The present data were used to through the LPM the effect of CLI in the acquisition of L3 English past perfect, present progressive, and present perfect tenses by L1 Kirundi-L2 French bilinguals. The subtractive language groups design was used: One trilingual (L1 Kirundi-L2 French-L3 English learners) group was compared to two bilingual (L1 Kirundi-L2 English and L1 French-L2 English learners) groups in order to derive which previously acquired language was driving CLI among L3 learners. Each language group had 30 learners distributed in four proficiency groups, namely the pre-intermediate group (6 participants), lower-intermediate group (7 participants), upper-intermediate group (11 participants), and advanced group (6 participants). Therefore, there were two independent variables (language group and proficiency group) and three continuous dependent variables which were the participants' scores on the three target structures, namely the past perfect, present progressive, and present perfect tenses.

The data were used to test the following predictions:

1. With regard to the past perfect tense (L1=L2=L3), learners of L3 English with a background knowledge in L1 Kirundi and L2 French are likely to have no difficulty in the acquisition of the said tense in English regardless of their English proficiency level; i.e. even lower proficiency learners will perform well on that tense. However, higher proficiency learners may make most correct use of this tense.

2. With regard to the present progressive tense (L1≠L3≠L2), we can predict that all the three language groups, i.e. L1 Kirundi, L1 French and L3 groups, will face difficulties in their performance on this tense. In other words, none of the previously acquired languages (neither L1 Kirundi, nor L2 French) is expected to significantly affect the performance of L3ers on the said tense. Lower proficiency learners are predicted to face most difficulty on the tense.

3. With regard to the present perfect tense (L3=L2≠L1), we can predict that the L3 group will perform similarly as the L1 French group, while the two groups are likely to outperform the L1 Kirundi group. This implies that facilitative CLI is expected from L2 French in the L3 group.

4. Considering the present research scenarios for the past perfect (L1=L2=L3), present perfect (L1=L3≠L2), and present progressive (L1≠L2≠L3) tenses, we predict CLI where L3 learners are expected to acquire the past perfect earlier than the present perfect, and the present perfect earlier than the present progressive. In other words, their performance on the past perfect tense should be significantly higher than that on the present perfect while their score on the present perfect is expected to be significantly higher than that on the present progressive.

Data were elicited through the grammaticality judgment task, and the raw data analyzed in the SPSS software using descriptive statistics, MANOVA, post-hoc comparisons, and independent samples t-tests.

This dataset consists of three data folders including all related documents of the online survey conducted within the NESP 3.2.3 project (Tropical Water Quality Hub) and a survey format document representing how the survey was designed. Apart from participants’ demographic information, the survey consists of three sections: conjoint analysis, picture rating and open question. Correspondent outcome of these three sections are downloaded from Qualtrics website and used for three different data analysis processes.

Related data to the first section “conjoint analysis” is saved in the Conjoint analysis folder which contains two sub-folders. The first one includes a plan file of SAV. Format representing the design suggestion by SPSS orthogonal analysis for testing beauty factors and 9 photoshoped pictures used in the survey. The second (i.e. Final results) contains 1 SAV. file named “data1” which is the imported results of conjoint analysis section in SPSS, 1 SPS. file named “Syntax1” representing the code used to run conjoint analysis, 2 SAV. files as the output of conjoint analysis by SPSS, and 1 SPV file named “Final output” showing results of further data analysis by SPSS on the basis of utility and importance data.

Related data to the second section “Picture rating” is saved into Picture rating folder including two subfolders. One subfolder contains 2500 pictures of Great Barrier Reef used in the rating survey section. These pictures are organised by named and stored in two folders named as “Survey Part 1” and “Survey Part 2” which are correspondent with two parts of the rating survey sections. The other subfolder “Rating results” consist of one XLSX. file representing survey results downloaded from Qualtric website.

Finally, related data to the open question is saved in “Open question” folder. It contains one csv. file and one PDF. file recording participants’ answers to the open question as well as one PNG. file representing a screenshot of Leximancer analysis outcome.

Methods: This dataset resulted from the input and output of an online survey regarding how people assess the beauty of Great Barrier Reef. This survey was designed for multiple purposes including three main sections: (1) conjoint analysis (ranking 9 photoshopped pictures to determine the relative importance weights of beauty attributes), (2) picture rating (2500 pictures to be rated) and (3) open question on the factors that makes a picture of the Great Barrier Reef beautiful in participants’ opinion (determining beauty factors from tourist perspective). Pictures used in this survey were downloaded from public sources such as websites of the Tourism and Events Queensland and Tropical Tourism North Queensland as well as tourist sharing sources (i.e. Flickr). Flickr pictures were downloaded using the key words “Great Barrier Reef”. About 10,000 pictures were downloaded in August and September 2017. 2,500 pictures were then selected based on several research criteria: (1) underwater pictures of GBR, (2) without humans, (3) viewed from 1-2 metres from objects and (4) of high resolution.

The survey was created on Qualtrics website and launched on 4th October 2017 using Qualtrics survey service. Each participant rated 50 pictures randomly selected from the pool of 2500 survey pictures. 772 survey completions were recorded and 705 questionnaires were eligible for data analysis after filtering unqualified questionnaires. Conjoint analysis data was imported to IBM SPSS using SAV. format and the output was saved using SPV. format. Automatic aesthetic rating of 2500 Great Barrier Reef pictures –all these pictures are rated (1 – 10 scale) by at least 10 participants and this dataset was saved in a XLSX. file which is used to train and test an Artificial Intelligence (AI)-based system recognising and assessing the beauty of natural scenes. Answers of the open-question were saved in a XLSX. file and a PDF. file to be employed for theme analysis by Leximancer software.

Further information can be found in the following publication: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Format: The Online survey dataset includes one PDF file representing the survey format with all sections and questions. It also contains three subfolders, each has multiple files. The subfolder of Conjoint analysis contains an image of the 9 JPG. Pictures, 1 SAV. format file for the Orthoplan subroutine outcome and 5 outcome documents (i.e. 3 SAV. files, 1 SPS. file, 1 SPV. file). The subfolder of Picture rating contains a capture of the 2500 pictures used in the survey, 1 excel file for rating results. The subfolder of Open question includes 1 CSV. file, 1 PDF. file representing participants’ answers and one PNG. file for the analysis outcome.

Data Dictionary:

Card 1: Picture design option number 1 suggested by SPSS orthogonal analysis. Importance value: The relative importance weight of each beauty attribute calculated by SPSS conjoint analysis. Utility: Score reflecting influential valence and degree of each beauty attribute on beauty score. Syntax: Code used to run conjoint analysis by SPSS Leximancer: Specialised software for qualitative data analysis. Concept map: A map showing the relationship between concepts identified Q1_1: Beauty score of the picture Q1_1 by the correspondent participant (i.e. survey part 1) Q2.1_1: Beauty score of the picture Q2.1_1 by the correspondent participant (i.e. survey part 2) Conjoint _1: Ranking of the picture 1 designed for conjoint analysis by the correspondent participant

References: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data esp3\3.2.3_Aesthetic-value-GBR

The SPSS file includes the raw data as well as the generated variables. The word file explains the SPSS file and provides information on the data analyses. The data is NOT available for public use.

This study selected the relevant literature related to the adverse drug reactions of metformin from 1991 to 2020 as the data source, divided the time segment with a period of 3 years, and obtained the title information (see the title collection of the literature included in the study. zip), and then extracted the subject words through the bicomb2021 software to construct the co-occurrence matrix, and a total of 10 co-occurrence matrices were obtained (see the subject word co-occurrence matrix collection included in the study. zip). Import the 10 co-occurrence matrices into the self-designed python code and r code (see opportunity code. zip; trust code. zip; open triangle and closed triangle code. zip) to obtain the opportunity, trust value and the number of edge triangles of each node pair in the 10 networks. Use GePhi0.9.7 software to calculate the motivation value of the node pair, use Excel to calculate the global clustering coefficient of each network, and the edge clustering coefficient of each node pair, The number of edge triangles of each node pair is built by using excel software to construct the scatter diagram of node pair opportunity, trust, motivation value and node pair edge clustering coefficient, and the correlation between node pair opportunity value and edge clustering coefficient is calculated by using spss software, as well as the correlation between node pair trust, motivation value and edge clustering coefficient, and the number of closed triangles of node pair (see code operation and software calculation result set. zip).Select the literature bibliography data from 2000 to 2009 to build the panel data (see the literature bibliography collection included in the study. zip), and also use the self-designed python code and r code (see opportunity code. zip; trust code. zip; open triangle and closed triangle code. zip) to get the opportunity, trust value and the number of edge triangles of each node pair in 10 networks, and use GePhi0.9.7 software to calculate the motivation value of node pairs Proximity centrality, intermediary centrality, feature vector centrality and average path length of node pairs are imported into Stata/MP 17.0 software to obtain the correlation between node attributes and network characteristics (see code operation and software calculation result set. zip).The data contained in each data name is described in detail:1. Collection of bibliographies included in the studyThe data collection contains two folders, named the literature collection from 1991 to 2020 and the literature collection from 2000 to 2009. The literature collection from 1991 to 2020 stores the bibliographic data of 10 time periods from 1991 to 2020, and the literature collection from 2000 to 2009 stores the bibliographic data of 10 overlapping windows from 2000 to 2009.2. Co-occurrence matrix set of subject words included in the studyThe data set contains two folders, named the 1991-2020 subject word co-occurrence matrix set and the 2000-2009 subject word co-occurrence matrix set. The subject word co-occurrence matrix of 1991-2020 contains the subject word co-occurrence matrix of 10 time segments from 1991-2020. The first row and first column of each co-occurrence matrix are subject words, and the number represents the number of co-occurrence times of the subject word pair. The subject word co-occurrence matrix set in 2000-2009 stores the subject word co-occurrence matrix of 10 time windows in 2000-2009.3. Opportunity Code.zipThis code is used to calculate the opportunity value of node pair. The input data is co-occurrence matrix, and the input format is. csv format.4. Trust Code.zipThis code is used to calculate the opportunity value of node pair. The input data is co-occurrence matrix, and the input format is. csv format.5. Code of open triangle and closed triangle.zipThis code is used to calculate the number of closed triangles and open triangles on the side of the node pair. The input data is the co-occurrence matrix, and the input format is. csv format.6. Code run and software calculation result set.zipThe data set contains two folders, named 1991-2020 calculation results and 2000-2009 calculation results. The 1991-2020 calculation results store the calculation results and scatter diagrams of 10 time segments in 1991-2020. Take 1991-1993 as an example, the first row of each table is marked with the opportunity, comprehensive trust, motivation, edge clustering coefficient, and the number of closed triangles. At the end of each table, the mean value of opportunity, trust, motivation and Pearson correlation coefficient with edge clustering coefficient and the number of closed triangles are calculated.The 2000-2009 folder stores the panel data and the opportunity, trust, motivation of the stata software calculation, and the correlation between the node attributes and the network characteristics of the node.

To access the coded interview transcripts and description of codes (i.e., codebook), you need NVivo for Mac Version 11.3.2 (1888). To access the raw dataset that lists the frequency with which each benefit was mentioned by DSA board members and non-DSA board members, you need IBM SPSS Statistics 24. ...To access the processed/analysed data from the Mann-Whitney U tests, you need IBM SPSS Statistics 24 to access the .spv file and Microsoft Word to access the .doc file. [more]

Word Lists for Experiments 1 through 3. SPSS data files for Experiments 1 through 3. T-test data file for Experiment 3. SPSS files include recall as a function of condition in each experiment.

Title: Peer-to-peer dialogue about teachers’ written feedback enhances students’ understanding on how to improve writing skills. Short description of study set-up: Sixty-three second-year university students participated in a pre-test-post-test design with mixed methods. Instruments: Questionnaires: Students’ perceptions of the quality of both the written feedback in terms of Feed up, Feed back and Feed forward and the feedback dialogue were measured using an adjusted version of a validated questionnaire by De Kleijn et al. (2014). The questionnaire contained 16 items of which one item targeted the overall quality of teachers’ written feedback on a ten-point scale, ranging from 1 to 10. The remaining 15 items were distributed among three subscales, specifically ‘Feed up’ (four items), ‘Feed back’ (six items) and ‘Feed forward’ (five items), and rated on a five-point Likert-type scale, ranging from 1 (fully disagree) to 5 (fully agree). An example of a feed-up item is: ‘By means of the written feedback it is clear what the assessment criteria of a scientific report are’. An example of a feed-back item is: ‘The written feedback indicates what I do wrong’ and an example of a feed-forward item is: ‘The written feedback indicates how I can improve my report’. The questionnaire that was administered before and after the intervention comprised similar items. In the post-test questionnaire, a few items were added to measure how students perceived the quality of the feedback dialogue. A reliability analysis of the feed-up, feed-back and feed-forward subscales within pre- and post-test questionnaires yielded acceptable reliability coefficients ranging from 0.79 to 0.91 (Peterson 1994). Preliminary pilot-tests were conducted to determine item clarity and adjustments were made to unclear items. Additionally, the logistics of the intervention were tested during the pilot-test. Focus groups: To provide more in-depth data focus groups were conducted (Stalmeijer et al. 2014). At the end of the last feedback dialogue session of both tracks, each student was invited for a focus group session. Eventually, two focus groups comprised six students and lasted approximately one hour. The third focus group contained 12 students; it was a combined group of two times six students, because we unfortunately scheduled the meetings at the same time. To ensure each student’s voice to be heard, this focus group continued for one and a half hour. Each focus group was guided by a moderator (fourth author) and was observed by one member of the research team. In semi-structured interviews, the actual topics discussed in the focus groups covered student experiences regarding the content of the written teacher feedback as well as the added value of the peer-to-peer dialogue about this written feedback. The interviews were audiotaped. Explanation of data files: The data files contain 114 anonymized pdf’s of the original questionnaires filled in by the participants; Focus group interviews; audio files of focus groups; transcripts of focus groups; SPSS Data file Schillings-complete DA.sav. Quantitative data files: 114 Original questionnaires (pdf’s), archived as questionnaires in pdf.7zip 1 Data file Schillings-complete DA.sav (SPSS file) Total SPSS tabellen (Word document): meaning and ranges or codings of all columns Study 2 variabelen kwantitatieve vragenlijst (pdf) Quantitative data files: Focus groep interview-gids (Word document) 8 audio files of 3 focus groups (6 m4a files; 2 wav files), as audio focusgroepen a.7zip; Transcripts of 3 focus group (Word documents)

Study 1 Text preparation (specific questionnaire questions can be found in the paper) Vocabulary 1: 845 words related to material wealth and spiritual wealth, as well as their relationship, in The Contemporary Chinese Dictionary (7th edition); Vocabulary 2: Further screening, deleting irrelevant words, merging synonyms, and organizing a total of 69 sets of vocabulary. Test (detailed information can be found in the paper, variable labels and meanings can be found in SPSS data) Data 1: In August 2021, questionnaires were distributed through online platforms with an IP address limited to Zhejiang Province. A total of 503 responses were received, and invalid responses such as short answer times and regular responses were deleted, resulting in 462 valid responses (91.85%). Data 2: In September 2021, questionnaires were distributed through online platforms with IP addresses limited to Zhejiang Province. A total of 208 responses were received, and invalid responses such as short response times and regular responses were deleted, resulting in 201 valid responses (96.63%). Study 2 Test (detailed information can be found in the paper, variable labels and meanings can be found in SPSS data) Data 3: From July to August 2023, questionnaires were distributed through online platforms with IP addresses limited to Zhejiang Province. A total of 1045 answer sheets were collected. Deleting invalid answers such as short answer times and regular responses resulted in 937valid responses (89.67%).

BackgroundPsychological distress is common amongst people with a terminal illness. While mental health specialists have a role in assessment and management of those with complex psychological problems, hospice clinicians provide psychological support to most patients and families.AimTo describe current practices relating to the provision of psychological support by hospice clinicians, and to explore perceived competence, confidence and training needs in relation to this aspect of their role.DesignWe used a parallel mixed methods research design. An online questionnaire consisting of closed and free-text questions was emailed to 273 hospices in the UK and the Republic of Ireland between May and June 2023.Setting/ParticipantsParticipants included nurses, doctors, and allied health professionals employed by a hospice. Quantitative data was analysed descriptively using SPSS 27, and free-text data was analysed thematically guided by the framework method.Results151 hospice staff completed the questionnaire. Most (81%) reported that they regularly screen for psychological distress, but clinical judgement, as opposed to use of a validated screening tool, was most common. Respondents reported confidence and competence in many areas. Overall, 72% strongly agreed they were willing to explore difficult subject matter. However, only 25% strongly agreed they were confident in differentiating level of psychological need, and 36% reported they could not arrange appropriate psychological support when needed. Almost all (95%) agreed that training in psychological support would enhance their practice. Individual and family factors such as denial, communication challenges and family conflict were barriers to providing psychological support. Systemic factors were time constraints, prioritisation of physical symptoms and limited access to mental health specialists.ConclusionHospice staff report that they are confident in providing basic psychological support. However, there was a desire for further training in this aspect of care. Clearer guidance on referral criteria for specialist psychological support is warranted.

Abstract

The Rwandan government has created a conducive environment for growth in multiple sectors, set ambitious targets to become a middle-income country by 2020, and sees the development of the financial sector as a key to meeting these targets.

In 2014-2015, a research company InterMedia conducted Financial Inclusion Insights Survey to explore the uptake and usage of financial services generally and mobile financial services in Rwanda. This study sought to understand the role digital financial services (DFS) play in money transfers, payments and savings among various consumer segments.

The study objectives were: - to track citizens' access to financial services generally and the uptake and use of mobile financial services (MFS) specifically - to evaluate service performance amongst MFS agents and customers - to identify drivers and barriers to further adoption of MFS - to make forward projections and provide insight that will generate market growth.

The survey was conducted among nationally representative sample of Rwandan adults age 15 and older. The sample size was 2,003. The survey was administered using face-to-face interviews from December 2014 to February 2015. The results provided baseline measurements. Subsequent annual surveys can measure trends and track market developments in digital financial services.

Geographic coverage

National

Analysis unit

• Individuals,
• Households

Universe

Adults age 15 and older residing in households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2012 Census file obtained from the Rwanda National Institute of Statistics (NISR) was used as the sampling frame. This file includes all the provinces (5), districts (30) and sectors (416) in Rwanda with their respective population. It also contains the proportion of rural and urban population. After selecting the sectors for the survey, the NISR provided the list of cells and villages for selected sectors from which villages were selected.

The total sample size was 2,000 interviews distributed across 200 villages with 10 interviews per village. A simple random probability sampling technique was used to distribute the Primary Sampling Units across the 416 sectors, taking into account the rural urban split of 83/17 in Rwanda.

Within the selected sectors one village was randomly selected. In total 33 urban and 167 rural villages were selected. Random walk and Kish grid methods were respectively used to select households and respondents.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was read word for word, almost always in Kinyarwanda.

In addition to the questionnaire, the following forms were filled daily: - Interviewer log sheet, - Supervisor observation forms, issue log, field log and back-check sheets.

Three consent forms were used in the study: - Parent/guardian consent form for all respondents who were between 15-17 years of age, - Informed consent form to participate in a separate follow-up study for respondents who had registered mobile money accounts, - Photography consent form for all respondents.

Cleaning operations

Data was manually captured using QPSMR (Questionnaire Processing Software Market Research) in double entry (100% verification). A total of 34 data entry clerks participated throughout project in two shifts (night and day shifts). 25% of the questionnaires were captured concurrently with field work and the rest upon field work completion.

The verified data was then exported to SPSS for consistency checks. Variable checks were created using a codebook developed by InterMedia for each column to check the consistency in base and by extension also used to pick out missing data points.

Stimulus word lists, SPSS data analysis files, and serial position curves for three experiments exploring the effects of distinctiveness and priority on free recall of words.

The study explored the relationship between students’ attitude towards, and performance in mathematics word problems (MWTs), mediated by the active learning heuristic problem solving (ALHPS) approach. Specifically, this study investigated the correlation between students’ performance and their attitude towards linear programming word tasks (ATLPWTs). Tools for data collection were: the adapted Attitude towards Mathematics Inventory-Short Form (ATMI-SF), (α = .75) as a multidimensional measurement tool, and linear programming achievement tests (pre-test and post-test). A quantitative approach with a quasi-experimental pre-test, post-test non-equivalent control group study design was adopted. A sample of 608 eleventh-grade Ugandan students (291 male and 317 female) from eight secondary schools (both public and private) participated. Data were analyzed using PROCESS macro (v.4) for SPSS version 26. The results revealed a direct significant positive relationship between students’ performance and their ATLPWTs. Thus, students’ attitude positively and directly impacted their performance in solving linear programming word problems. The present study contributes to the literature on performance and attitude towards learning mathematics. Overall, the findings carry useful practical implications that can support theoretical and conceptual framework for enhancing students’ performance and attitude towards mathematics word problems.

Abstract

The Ghana Child Labour Survey is the first nationwide survey in the country specifically designed to collect information on the various aspects of working children, within the framework of the International Programme on the Elimination of Child Labour (IPEC). It is a two-in-one survey, which canvassed children in households as well as children on the street, using two different sample designs. The fieldwork was conducted in February 2001, with technical assistance from the International Labour Organization (ILO). It is expected that the results of the survey will generate more awareness of child labour issues, promote the campaign against its practice, and serve as the basis for the formulation of appropriate intervention programmes.

Geographic coverage

National

Analysis unit

Individual person (household head and children)

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2001 Ghana Child Labour Survey comprised both a nationwide probability sample survey of all households in Ghana and a supplementary non-probability survey of street children.

The sampling frame for the household-based sample survey was the list of all 26,555 Enumeration Areas (EAs) from the 2000 Population and Housing Census of Ghana with corresponding data on number of households. The household sample survey was based on a two-stage stratified cluster design. The frame was stratified into urban and rural localities of residence and by the 10 administrative regions in the country.

At the first stage, 500 Enumeration Areas (EAs) were systematically selected, with probability proportional to size, the measure of size being the number of census households. At the second stage, 20 households were selected from each of the 500 EAs to produce an overall sample size of 10,000 households. The design ensured that every household in the country had the same chance to be selected; in other words, the sample was self-weighting (see Appendix II for a detailed explanation of the sample design). The sampling process yielded the allocation of households to each stratum (urban/rural and region) shown in Table 2.1. The sample also yielded an average weight of 370.12 for each child. This means that each child in the survey represents about 370 children.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry was centralized at the head office. The main data entry software was the IMPS (Integrated Microcomputer Processing System). The two questionnaires, street children and the household questionnaires, were entered separately. Edit programs in CONCOR were used to edit the data, after which error listings were printed and corrected on EA level.

After editing, the ASCII data were put together and cleaned further, using SPSS and SAS. This was done by running consistency checks on every variable and the database was generated thereby. The analysis and tabulation were executed in SAS and SPSS. Estimates, standard errors, confidence intervals and design effects were generated using the CENVAR module in IMPS.

Response rate

Out of the 10,000 selected households, 9,889 were successfully interviewed, indicating a household response rate of 98.9 percent. A similar response rate was achieved in all regions and in rural/urban areas.

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

Survey Objectives The 2005 Sierra Leone Multiple Indicator Cluster Survey has the following primary objectives: - To provide up-to-date information for assessing the situation of children and women in Sierra Leone; - To furnish data needed for monitoring progress toward goals established by the Millennium Development Goals and the goals of A World Fit For Children (WFFC) as a basis for future action; - To contribute to the improvement of data and monitoring systems in Sierra Leone and to strengthen technical expertise in the design and implementation of these systems and analysis of the information they generate.

Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation The survey was conducted by Statistics Sierra Leone with financial and technical support from UNICEF Sierra Leone and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey is nationally representative and covers the whole of Sierra Leone

Analysis unit

Households (defined as a group of persons who usually live and eat together)

De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Sierra Leone MICS3 was to produce statistically reliable estimates of most indicators at the national level, for urban and rural areas, and at the province level. The design of the sample allows the estimation of indicators at district level - however, such estimates are likely to be very imprecise, since the sample size was not determined to enable district-level estimates.

A multi-stage, stratified cluster sampling approach was used to select the survey sample. The 2004 census frame was used for the selection of clusters. Census enumeration areas (EAs) were defined as primary sampling units (PSUs), and were selected in each district using pps sampling procedures. The stages of the sampling approach are described below.

Description of sampling approach for Sierra Leone MICS3

Stage 1: Selection of EAs The list of all EAs in Sierra Leone was ordered using implicit stratification according to the following variables: province; district; chiefdom; and, population size. 320 EAs were then selected using stratified systematic sampling, thus yielding a self-weighting sample. Selected EAs were then classified as rural (population of the settlement were the EA is located is < 2,000) or urban (population of the settlement where the EA is located is = 2,000).

Stage 2: Selection of households A list of all households in each of the 320 selected EAs as enumerated during the 2004 census was prepared using data contained in the 2004 Population and Housing Census registers. A team of listers/verifiers visited each of the 320 EAs to update the household lists in the EA by verifying each of the households on the list and adding any new households that have been formed in order to control for out-movers, non-existent households, and/or new households. This task produced an updated listing of households in all selected EAs. The newly updated listing of households in each EA was then sequentially numbered from 1 to n (the total number of households in the enumeration area of interest) at the Statistics Sierra Leone Office. Sampling experts then selected 25 households in each EA using systematic selection procedures.

(Information extracted from final report: Statistics Sierra Leone and UNICEF-Sierra Leone 2007. Sierra Leone Multiple Indicator Cluster Survey 2005, Final Report. Freetown, Sierra Leone: Statistics Sierra Leone and UNICEF-Sierra Leone.)

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Sierra Leone MICS3 were structured questionnaires based on the MICS3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or caretaker of the child.

English is the only written language in Sierra Leone; for this reason, questionnaires were written in English and verbally translated by enumerators into the language preferred by the respondent (generally Krio, Timne, Mende or Limba), using standardized, pre-tested key words. The questionnaires were pre-tested in the Western Area in September 2005. Based on the results of the pre-test, modifications were made to the wording of the questions, the response categories, and the key words.

Information extracted from final report: Statistics Sierra Leone and UNICEF-Sierra Leone 2007. Sierra Leone Multiple Indicator Cluster Survey 2005, Final Report. Freetown, Sierra Leone: Statistics Sierra Leone and UNICEF-Sierra Leone.

Cleaning operations

Data were entered on 30 microcomputers by 30 data entry operators and two data entry supervisors.

Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps: 1) Questionnaire reception 2) Office editing and coding 3) Data entry 4) Structure and completeness checking 5) Verification entry 6) Comparison of verification data 7) Back up of raw data 8) Secondary editing 9) Edited data back up After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files: 10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5) 11) Recoding of variables needed for analysis 12) Adding of sample weights 13) Calculation of wealth quintiles and merging into data 14) Structural checking of SPSS files 15) Data quality tabulations 16) Production of analysis tabulations

Data editing took place at a number of stages throughout the processing (see Other processing), including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files

Detailed documentation of the editing of data can be found in the data processing guidelines in the MICS global manual, see www.childinfo.org

Response rate

Of the 8,000 households selected for the sample, only 7,125 were found to be occupied. Of the 7,125 occupied households, 7,078 were successfully interviewed for a household response rate of 99.3 per cent. In the interviewed households, 9,257 eligible women (aged 15-49) were identified. Of these, 7,654 were successfully interviewed, yielding a response rate of 82.7 per cent. The response rate for the Questionnaire for Children Under Five was 88.9 per cent; mothers/caretakers of 5,246 children under five were successfully interviewed, from among 5,904 children under five who were identified in the interviewed households. Overall response rates of 82.1 percent and 88.3 percent are calculated for the women's and under-5's interviews, respectively

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors can be evaluated statistically. The sample of respondents to the 2005 MICS is only one of many possible samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differe

(1) Part2FSWDataJan312018_Final.SAV This file contains the SPSS data file with deidentified and clean data on female sex worker sample. (2) SPSS Syntax for part 2 Female Sex Worker paper.docx This is a word document with the SPSS syntax used for the analysis of the FSW sample that is presented in the 3ie report.

This dataset consists of 3 files:

File 1: Qualitative data enclosed in Microsoft Excel file. Data collection includes one to one interviews with Western Australian migrants.

Content analysis: interview utterances were analysed for content, classified into categories, and coded and entered in Microsoft Excel file columns.

File 2: Qualitative data enclosed in Microsoft Word file. Data collection includes one to one interviews with Western Australian migrants.

Content analysis: interview utterances were analysed for content, classified into categories, and coded and entered in Microsoft Word file columns.

File 3: Quantitative data enclosed in Qualtrics software which includes public librarians’ responses to a Web survey. SPSS Tables and Figures imported from the Qualtrics Software raw data into a Word file.

(1) Part2FSWDataJan312018_Final.SAV This file contains the SPSS data file with deidentified and clean data on female sex worker sample. (2) Part2TruckerDataJan312018_Final.SAV This file contains the SPSS data file with deidentified and clean data on female sex worker sample. (3) SPSS Syntax for part 2 Female Sex Worker paper.docx This is a word document with the SPSS syntax used for the analysis of the FSW sample that is presented in the 3IE report. (4) SPSS syntax for part 2 trucker paper.docx This is a word document with the SPSS syntax used for the analysis of the trucker sample that is presented in the 3IE report.

The study explored the relationship between students’ attitude towards, and performance in mathematics word problems (MWTs), mediated by the active learning heuristic problem solving (ALHPS) approach. Specifically, this study investigated the correlation between students’ performance and their attitude towards linear programming word tasks (ATLPWTs). Tools for data collection were: the adapted Attitude towards Mathematics Inventory-Short Form (ATMI-SF), (α = .75) as a multidimensional measurement tool, and linear programming achievement tests (pre-test and post-test). A quantitative approach with a quasi-experimental pre-test, post-test non-equivalent control group study design was adopted. A sample of 608 eleventh-grade Ugandan students (291 male and 317 female) from eight secondary schools (both public and private) participated. Data were analyzed using PROCESS macro (v.4) for SPSS version 26. The results revealed a direct significant positive relationship between students’ performance and their ATLPWTs. Thus, students’ attitude positively and directly impacted their performance in solving linear programming word problems. The present study contributes to the literature on performance and attitude towards learning mathematics. Overall, the findings carry useful practical implications that can support theoretical and conceptual framework for enhancing students’ performance and attitude towards mathematics word problems.

General information on the survey and cohort

The present data were collected in 2023 at a secondary school in Hamburg. A questionnaire was used as a pre- and post-test in computer science courses in years 10 and 11 to investigate the agreement with various items and how this changed. The questionnaires were transferred to SPSS for analysis.

76 students aged 15-17 took part in the survey and the teaching intervention. This data set only contains the data that is available in full. It therefore includes the responses of 69 students, of which a total of 22 felt assigned to the female gender and 47 to the male gender.

Data set

The data is purely quantitative, as this is only the evaluation of the pre- and post-test and not the evaluation of the conceptual change texts themselves. The item list is made up of 20 items relating to artificial intelligence, which were drawn up according to the Big Ideas of the AI4K12 initiative [1]. The students' perceptions of the items come from various past studies that have surveyed students' conceptions of AI.

The data has a pseudonymized code that can be linked to the conceptual change text of the experimental group and the data can be assigned accordingly. The data is also divided into the experimental group (abbreviation 2) and the control group (abbreviation 1) so that a comparison between the groups is quickly possible. The abbreviation W in front of the items stands for a “true” statement and SV for “student conception”. A Likert scale from 0 - does not apply at all to 3 - applies completely was used.

The data set is cleansed data. All data sets that were not complete or that were given different codes in the pre-test and post-test and therefore could no longer be clearly assigned were removed.

Literature

[1] AI for K12 (Hrsg.) (2020): AI4K12.org. Available online at: https://ai4k12.org/ [last checked 12.03.2023]

The dataset included the event-related potential (ERP) responses to visual stimuli, focusing on the N170 component, which were collected from 20 children with ADHD aged 6 to 12 and 20 typical developing children who were stringently matched with the ADHD participants based on gender and age. The performance of reading-related skills, such as rapid naming speed and orthographic processing abilities were included in as well. The dataset.sav is a SPSS Statistics Data Document file that has been organized for data analysis purposes. The analysis.sps file is a SPSS Statistics Syntax file containing the code to perform data analysis operations.