SMaRteN, in partnership with Vitae, conducated research into the impact of COVID-19 on the working lives of doctoral researchers and research staff. This is the Time 2 data set. Data was collected at the end of September and start of October 2020. Please see link at bottom of page for the first data set.SMaRteN www.smarten.org.ukThe UK Research and Innovation (UKRI) funded Student Mental Health Research Network (SMaRteN) is working to support and encourage better research into student mental health. SMaRteN is based at Institute of Psychiatry, Psychology and Neurosciences at King’s College London.Vitae is a non-profit programme supporting the professional and career development of researchers. www.vitae.ac.uk @vitae_newsCovid-19 and the associated lock down has caused substantive disruption to the study and work of doctoral students and researchers in universities. The response to the pandemic has varied across universities and research funders.SMaRteN and Vitae aim to develop a national picture for how doctoral researchers and research staff have been affected by the pandemic.​The survey includes questions relating to the impact of COVID-19 on research work, mental wellbeing, social connection. We further address the impact of COVID-19 on changes to employment outside of academia, living arrangements and caring arrangements and the consequent effect of these changes on research work. The survey considers the support provided by supervisors / line managers and by universities.Data available here as either an SPSS or Excel download:SPSS file contains labelsExcel file contains labels and brief notes about codingRecoding data for CV19 impact - SPSS Syntax file describes steps taken to code dataCV19_impact_on_researchers - word document, export from Qualtrics of the survey.Please note, data has been removed from this data set to ensure participant anonymity.For further information, please contact Dr Nicola Byrom - nicola.byrom@kcl.ac.uk

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

I attach data and code to reproduce analyses for manuscript - Personality and Team Identification Predict Violent Intentions Among Soccer Supporters.I have attached the following data files:- Soccer_supporters_raw.sav- Soccer_data_raw.csv- Soccer_data.xlsx- Soccerpathmodel.txtCodebook:- CodeBook_soccersupportersdata.csv*Note that this codebook applies to the raw data.And code:Syntax_soccer_supporters.sps (to be opened in SPSS)*Note that this code is also available in non-proprietary .txt format: Syntax_soccer_supporters.txtSoccerpathmodel.inp (to be opened in MPLUS (Muthén & Muthén, 2012, see also https://www.statmodel.com/ ).

@font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-536859905 -1073732485 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin-top:6.0pt; margin-right:0cm; margin-bottom:12.0pt; margin-left:0cm; mso-pagination:widow-orphan; font-size:12.0pt; mso-bidi-font-size:11.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:11.0pt; mso-ansi-font-size:11.0pt; mso-bidi-font-size:11.0pt; font-family:"Cambria",serif; mso-ascii-font-family:Cambria; mso-ascii-theme-font:major-latin; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:major-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}.MsoPapDefault {mso-style-type:export-only; margin-bottom:10.0pt; line-height:115%;}div.WordSection1 {page:WordSection1;}*Note that this code is also available in non-proprietal .txt format: soccerpathmodelcode.txtTo reproduce the results for this manuscript, please first open the file “Soccer_supporters_raw.sav” in SPSS (ideally version 25, with PROCESS add-on), and run the accompanying syntax: “Syntax_soccer_supporters.sps”. I also attach a non-proprietary version of this raw data - Soccer_data_raw.csvNote that the code/syntax to run mediation analyses with PROCESS, is not available, since PROCESS does not allow for the pasting of syntax. So this part of the analyses needs to be completed manually through the point-and-click interface.The remaining analyses were conducted in MPLUS. To do so, the original raw SPSS file was saved (after recoding and computing index variables), as a text file. We have also included this data in .xlsx format - see file Soccer data.xlsxTo reproduce the path model tested in MPLUS, run the input file “soccerpathmodel.inp” ensuring that the accompanying file - Soccerpathmodel.txt is located in the same folder.

"We believe that by accounting for the inherent uncertainty in the system during each measurement, the relationship between cause and effect can be assessed more accurately, potentially reducing the duration of research."

Short description

This dataset was created as part of a research project investigating the efficiency and learning mechanisms of a Bayesian adaptive search algorithm supported by the Imprecision Entropy Indicator (IEI) as a novel method. It includes detailed statistical results, posterior probability values, and the weighted averages of IEI across multiple simulations aimed at target localization within a defined spatial environment. Control experiments, including random search, random walk, and genetic algorithm-based approaches, were also performed to benchmark the system's performance and validate its reliability.

The task involved locating a target area centered at (100; 100) within a radius of 10 units (Research_area.png), inside a circular search space with a radius of 100 units. The search process continued until 1,000 successful target hits were achieved.

To benchmark the algorithm's performance and validate its reliability, control experiments were conducted using alternative search strategies, including random search, random walk, and genetic algorithm-based approaches. These control datasets serve as baselines, enabling comprehensive comparisons of efficiency, randomness, and convergence behavior across search methods, thereby demonstrating the effectiveness of our novel approach.

Uploaded files

The first dataset contains the average IEI values, generated by randomly simulating 300 x 1 hits for 10 bins per quadrant (4 quadrants in total) using the Python programming language, and calculating the corresponding IEI values. This resulted in a total of 4 x 10 x 300 x 1 = 12,000 data points. The summary of the IEI values by quadrant and bin is provided in the file results_1_300.csv. The calculation of IEI values for averages is based on likelihood, using an absolute difference-based approach for the likelihood probability computation. IEI_Likelihood_Based_Data.zip

The weighted IEI average values for likelihood calculation (Bayes formula) are provided in the file Weighted_IEI_Average_08_01_2025.xlsx

This dataset contains the results of a simulated target search experiment using Bayesian posterior updates and Imprecision Entropy Indicators (IEI). Each row represents a hit during the search process, including metrics such as Shannon entropy (H), Gini index (G), average distance, angular deviation, and calculated IEI values. The dataset also includes bin-specific posterior probability updates and likelihood calculations for each iteration. The simulation explores adaptive learning and posterior penalization strategies to optimize the search efficiency. Our Bayesian adaptive searching system source code (search algorithm, 1000 target searches): IEI_Self_Learning_08_01_2025.pyThis dataset contains the results of 1,000 iterations of a successful target search simulation. The simulation runs until the target is successfully located for each iteration. The dataset includes further three main outputs: a) Results files (results{iteration_number}.csv): Details of each hit during the search process, including entropy measures, Gini index, average distance and angle, Imprecision Entropy Indicators (IEI), coordinates, and the bin number of the hit. b) Posterior updates (Pbin_all_steps_{iter_number}.csv): Tracks the posterior probability updates for all bins during the search process acrosations multiple steps. c) Likelihoodanalysis(likelihood_analysis_{iteration_number}.csv): Contains the calculated likelihood values for each bin at every step, based on the difference between the measured IEI and pre-defined IE bin averages. IEI_Self_Learning_08_01_2025.py

Based on the mentioned Python source code (see point 3, Bayesian adaptive searching method with IEI values), we performed 1,000 successful target searches, and the outputs were saved in the:Self_learning_model_test_output.zip file.

Bayesian Search (IEI) from different quadrant. This dataset contains the results of Bayesian adaptive target search simulations, including various outputs that represent the performance and analysis of the search algorithm. The dataset includes: a) Heatmaps (Heatmap_I_Quadrant, Heatmap_II_Quadrant, Heatmap_III_Quadrant, Heatmap_IV_Quadrant): These heatmaps represent the search results and the paths taken from each quadrant during the simulations. They indicate how frequently the system selected each bin during the search process. b) Posterior Distributions (All_posteriors, Probability_distribution_posteriors_values, CDF_posteriors_values): Generated based on posterior values, these files track the posterior probability updates, including cumulative distribution functions (CDF) and probability distributions. c) Macro Summary (summary_csv_macro): This file aggregates metrics and key statistics from the simulation. It summarizes the results from the individual results.csv files. d) Heatmap Searching Method Documentation (Bayesian_Heatmap_Searching_Method_05_12_2024): This document visualizes the search algorithm's path, showing how frequently each bin was selected during the 1,000 successful target searches. e) One-Way ANOVA Analysis (Anova_analyze_dataset, One_way_Anova_analysis_results): This includes the database and SPSS calculations used to examine whether the starting quadrant influences the number of search steps required. The analysis was conducted at a 5% significance level, followed by a Games-Howell post hoc test [43] to identify which target-surrounding quadrants differed significantly in terms of the number of search steps. Results were saved in the Self_learning_model_test_results.zip

This dataset contains randomly generated sequences of bin selections (1-40) from a control search algorithm (random search) used to benchmark the performance of Bayesian-based methods. The process iteratively generates random numbers until a stopping condition is met (reaching target bins 1, 11, 21, or 31). This dataset serves as a baseline for analyzing the efficiency, randomness, and convergence of non-adaptive search strategies. The dataset includes the following: a) The Python source code of the random search algorithm. b) A file (summary_random_search.csv) containing the results of 1000 successful target hits. c) A heatmap visualizing the frequency of search steps for each bin, providing insight into the distribution of steps across the bins. Random_search.zip

This dataset contains the results of a random walk search algorithm, designed as a control mechanism to benchmark adaptive search strategies (Bayesian-based methods). The random walk operates within a defined space of 40 bins, where each bin has a set of neighboring bins. The search begins from a randomly chosen starting bin and proceeds iteratively, moving to a randomly selected neighboring bin, until one of the stopping conditions is met (bins 1, 11, 21, or 31). The dataset provides detailed records of 1,000 random walk iterations, with the following key components: a) Individual Iteration Results: Each iteration's search path is saved in a separate CSV file (random_walk_results_.csv), listing the sequence of steps taken and the corresponding bin at each step. b) Summary File: A combined summary of all iterations is available in random_walk_results_summary.csv, which aggregates the step-by-step data for all 1,000 random walks. c) Heatmap Visualization: A heatmap file is included to illustrate the frequency distribution of steps across bins, highlighting the relative visit frequencies of each bin during the random walks. d) Python Source Code: The Python script used to generate the random walk dataset is provided, allowing reproducibility and customization for further experiments. Random_walk.zip

This dataset contains the results of a genetic search algorithm implemented as a control method to benchmark adaptive Bayesian-based search strategies. The algorithm operates in a 40-bin search space with predefined target bins (1, 11, 21, 31) and evolves solutions through random initialization, selection, crossover, and mutation over 1000 successful runs. Dataset Components: a) Run Results: Individual run data is stored in separate files (genetic_algorithm_run_.csv), detailing: Generation: The generation number. Fitness: The fitness score of the solution. Steps: The path length in bins. Solution: The sequence of bins visited. b) Summary File: summary.csv consolidates the best solutions from all runs, including their fitness scores, path lengths, and sequences. c) All Steps File: summary_all_steps.csv records all bins visited during the runs for distribution analysis. d) A heatmap was also generated for the genetic search algorithm, illustrating the frequency of bins chosen during the search process as a representation of the search pathways.Genetic_search_algorithm.zip

Technical Information

The dataset files have been compressed into a standard ZIP archive using Total Commander (version 9.50). The ZIP format ensures compatibility across various operating systems and tools.

The XLSX files were created using Microsoft Excel Standard 2019 (Version 1808, Build 10416.20027)

The Python program was developed using Visual Studio Code (Version 1.96.2, user setup), with the following environment details: Commit fabd6a6b30b49f79a7aba0f2ad9df9b399473380f, built on 2024-12-19. The Electron version is 32.6, and the runtime environment includes Chromium 128.0.6263.186, Node.js 20.18.1, and V8 12.8.374.38-electron.0. The operating system is Windows NT x64 10.0.19045.

The statistical analysis included in this dataset was partially conducted using IBM SPSS Statistics, Version 29.0.1.0

The CSV files in this dataset were created following European standards, using a semicolon (;) as the delimiter instead of a comma, encoded in UTF-8 to ensure compatibility with a wide

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg

ivers. This tool (“Caregiver Balance”; https://balans.mantelzorg.nl) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs.

In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3).

Table: Study details and associated files

    Number
    Study Name
    Study Aim
    Study Type
    Type of data
    Associated Files


    1
    Perceived life balance among young adult students: a comparison between caregivers and non-caregivers
    Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData

ENTWINE_ PerceivedLifeBalanceSurvey _Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    2
    Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers
    Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData

ENTWINE_PositiveAspectsCaregiving_Survey_Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    3
    Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool
    Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.
    Usability study

Qualitative and Quantitative

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData

Description of the files to be uploaded

Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])

Short procedure conducted to receive data: online survey on Qualtrics platform

SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 74 young adult caregivers

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )

Short procedure conducted to receive data: online survey on Qualtrics platform.

SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.

Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata –

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool

Short procedure conducted to receive data: Online interviews

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])

Short procedure conducted to receive data: Online questionnaire

Data collection details

All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.

The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.

The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).

Participants digitally signed informed consent for participating in the study.

Terms of use

Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands mariet.hageboorn@umcg.nl).

Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.

The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).

(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.

Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.

References

1. Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971

2. Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940

3. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The

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 2006 Palestinian Refugee Camps, Lebanon Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Generic - To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Generic and to strengthen technical expertise in the design, implementation, and analysis of such systems.

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 surveys are typically carried out by government organizations, with the support and assistance of UNICEF 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.

Survey results

Results from the surveys, including national reports, standard sets of tabulations and micro level datasets will all be made widely available after completion of the surveys. Results from the surveys will also be made available in DevInfo format. DevInfo v5.0 is a powerful database system which has been adapted from UNICEF's ChildInfo technology to specifically monitor progress towards the Millennium Development Goals. MICS Results will also be available through UNICEF's web site dedicated to monitoring the situation of children and women at www.childinfo.org. Results of the prior round of MICS can already be found at this site.

Geographic coverage

The survey is representative and covers the whole of Palestinian refugee camps and gatherings in Lebanon.

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 sample for the Multiple Indicator Cluster Survey (MICS) in Palestinian Refugee Camps and Gatherings in Lebanon was designed to provide estimates on a large number of indicators on the situation of children and women at the geographical area and camp/gathering level, for urban and rural areas, and for 12 camps and 12 gatherings in 5 geographical areas. With this design we could monitor a large number of women and children indicators at the geographical area and camp level for urban and rural areas.

The sample population (based on the Palestinian Refugee Camps and Gatherings in Lebanon Census of 1999) was divided into equal clusters each containing 20 households (totaling 1300 clusters). Sample clusters (310 clusters, i.e. 6200 households) were drawn with uniformity, random start and a sampling fraction of 0.25.

Sampling deviation

No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three sets of questionnaires were used in the survey: 1) a household questionnaire was used to collect information on all household members, the household, and the dwelling; 2) a women’s questionnaire administered in each household to all women aged 15-49 years; 3) an under-5 questionnaire, administered to mothers or caretakers of all children under 5 living in the household.

The questionnaires included the following modules: Household Questionnaire, Household Listing, Education, Water and Sanitation Facilities, Household Background Characteristics, Child Labour, and Salt Iodization.

Questionnaire for Individual Women: Child Mortality, Tetanus Toxoid, Maternal and Newborn Health, Contraception, and - HIV/AIDS.

Questionnaire for Children Under Five: Birth Registration and Early Learning, Vitamin A, Breastfeeding, Care of Illness, Immunization, and Anthropometry.

The questionnaires are based on the MICS3 model questionnaire. Changes in format were made to the UNICEF MICS3 model Arabic version questionnaires that were pre-tested during March 2006.

Cleaning operations

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

Details of each of these steps can be found in the data processing documentation, data editing guidelines, data processing programs in CSPro and SPSS, and tabulation guidelines.

Data entry was conducted by 12 data entry operators in tow shifts, supervised by 2 data entry supervisors, using a total of 7 computers (6 data entry computers plus one supervisors computer). All data entry was conducted at the GenCenStat head office using manual data entry. For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach, that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included inthe data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses ("Other" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire.

Structure and completeness checking ensured that all questionnaires for the cluster had been entered, were structurally sound, and that women's and children's questionnaires existed for each eligible woman and child.

100% verification of all variables was performed using independent verification, i.e. double entry of data, with separate comparison of data followed by modification of one or both datasets to correct keying errors by original operators who first keyed the files.

After completion of all processing in CSPro, all individual cluster files were backed up before concatenating data together using the CSPro file concatenate utility.

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 Manual (http://www.childinfo.org/mics/mics3/manual.php)

Response rate

The response rate of households, mothers and children was remarkably high. Of the 6200 households selected for the sample, only 33 households could not be interviewed thus making the household response rate 99.5 percent.

In the interviewed households, 4001 ever married women (age 15-49) were identified. Of these, 3955 were successfully interviewed, yielding a response rate of 98.9 percent. In addition, 2431 children under age five were listed in the household questionnaire. Questionnaires were completed for 2381 of these children, which corresponds to a response rate of 97.9 percent.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling

SMaRteN, in partnership with Vitae, conducated research into the impact of COVID-19 on the working lives of doctoral researchers and research staff.SMaRteN www.smarten.org.uk

The UK Research and Innovation (UKRI) funded Student Mental Health Research Network (SMaRteN) is working to support and encourage better research into student mental health. SMaRteN is based at Institute of Psychiatry, Psychology and Neurosciences at King’s College London. Vitae is a non-profit programme supporting the professional and career development of researchers. www.vitae.ac.uk @vitae_news Covid-19 and the associated lock down has caused substantive disruption to the study and work of doctoral students and researchers in universities. The response to the pandemic has varied across universities and research funders.SMaRteN and Vitae aim to develop a national picture for how doctoral researchers and research staff have been affected by the pandemic.​The survey includes questions relating to the impact of COVID-19 on research work, mental wellbeing, social connection. We further address the impact of COVID-19 on changes to employment outside of academia, living arrangements and caring arrangements and the consequent effect of these changes on research work. The survey considers the support provided by supervisors / line managers and by universities. Please note, Time 2 (follow up) data was collected, corresponding to this survey, in September / October 2020. This can be accessed via the link at the bottom of the page.Data available here as either an SPSS or Excel download:SPSS file contains labels Excel file contains labels and brief notes about codingRecoding data for CV19 impact - SPSS Syntax file describes steps taken to code data CV19_impact_on_researchers - word document, export from Qualtrics of the survey. Qualitative data - Benefits and Challenges of the lockdown. Respondents gave qualitative responses to a number of questions. We are slowly cleaning these up to get them fully anonymised. At the moment, we have only done this for the sample (approximately 1,000) of responses that have been analysed to look at the benefits and challenges of the lockdown.Please note, data has been removed from this data set to ensure participant anonymity.For further information, please contact Dr Nicola Byrom - nicola.byrom@kcl.ac.uk

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2010 (LFS). The survey rounds covered a total sample of about 31,179 households, and the number of completed questionaire is 27,510, which amounts to a sample of around 116,439 individuals aged 10 years and over, including 95,067 individuals in the working-age population 15 years and above.

The importance of this survey lies in that it focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

The survey main objectives are: - To estimate the labor force and its percentage to the population. - To estimate the number of employed individuals. - To analyze labour force according to gender, employment status, educational level, occupation and economic activity. - To provide information about the main changes in the labour market structure and its socio economic characteristics. - To estimate the numbers of unemployed individuals and analyze their general characteristics. - To estimate the rate of working hours and wages for employed individuals in addition to analyze of other characteristics.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: All Palestinians aged 10 years and over living in the Palestinian Territory, excluding persons living in institutions such as prisons or shelters.

---> Sampling Frame: The sampling frame consisted of a master sample of enumeration areas (EAs) selected from the population housing and establishment census 2007, the master sample consists of area units of relatively equal size (number of households), these units have been used as primary sampling units (PSUs).

---> Sample Design The sample is a two-stage stratified cluster random sample.

---> Stratification: Four levels of stratification were made: 1. Stratification by Governorates. 2. Stratification by type of locality which comprises: (a) Urban (b) Rural (c) Refugee Camps 3. Stratification by classifying localities, excluding governorate centers, into three strata based on the ownership of households of durable goods within these localities. 4. Stratification by size of locality (number of households).

---> Sample Size: The sample size was about 7,770 households in the 56th round and 7,818 households in the 57th round, and 7,819 households in the 58th round and 7,772 households in the 59th round. The total number of the households was about 31,179 households. The number of completed questionnaires was about 27,514 questionnaires and this was considered appropriate to provide estimations on main labour force characteristics in the Palestinian Territory. The sample size in 1st quarter, 2010 consisted of 7,770 households, which included 29,999 persons aged 10 years and over (including 24,395 aged 15 years and over). In the 2nd quarter, the sample consisted of 7,818 households, which included 29,483 persons aged 10 years and over (including 24,118 aged 15 years and over). In the 3rd quarter, the sample consisted of 7,819 households, which included 28,479 persons aged 10 years and over (including 23,260 aged 15 years and over). In the 4th quarter the sample consisted of 7,772 households; which included 28,478 persons aged 10 years and over (including 23,288 aged 15 years and over).

---> Sample Rotation: Each round of the Labor Force Survey covers all the 481 master sample areas. Basically, the areas remain fixed over time, but households in 50% of the EAs are replaced each round. The same household remains in the sample over 2 consecutive rounds, rests for the next two rounds and represented again in the sample for another and last two consecutive rounds before it is dropped from the sample. A 50 % overlap is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes). In earlier applications of the LFS (rounds 1 to 11); the rotation pattern used was different; requiring a household to remain in the sample for six consecutive rounds, then dropped. The objective of such a pattern was to increase the overlap between consecutive rounds. The new rotation pattern was introduced to reduce the burden on the households resulting from visiting the same household for six consecutive times.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data Data editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data files

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Errors due to non-response because households were away from home or refused to participate. The overall non response rate amounted to almost 11.8% which is relatively low; a much higher rates is rather common in an international perspective. The refusal rate was only 1.1% and the coverage rate was only 4.7%. It is difficult; however, to assess the amount of bias resulting from non response. PCBS has not yet undertaken any non-response study. Such a study may indicate, that non-response is more frequent in some population groups than in others. This is rather normal and such information is necessary to be able to compensate for bias resulting from non-response errors.

Sampling error estimates

---> Statistical Errors Since the data reported here are based on a sample survey and not on a complete enumeration, they are subjected to sampling errors as well as non-sampling errors. Sampling errors are random outcomes of the sample design, and are, therefore, in principle measurable by the statistical concept of standard error. A description of the estimated standard errors and the effects of the sample design on sampling errors are provided in the previous chapter. Data of this survey affected by statistical errors due to use the sample, Therefore, the emergence of certain differences from the real values expect obtained through censuses. It had been calculated variation of the most important indicators exists and the facility with the report and the dissemination levels of the data

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2014 (LFS). The survey rounds covered a total sample of about 25,736 households, and the number of completed questionaire is 16,891.

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2014.

---> Sampling Frame: The sampling frame consists of the master sample, which was updated in 2011: each enumeration area consists of buildings and housing units with an average of about 124 households. The master sample consists of 596 enumeration areas; we used 498 enumeration areas as a framework for the labor force survey sample in 2014 and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 7,616 households in each quarter of 2014, but in the second quarter 2014 only 7,541 households were collected, where 75 households couldn't be collected in Gaza Strip because of the Israeli aggression.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 494 enumeration areas for the whole round ,and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 494 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2013 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 30,464 households of which 25,736 households completed the interview; whereas 16,891 households from the West Bank and 8,845 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 88.8% while in the Gaza Strip it reached 93.3%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total