponing meetings, 8) avoiding visiting elderly people, and 9) praying at home. In addition, other variables in this data set are sociodemographic characteristics; COVID-19-related variables such as COVID-19 testing, knowledge of COVID-19, etc.; and religious and tradition-related activities such as breaking fast during Ramadan, joining Mudik tradition, etc. Qualitative data includes Online FGDs and Photovoice Group Discussions transcripts and Photovoice Photographs. Five Online FGDs transcripts and 10 transcripts for Photovoice. 29 Photographs of Photovoice are also available in a list.

Abstract

This study examines the effect of the use of two Open Educational Resources (OER) (a Khan Academy online tutorial and an open textbook hosted on Wikibooks) on logical-mathematical outcomes for first and second-year students in higher education institutions in Chile. It also investigates perceptions of instructors and students about the use of OER, in order to understand how these resources are used and valued. Quantitative and qualitative methods were used to collect student performance data via a student survey, student focus groups, interviews with instructors, and sourcing institutional records.

Only the institutional records, focus group data and interview data are included in the final dataset. Student survey data is not made available for confidentiality reasons. Findings indicate that students in a contact-study mathematics course who used a Khan Academy online mathematics tutorial obtained better examination results than students who did not use any additional resources, or those who used the open textbook. Moreover, it was also found that instructors and students have positive perceptions about the use of Khan Academy and Wikibooks materials.This study is Sub-project 9 of the Research on Open Educational Resources for Development (ROER4D) project, hosted by the Centre for Innovation in Learning and Teaching (CILT) at the University of Cape Town, South Africa, and Wawasan Open University, Malaysia.

Geographic coverage

The interviews and survey data were conducted at one institution in Chile and are not representative of the country as a whole.

Analysis unit

Individuals

Universe

The survey covered students and instructors in the single institution involved in the study.

Kind of data

Focus group and survey data

Mode of data collection

Face-to-face and internet [f2f-int]

Abstract

The evaluation considers a range of outcomes of the ADA program, including production and profitability, investment and technology adoption, employment and wages, and access to credit and markets. Though it was originally designed as a rigorous impact evaluation that incorporated a randomized design, the evaluation was not able to undertake a rigorous statistical analysis of the program on these outcomes for a number of reasons, including the small overall size of the program, changes during implementation that compromised the original evaluation design, and the timing of the evaluation. Instead, the evaluation uses a mixed methods approach combining qualitative data with descriptive quantitative analysis to assess the impact of the project.

Qualitative data collection included focus group discussions and in-depth interviews that collected detailed information from a total of 69 respondents. Respondents were recruited from among those who responded to the ADA survey and were grouped together by type of grantee (PP, VA/VCI, and FSC as separate groups) and by characteristics of interest based on responses to the ADA survey (those that reported an increase in income, those that didn't respond to income questions, those that closed their businesses, exporters, and machinery ring grantees).These interviews and focus groups were transcribed and analyzed using the specialized software package NVivo to systematically categorize responses and identify commonalities. Themes of interest to the evaluation were identified and then coded in all of the transcriptions. Summaries of responses by code and respondent type were completed and interesting cases were highlighted, providing some concrete examples of project results and/or feedback that also served in helping interpret the quantitative data.

Geographic coverage

The program was implemented nationally.

Analysis unit

Small, medium and large agribusinesses (MCG grantees and non-grantees)

Universe

Applicants to the ADA program from all application rounds (9 in total) in Georgia.

Kind of data

Sample survey data [ssd]

Sampling procedure

Round 1: The frame for the survey is the list of all applicants. It was supplied by CNFA, the program implementer, along with the scores from the initial evaluation, various statuses assigned by CNFA, and various items of information taken from the applications.

Each of the four applicant types were considered as separate strata, that is, primary producers (PPs), farm service centers (FSCs), value adders (VAs) and value chain enterprises (VCHs).

For PPs, one comparison case was selected for each new treatment case. A propensity score matching (PSM) methodology was used to select the comparison cases, using binary logistic regression. The dependent variable was the event of being a treatment case. The independent variables, all available from data supplied by CNFA on the frame, were: * the amount of matching contribution the applicant proposed to make * the current turnover of the business when it made its application * the number of employees of the business when it made its application * whether the business was able to secure credit * the year in which the business was established * whether the business was located in a village or larger town * the type of activity the business was proposing to be engaged in * the round in which the applicant applied

For each PP treatment case, the comparison case with the closest PSM score was selected for inclusion in the survey sample, as long as it had not been selected for interview previously.

For the other applicant types (FSCs, VAs and VCHs), stratified random sampling was used to select comparison cases. Because the populations were relatively small, two comparison cases were selected for each treatment case. Selection of comparison cases was to be made within the same strata in which the treatment cases occurred. The strata were defined in terms of the current turnover of the business when it made its application and the year in which the business was established. Type of activity was also used to define the strata for VAs and VCHs.

Round 2: The following sampling rules were applied: 1. Include all businesses that had been interviewed in Round 1 from ADA application waves 1 to 7. a) Interviewees from ADA application waves 8 and 9 were excluded because those interviews had been conducted too recently to expect significant change to have taken place in the meantime. b) Selections were made in terms of "businesses" rather than "applications" because some businesses had applied several times. Where a selected business had made multiple applications, the most recent application was nominally selected for inclusion in the survey, regardless of whether that application or an earlier one was the basis of interview in ADA application waves 1 to 7. The most recent one was chosen because it would have the most up-to-date contact information. c) 199 applications were selected on this basis.

1. Include treatments from any ADA application wave that had not yet been interviewed in Round 1. Some of these were previously non-response and some appeared to have wrongly claimed to have been previously interviewed on the basis of another application. 29 applications were selected on this basis.

2. Include applicants that scored 70+ (passing score) in ADA application waves 1-7, that have not yet been interviewed, but that are not previous nonresponse. Most appear to have wrongly claimed to have been previously interviewed on the basis of another application. 8 applications were selected on this basis.

3. PPs and VAs were not fully enumerated in Round 1, and the process used to randomly select applicants with a score less than 70 has not enabled the probability of selection to be derived. Therefore, for Round 2, select a random sample of 100 PPs and 25 VAs applications, where (i) neither they nor any related application was interviewed in ADA application waves 8 or 9, and (ii) neither they nor any related application received a score of 70+. If the selected application has not already been selected under condition 1 above, include in the Round 2 Survey. a) 78 PP applications were selected on this basis, that is, 22 of the 100 were already selected under condition 1 above. b) 18 VA applications were initially selected on this basis, that is, 7 of the 25 were already selected under condition 1 above.

However, as there were only 20 eligible VAs to be chosen under this condition, all 20 were included and so the VAs became fully enumerated.

In total there were 334 applications selected for inclusion in the survey.

The frame and summary information about the selections are included in the external resource "Followup frame and selections.xlsx".

Round 3: The sample frame was created by NORC and included all cases that were part of the sample in Round 1 and all the cases that were part of the sample in Round 2. The sample comprised of treatment and control groups with three main types of businesses in each group. Overall 600 face-to-face interviews were planned to be conducted for Round 3. This sample frame was then put through a re-listing exercise to update it since the list of business status and contact information included many incorrect telephone numbers and addresses, there was turnover in owners/managers of agribusinesses, and some had shut down.

For the relisting exercise, ACT first tried calling the phone numbers, then conducted field visits to the listed addresses. If still unable to locate the business, ACT regional coordinators contacted local authorities/representatives. Upon contacting the business, updated information about the business status, location, and contact information was collected for use during the main data collection. This updated list was the sample used for data collection.

Sampling deviation

Round 1 It should be noted that the model for PPs was re-estimated many times and some comparison cases were selected on the basis of the PSM scores generated in each of those runs. First of all, it had to be re-estimated for each wave of the survey, as new applicants appeared in the frame and new treatment cases were chosen by CNFA. Secondly, many applicants did not have values for all the independent variables, and therefore the model was re-estimated a number of times with varying reduced sets of independent variables.

In practice, the strata were defined with too much detail and comparison cases often could not be found in the same strata as treatment cases. Therefore strata had to be combined. This was done in an ad hoc way, with the result that the probability of selection is not available and corresponding sampling weights cannot be calculated.

By wave 4, it was also found that the pool of comparison cases was so small for FSCs and VCHs that all cases had to be included in the sample, that is, these categories are fully enumerated. This then applies to wave 5 also.

Selection of comparison cases was on a quota basis, that is, there was substitution for non-responding selections and for selections that no longer existed as separate entities. This occurred because some green-field proposals never commenced operations, because some businesses ceased operations, and because some businesses merged with or had always operated jointly with other applicants that had already been interviewed.

Round 2 During the course of the survey, two notable changes were made to the frame. First, it was discovered that one applicant had not been included in the CNFA Masterlist. This was a VCI applicant and it was therefore added to the survey. Second, it was discovered during interview (and subsequently confirmed) that applicant #318 should have been

Detection of differentially abundant proteins in label-free quantitative shotgun liquid chromatography–tandem mass spectrometry (LC–MS/MS) experiments requires a series of computational steps that identify and quantify LC–MS features. It also requires statistical analyses that distinguish systematic changes in abundance between conditions from artifacts of biological and technical variation. The 2015 study of the Proteome Informatics Research Group (iPRG) of the Association of Biomolecular Resource Facilities (ABRF) aimed to evaluate the effects of the statistical analysis on the accuracy of the results. The study used LC–tandem mass spectra acquired from a controlled mixture, and made the data available to anonymous volunteer participants. The participants used methods of their choice to detect differentially abundant proteins, estimate the associated fold changes, and characterize the uncertainty of the results. The study found that multiple strategies (including the use of spectral counts versus peak intensities, and various software tools) could lead to accurate results, and that the performance was primarily determined by the analysts’ expertise. This manuscript summarizes the outcome of the study, and provides representative examples of good computational and statistical practice. The data set generated as part of this study is publicly available.

A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes–Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity.

Abstract

In collaboration with the Gambia National Nutrition Agency and the Ministry of Health and Social Welfare, a research team from the World Bank, University of Southern California and Harvard University has developed an evaluation and roll-out design which allows rigorous assessment of the impact of the results-based financing interventions on health related outcomes.

The key objective of the impact evaluation is to assess the effectiveness and cost-effectiveness of the package of supply and demand side interventions developed for, and implemented at health facilities and communities as part of the Gambia Maternal and Child Nutrition and Health Results Project. This project is implemented in three regions with some of the poorest performing indicators - Upper River, Central River and North Bank West Regions. Over the five-year period of implementation, the project is expected to reach approximately 183,000 children under five and 180,000 women aged 15-49 years, yielding a total of 363,000 direct beneficiaries of the project. The interventions will provide support through RBF arrangements with women, Village Development Committees (VDC) and Village Support Groups (VSG), and primary health facilities.

The primary research questions are grouped according to three broad categories: effect on nutrition and health outcomes, effect on service utilization and adoption of behaviors, and effect on intermediate outcomes along pathways of impact. The evaluation uses a mixed-methods explanatory design with an embedded process evaluation based on a conceptual framework that details out the pathways of impact for both interventions.

The overall approach for the impact evaluation is a randomized phased-in 2 x 2 design. The plan for the supply-side is that facilities in the three target regions are enrolled in the project in two phases, each lasting 18 months which should provide a sufficiently long time window to allow behavioral changes. In addition to the supply-side interventions, some communities in the target regions will be enrolled in a community-based demand-side component, for which each phase will last 12 months.

To measure the community-level impact of the project, three main surveys are to be conducted. A baseline survey was administered at the beginning of the project in October 2014 - February 2015. A midline survey is planned to be conducted approximately 18 months after the project launch, and an endline survey - after approximately 36 months of the project start.

The quantitative and qualitative data from the baseline survey is documented here. This assessment prepared a baseline against which the impact of the project can subsequently be measured. The quantitative baseline data was collected through household, health facility and village questionnaires. Qualitative baseline data was collected using focus group discussions and in-depth interviews.

Geographic coverage

Upper River, Central River and North Bank West Regions

Analysis unit

• households,
• health facilities,
• villages

Kind of data

Sample survey data [ssd]

Sampling procedure

A household survey targeted a random sample of women 15 and older in communities in 2259 households, and a facility survey directly targeted all 24 health facilities in the area.

For the household survey, two-stage cluster sampling was used to identify a random sample of approximately 100 households with at least one woman of age 15 or older and at least one child under the age of five from the catchment areas of each facility. To identify these women, researchers first randomly selected five enumeration areas from the catchment areas of each of the 24 facilities using probability proportional to population size (based on the latest census estimates); in all selected enumeration areas, a household listing was conducted. From all eligible households listed, 20 households were selected for the survey.

The resulting sample is not representative at the national or regional level for three main reasons: first, geographically, the project covered only 3 regions in the country, which are on average less developed than the regions not included. Second, within regions, surveys were only conducted in communities with existing health platforms. These communities are on average slightly larger and likely also more developed than communities without such platforms. Last, within communities, the survey targeted only women with recent births, which are not representative of the larger adult female population.

Overall, surveys were administered to the officer in charge of each of the 24 health facilities and a total of 94 health workers working in maternal and child health services. Exit interviews were administered to 150 women attending ANC services and 160 caregivers of children aged under 5 attending out-patient services.

The community-based survey was administered to 109 VDC (Village Development Committee) members and 108 VSG (Village Support Group) members.

Mode of data collection

Face-to-face [f2f]

Research instrument

A mix of quantitative and qualitative methods were employed for the baseline survey. The quantitative part of the evaluation relied on three main sources of data, while the qualitative part of the study used both focus group discussions and key informant interviews with a wide range of stakeholders to elicit their perspectives on different issues relevant to maternal and child nutrition and health.

Quantitative assessment

1) Household surveys: 2,257 households, within which questionnaires were administered to two people 2) Facility-based surveys: 24 health facilities in the study area, within which questionnaires were administered to the head of the health facility, health workers and women attending Maternal and Child Health (MCH) services. 3) Community-based surveys (Village Development Committees and Village Support Groups): 109 communities (approximately five for each facility), within which questionnaires were administered to members of the committees.

Qualitative assessment

1) Focus group discussions: 27 focus group discussions of approximately 5-8 participants each. 2) Key informant interviews: 20 interviews.

This dataset contains qualitative and quantitative data from the doctoral dissertation “Learning the language of instruction in monolingual countries: A mixed methods comparative study on newly arrived migrant students in Turkey and Germany”.

It is available for reuse and reanalysis. Researchers are encouraged to explore the dataset to pose new questions and conduct further analyses from different perspectives.

The study investigated organization of destination language support for newly arrived migrant students in monolingual school contexts and explored contextual factors determining their language proficiency. Istanbul (IST) and Hamburg (HAM) were illustrative cases. Drawing on Bronfenbrenner’s ecological theory, the study focused on students in lower-secondary education through a four-phase mixed methods convergent comparative design.

Qualitative Data

The qualitative data includes:

• Interviews with students (n = 22 IST, n = 6 HAM), teachers (n = 15 IST, n = 6 HAM), school administrators (n = 10 IST), parents (n = 6 IST, n = 3 HAM), and key informants (n = 2 IST, n = 7 HAM).
• Classroom observation notes from the language preparatory classes, which covers 21 hours in Istanbul and 12 hours in Hamburg.

Interview languages include Turkish, German, and English, depending on the participant group. Classroom observation notes are in Turkish.

Quantitative Data

The quantitative dataset consists of:

• Survey data from 245 Syrian refugee students in Istanbul and 189 newly arrived migrant students (mixed-migrant group) in Hamburg
• Variables on:
  • Destination language proficiency (self-assessed Turkish/German skills)
  • Migration-related individual characteristics (e.g., age at migration, length of stay, prior schooling, first language proficiency)
  • Family environment (e.g., family language proficiency in Turkish/German, family involvement in education)
  • Classroom learning environment
• Descriptive information (e.g., gender, age, district)

Dataset Files

The dataset includes:

• Raw qualitative and quantitative data files
• Interview schedules and classroom observation protocols
• Participant characteristics for interviews
• Documentation of quantitative variables

Access and Further Information

For detailed information on data collection, validation, and research design, please refer to the Method chapter of the open-access dissertation available in the Middle East Technical University Repository.

This space will be regularly updated with relevant publications based on this dataset.

This data includes focus group protocols including scenarios and discussion questions, survey instruments, and focus group transcripts and survey data related to the “Improving Delivery of Weather Prediction Center Precipitation Products” project. This project was funded by a $1,211,082 grant from the National Oceanic and Atmospheric Administration (NOAA) (NA23OAR4590137) to advance social science research work around the Weather Prediction Center’s precipitation forecast products. The grant supports a four-year (02-01-2023 to 01-31-2027) mixed-methods social science research study. This data comes from the portion of the study that focused on a new Urban Rain Rate Dashboard (URRD) product being developed by WPC. The research team focused on three main research questions: How will professional users incorporate an Urban Rain Rate Dashboard into decision-making?; How do users want to access the dashboard, and in which circumstances?; What design and delivery elements can maximize the utility of the dashboard in decision-making for users? Methods involved working with the WPC and Weather Forecast Offices (WFOs) to create location-specific scenarios for each focus group based on past events and included the URRD prototype and several re-designed mock-ups for testing. Six 2-hour virtual focus groups were held in fall 2024 focused in the metro areas of Houston, TX, Denver, CO, Atlanta, GA, Los Angeles, CA, New Orleans, LA, and New York City. Sessions focused on identifying the information needs, gaps, barriers and opportunities in using and understanding the URRD, with the purpose of informing recommendations to improve the usability of these products by end users. Participants were recruited through email and phone with initial contact lists provided by WFO partners. Participants included city officials, emergency management, water resource and flood professionals. During each focus group, participants completed a pre-session survey (which included acknowledgement of informed consent), participated in a facilitated discussion around the scenario, and completed a post-session survey. Pre-session surveys gathered demographic information and flood experience, and post-session surveys asked about specific product usefulness and feedback. Focus group discussion was elicited with questions including: What decisions are you making and when are you making them when preparing for heavy precipitation? and What forecast or weather information do you look for when concerned about urban flooding? And then focusing on each product: Is this information useful?; How would you use this information?; Would you seek this information out?; Would you share this with others?; What changes could improve this product? Quantitative (survey) and qualitative (focus group transcripts/inductive coding using MaxQDA) analysis informed findings shared with the WPC and subsequent revisions to the products. A follow-up online survey was administered after the research team revised some of the products in response to the findings. The research team, including Rachel Hogan Carr, Dr. Kathryn Semmens, Keri Maxfield, and Patrick Painter (all of Nurture Nature Center) and Dr. Burrell Montz (emeritus from East Carolina University), have compiled and shared recommendations to NOAA/WPC to inform iteration on the prototype design. The data provided here include the focus group protocols (scenarios and questions), the pre and post session survey questions, the follow-up survey questions, and the raw survey responses and focus group transcripts (anonymized).

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. 'Parents Anonymous' is a self-help group aimed at strengthening families and reducing child maltreatment outcomes. This study assessed whether parent's participation in the program was associated with child maltreatment outcomes and with their change in risk and protective factors. The study contains both qualitative and quantitative data. For the quantitative segment, group facilitators completed a survey at the beginning of the study. Through these surveys facilitators provided information regarding their level of education, how they heard about their positions, whether they were paid workers or volunteers, and more. Following the completion of facilitator surveys, 206 parents new to the 'Parents Anonymous' program were interviewed. The first interview took place 1 month into the program and the third 6 months later. Parents were asked about their demographics, their living situations, parenting style, and stressors in their lives. In the qualitative segment 36 parents from two states participating in the Spanish-language 'Parents Anonymous' groups were assessed with semi-structured in-person and over the phone interviews. The interviews were conducted once at the beginning of the program, 1 month into the program, and again at 6 months. Additional qualitative data was collected through group observations and focus groups.

The dataset is generated as part of an empirical study to develop a mixed research methodology, taking variables from both quantitative household surveys and qualitative case studies for quickly and effectively capturing rural women involvement and empowerment and their ramifications on technological change and farmer livelihoods. Focus Group Discussions (FGDs) were conducted in the second half of 2018 in Madhya Pradesh (India), where wheat is one of the main crops.

The empirical part of this study is based on data collected from three districts of Madhya Pradesh, India – Jabalpur, Mandla and Damoh. Madhya Pradesh is one of the states with largest wheat growing area (19% of wheat area in India) but with lower wheat productivity (2.85 tons) compared to other major producers (4.29 tons in Punjab and 3.98 tons in Haryana in 2014-15 season).

From each of the three districts, 5 villages were selected for data collection. We restricted our study to those villages where wheat is one of the main crops. Of the total 15 villages, three included the GENNOVATE case study communities (non-random selection). The other 12 will be selected based on the remoteness to the state road highways (6 close, 6 remote). From each village, two wards (sub-units of village) will be selected based on the income poverty (one where most of the poor households reside, and other with the non-poor). Two FGDs were conducted (one for men, one for women) in the selected wards.

Project Overview Adolescence is a critical period for political development. Different political attitudes, political behaviors, and political interests tend to develop during adolescence and persist into adulthood. Welfare participation is associated with lower political participation and pessimistic views of politics among adults, yet we have not uncovered the extent to which welfare participation in adolescence affects political outcomes in adulthood. This project aims to address the disconnect in the literature between what we know about the effects of welfare program experiences and what we know about individual political development. Data and Data Collection Overview The broader project relied on both qualitative and quantitative data, including secondary data from the American National Election Studies (ANES), the National Longitudinal Survey of Youth 1997 (NLSY97) cohort, and the National Longitudinal Study of Adolescent Health (Add Health), which are not included here. The original data collected by the depositing researcher are included, as described below. The qualitative data included a focus group with seven participants and individual interviews with 30 other individuals recruited by the researcher. Interviews were chosen so that participants could be more comfortable sharing personal experiences in a private setting. This data collection technique also allowed the researcher to keep conversations on topic and to ask probing and follow-up questions more easily. The focus group technique was chosen to provide for interactions among the participants involved, thus allowing participants to react to each other’s experiences and comments, and going beyond top-of-mind themes for any one participant. Participants in Round 1 (including those in the focus group and individual interviews) and Round 2 were recruited from the undergraduate student body at a large midwestern public university (N=7), as well as from a local community college (N=13). They were recruited through IRB-approved mass emails to the undergraduate student bodies. Participants in the Round 3 data collection (N=10) were recruited from the sample of Qualtrics panel respondents who completed the Adolescent Hardship and Politics Attitudes Survey (AHPAS; more detail below). Among the ten individuals interviewed in Round 3, five were on welfare during their adolescence, and the other five were not on welfare but grew up in poverty. The Round 1 and Round 2 questionnaire data include the pseudonyms that were selected by participants from a list. The participants in Round 3 chose any name they wanted as a pseudonym. A list of Round 3 names chosen is included as documentation, so that they can be paired with the unique ID code that was used as part of the AHPAS survey. There were two key original quantitative data sources. First, the quantitative data included national-level survey data called the Adolescent Hardship and Political Attitudes Survey (AHPAS), fielded by the researcher via Qualtrics Research Services ( https://www.qualtrics.com/support/survey-platform/distributions-module/online-panels/ ). The AHPAS sample consisted of 1,137 respondents recruited by Qualtrics, who were surveyed in January 2025. About half of the sample had experienced means-tested welfare programs during adolescence, while the other half had not been on welfare, but was in poverty during the period. Second, quantitative data were separately derived from a questionnaire about political attitudes and demographic factors that interview participants from the Round 1 and Round 2 qualitative data collection also completed. After receiving IRB approval, a recruitment email was distributed with a screener survey to identify individuals with adolescent welfare program experience. Participants were selected based on the extent of their program experience (indexed in terms of number of programs used), as well as their availability to participate in the focus group or an interview. Participants were offered a $25 gift card incentive for their participation. To protect confidentiality and privacy, participants selected a pseudonym to use in the subsequent focus group The focus group and interview transcripts were analyzed using Atlas.ti. The transcripts were coded by combining deductive and inductive coding approaches. Selection and Organization of Shared Data Data files shared in this deposit include: The de-identified transcripts from the focus group discussion and the three rounds of individual interviews, all labeled with participants’ chosen pseudonyms, along with the researcher-collected questionnaire data from the same participants. The original national-level quantitative data from the AHPAS used for analysis are also shared, in a raw and clean version, in .dta and .csv formats. The Original version has the uncoded variables in it, while in the Clean version, the variables are coded/labeled, although there is no separate codebook. Secondary users who want to...

This study designed a group reminiscence intervention for older adults and evaluated its preliminary effects on loneliness and psychological well-being. A randomized control trial with convergent parallel design. The intervention comprised 10 sessions based on simple-reminiscence techniques in a senior centre. Inclusion criteria: being lonely, non-institutionalized, ≥60 years and without cognitive impairment. Participants from two senior centres were randomly allocated to experimental or active-control groups. Loneliness and psychological well-being were measured at baseline, post-intervention and follow-up; qualitative data at follow-up. Seventeen women aged 60-72 (66.71 ± 3.077) participated. The experimental group showed significant reductions in emotional loneliness post-intervention (p < 0.05) and at three months (p < 0.05), and increased well-being post-intervention (p < 0.05). No immediate effects on social loneliness were found, but lower emotional (p < 0.05) and social loneliness (p < 0.01) emerged at follow-up. Qualitative findings highlighted the intervention as a meaningful space for social exchange. Group reminiscence appears to reduce emotional loneliness and enhance well-being in older adults. Given the small, homogeneous sample and pilot nature, results are preliminary. Larger studies are needed to confirm and extend these findings. Results may inform future research and clinical practice. What is already known about this topic:Loneliness is a prevalent issue among older adults, and reminiscence therapy has been explored as a potential intervention to address it.Most reminiscence interventions do not specifically target individuals who already experience loneliness, and loneliness is often measured as a secondary outcome.Previous studies have shown that reminiscence interventions can foster social connections, but their direct effects on different types of loneliness remain unclear. Loneliness is a prevalent issue among older adults, and reminiscence therapy has been explored as a potential intervention to address it. Most reminiscence interventions do not specifically target individuals who already experience loneliness, and loneliness is often measured as a secondary outcome. Previous studies have shown that reminiscence interventions can foster social connections, but their direct effects on different types of loneliness remain unclear. What this topic adds:This study is one of the first to design and test a theoretically grounded reminiscence protocol specifically targeting loneliness reduction in lonely older adults, filling a gap in the current research and offering a practical tool for clinicians.Preliminary findings suggest that group reminiscence interventions may positively impact emotional loneliness and psychological well-being.The study presents qualitative experiences of participants to deeper understand how reminiscence may foster meaningful social connections and contribute to well-being. This study is one of the first to design and test a theoretically grounded reminiscence protocol specifically targeting loneliness reduction in lonely older adults, filling a gap in the current research and offering a practical tool for clinicians. Preliminary findings suggest that group reminiscence interventions may positively impact emotional loneliness and psychological well-being. The study presents qualitative experiences of participants to deeper understand how reminiscence may foster meaningful social connections and contribute to well-being.

The framework and methodology will be of particular interest to data- and evidence-oriented agencies and actors who have a commitment to inclusion, gender equity, and fair management and governance. As well as enabling these agencies and actors to strengthen their own programs and projects and the CBNRM policies they inform, the frameworks, methods and insights may be of interest to communities and CBNRM groups themselves as governance actors, co-researchers and owners of CBNRM processes. The methodology is a sequential mixed methods approach comprising three main methods: semistructured interviews, a quantitative survey and two forms of FGDs (one conducted before and the other after the interviews and surveys). Each method supports a different purpose, generated a different type of information and requires a different analytical approach. Each method also offers a different opportunity to explore inclusion and exclusion. The formats of the methods we provide here are those that we refined and adjusted for the Solomon Islands context. We encourage you to adjust and adapt the questions, methods, and sampling strategies to suit your objectives and contexts.

Researchers from the University of Colorado Boulder's Center for the Study and Prevention of Violence (CSPV) partnered with educators in 46 middle schools to implement Safe Communities Safe Schools (SCSS). SCSS seeks to prevent and reduce behavioral incidents, address mental and behavioral health concerns, and increase prosocial behavior in the school setting through three core program components: developing a functioning multidisciplinary school team, building capacity around data use, and selecting and implementing evidence-based programs. The study explored research questions in three areas: readiness (whether schools met baseline criteria and experienced changes in readiness over time), implementation (whether the SCSS model was implemented as intended; whether it is feasible, acceptable, and effective when implemented schoolwide), and associated outcomes (effects on school climate, safety, related behavioral and mental health indicators, and academic outcomes). To explore questions in these three areas, CSPV and external evaluators from American Institutes for Research conducted a mixed-methods randomized control trial with a staggered implementation design using qualitative data (open-ended questions on implementation surveys, focus groups) and quantitative data (staff and student school climate data, attendance/truancy rates, and suspension rates, and academic achievement data). This collection is organized into 12 parts and includes administrative school record data, student and staff climate surveys, and fidelity data. School record data from years 1 and 2 of the study include school-level attendance, truancy, and suspension rates, as well as student-level assessment data. Qualitative focus group data is not currently included in the collection.

Market research companies have benefited from research and development (R&D) expenditure growth as companies develop new products to satisfy consumer demand. Downstream companies continue to rely on market research to create new products and campaigns that fit evolving consumer preferences. As companies strive to enhance consumer-centric strategies amid increased consumer spending, demand for tailored market research solutions has surged. A 10.7% surge in corporate profit over the past five years enabled businesses to outsource more of their research operations to professional market researchers. The digital shift has further transformed the landscape, with companies pioneering new research tools to tap into the vast potential of big data to enhance accessibility and participation. These trends have led to revenue growing at a CAGR of 3.8% to an estimated $36.4 billion over the past five years, including an estimated 2.1% boost in 2025 alone. Consumers' and advertisers' growing reliance on the internet has led to new metrics market researchers can use to better understand consumers. These have allowed new companies to enter the industry and driven providers to adjust services and implement new technologies. The rising use of social media to advertise and market new products across platforms like TikTok and Instagram also contributed to the growing demand for market research. These technological advancements improved data collection and analysis methods, offering actionable insights that helped companies refine marketing strategies and develop better products. New opportunities continue to drive revenue growth, but expansions to services and onboarding of new technology cut researchers’ profitability. Moving forward, the industry will benefit from acceleration in R&D budgets and technological and a data procurement evolution. Companies will strengthen their R&D budgets as economic conditions improve, further driving demand for advanced market research tools. The proliferation of online commerce and smart technologies will give researchers unprecedented access to consumer data. Technological developments, such as artificial intelligence (AI), are poised to create new metrics based on human reactions, which companies can leverage to better understand consumer behavior and preferences. Access to these metrics, however, will lead to tightening data privacy regulations, which may result in higher compliance costs that eat into profitability. Finally, growing emphasis on ethical practices, transparency and data security will shape consumer trust and research standards, creating new opportunities and challenges in a rapidly evolving marketplace. Revenue is poised to grow at a CAGR of 2.4% to an estimated $41.0 billion through the end of 2030.

Data is becoming increasingly ubiquitous today, and data literacy has emerged an essential skill in the workplace. Therefore, it is necessary to equip high school students with data literacy skills in order to prepare them for further learning and future employment. In Indonesia, there is a growing shift towards integrating data literacy in the high school curriculum. As part of a pilot intervention project, academics from two leading Universities organised data literacy boot camps for high school students across various cities in Indonesia. The boot camps aimed at increasing participants’ awareness of the power of analytical and exploration skills, which in turn, would contribute to creating independent and data-literate students. This paper explores student participants’ self-perception of their data literacy as a result of the skills acquired from the boot camps. Qualitative and quantitative data were collected through student surveys and a focus group discussion, and were used to analyse student perception post-intervention. The findings indicate that students became more aware of the usefulness of data literacy and its application in future studies and work after participating in the boot camp. Of the materials delivered at the boot camps, students found the greatest benefit in learning basic statistical concepts and applying them through the use of Microsoft Excel as a tool for basic data analysis. These findings provide valuable policy recommendations that educators and policymakers can use as guidelines for effective data literacy teaching in high schools.