BackgroundNational Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources.Methods and findingsThis paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969–2014) of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal’s category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care.ConclusionsStudies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200–300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds.

The levels are compared using 4 measurement-related classes: resolution, property, mathematical operators, and central tendency [2].

Cronbach’s alpha (coefficient α) is the conventional statistic communication scholars use to estimate the reliability of multi-item measurement instruments. For many, if not most communication measures, α should not be calculated for reliability estimation. Instead, coefficient omega (ω) should be reported as it aligns with the definition of reliability itself. In this primer, we review α and ω, and explain why ω should be the new ‘gold standard’ in reliability estimation. Using Mplus, we demonstrate how ω is calculated on an available data set and show how preliminary scales can be revised with ‘ω if item deleted.’ We also list several easy-to-use resources to calculate ω in other software programs. Communication researchers should routinely report ω instead of α.

This data is associated with the paper O’Grady, J.G., McInnes, K.L., Hemer, M. A., Hoeke, R. K., Stephenson, A., and Colberg, F. (in press), "Extreme Water Levels for Australian Beaches using Empirical Equations for Shoreline Wave Setup", Journal of Geophysical Research: Oceans.

Understanding how high ocean water levels can reach up the coast is important for designing coastal protection from coastal inundation and erosion. This is particularly important as climate change affects wind and weather conditions and sea-level rise with the subsequent modification to the occurrence of the largest storm-driven water levels. While the height of storm-driven water levels are well understood for protected harbours and estuaries, new research is providing estimates of how high water levels can reach for coastlines exposed to dangerous wave/surf conditions. This study uses mathematical model simulations spanning ~30 years of historical water levels and ocean waves. Statistical analysis is performed to determine how high the largest storm events will likely reach on natural sandy beaches directly exposed to large wave/surf conditions.

The data comprises Gumbel distribution parameters from regression fitting to the hindcast model data. The file ST_rGUM_25m_sta.1981-2013.nc is for the storm-tide SWL heights from the ROMS storm surge hindcast. The file SU_GT81_rGUM_25m_sta.1981-2013.nc is for wave setup calculated with the Guza, R. T., & Thornton 1981 method.
The file SU_GT81_ST_rGUM_25m_sta.1981-2013.nc is for the time-series combined storm-tide and wave setup.
Notes: 1) The data datum is relative to the model bathymetry mean sea level (Geoscience Australia’s 2009 250m dataset). Haigh corrected their dataset of storm tide to AHD by comparing modelled 1-year ARI to the tide gauge measurements. “The predicted levels have been artificially adjusted so that the 1-year return period levels exactly match those of the measured estimates at each site. This was done because the predicted water levels are relative to MSL, whereas the measured levels are relative to AHD. Around mainland Australia, AHD was defined using MSL records between 1966 and 1968 at 30 sites and hence differs from present day MSL. Around Tasmania, AHD was defined using two records from 1972.” 2) To convert to AHD, the netcdf file ‘ST_rGUM_25m_sta.1981-2013.nc’ has a variable ‘toAHD’, you will need to add this onto the location parameter ‘mu’. Alternatively add it to the predicted return levels. 3) Wave setup is really only valid for open coastlines exposed to waves, so be careful applying it in estuaries. Lineage: Created with R's ismev Gumbel function on selected datasets (ROMS storm surge hindcast, CAWCR wave hindcast, and combined data).

Descriptive statistics of measured variables in each experimental condition along with appropriate tests (ANOVA or chi-square, depending on measurement level) to verify differences between the three conditions in means/frequencies.

IDWR maintains a groundwater level database containing data primarily collected by IDWR, but also includes data gathered by the USGS, USBR, and other public and private entities. Please reach out to these other entities to obtain their full complete record, as not all values are present in this database (IDWR can provide a full list of data contributors upon request). IDWR staff manually measure the "depth to water" in wells throughout Idaho. Pressure transducers in many wells provide near-continuous water level measurements. IDWR strives to create complete and accurate data and may revise these data when indicated.

“Groundwater Level Data: All Historic Data” includes all well data managed in IDWR’s internal database, regardless of current well status. For example, historic data from discontinued, abandoned, or inactive wells are contained in this dataset. IDWR’s water level data are also hosted in the Groundwater Data Portal (https://idwr-groundwater-data.idaho.gov/), which displays only actively monitored wells.

The three files included in this download are 1) discrete (manual) depth to water measurements 2) continuous* (pressure transducer) depth to water measurements, and 3) the associated well metadata.

*The continuous measurements data have been condensed to display only the shallowest daily pressure transducer measurements. Complete datasets are available upon request.

Overview   This dataset provides the measurements of raw water storage levels in reservoirs crucial for public water supply, The reservoirs included in this dataset are natural bodies of water that have been dammed to store untreated water.    Key Definitions   Aggregation  The process of summarizing or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.    Capacity The maximum volume of water a reservoir can hold above the natural level of the surrounding land, with thresholds for regulation at 10,000 cubic meters in England, Wales and Northern Ireland and a modified threshold of 25,000 cubic meters in Scotland pending full implementation of the Reservoirs (Scotland) Act 2011. Current Level The present volume of water held in a reservoir measured above a set baseline crucial for safety and regulatory compliance. Current Percentage The current water volume in a reservoir as a percentage of its total capacity, indicating how full the reservoir is at any given time. Dataset  Structured and organized collection of related elements, often stored digitally, used for analysis and interpretation in various fields.   Granularity  Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours  ID  Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.   Open Data Triage  The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.   Reservoir Large natural lake used for storing raw water intended for human consumption. Its volume is measurable, allowing for careful management and monitoring to meet demand for clean, safe water. Reservoir Type The classification of a reservoir based on the method of construction, the purpose it serves or the source of water it stores. Schema  Structure for organizing and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.   Units  Standard measurements used to quantify and compare different physical quantities.     Data History   Data Origin   Reservoir level data is sourced from water companies who may also update this information on their website and government publications such as the Water situation reports provided by the UK government. Data Triage Considerations  Identification of Critical Infrastructure Special attention is given to safeguard data on essential reservoirs in line with the National Infrastructure Act, to mitigate security risks and ensure resilience of public water systems. Currently, it is agreed that only reservoirs with a location already available in the public domain are included in this dataset. Commercial Risks and Anonymisation The risk of personal information exposure is minimal to none since the data concerns reservoir levels, which are not linked to individuals or households. Data Freshness It is not currently possible to make the dataset live. Some companies have digital monitoring, and some are measuring reservoir levels analogically. This dataset may not be used to determine reservoir level in place of visual checks where these are advised. Data Triage Review Frequency   Annually unless otherwise requested  Data Specifications  Data specifications define what is included and excluded in the dataset to maintain clarity and focus. For this dataset: Each dataset covers measurements taken by the publisher. This dataset is published periodically in line with the publisher’s capabilities Historical datasets may be provided for comparison but are not required The location data provided may be a point from anywhere within the body of water or on its boundary. Reservoirs included in the dataset must be: Open bodies of water used to store raw/untreated water Filled naturally Measurable Contain water that may go on to be used for public supply Context  This dataset must not be used to determine the implementation of low supply or high supply measures such as hose pipe bans being put in place or removed. Please await guidance from your water supplier regarding any changes required to your usage of water. Particularly high or low reservoir levels may be considered normal or as expected given the season or recent weather. This dataset does not remove the requirement for visual checks on reservoir level that are in place for caving/pot holing safety. Some water companies calculate the capacity of reservoirs differently than others. The capacity can mean the useable volume of the reservoir or the overall volume that can be held in the reservoir including water below the water table. Data Publish Frequency   Annually

Abstract This paper aims to assess the use of Exploratory Factor Analysis by Production and Operations researchers, discussing the adequacy of its application. We analyzed 97 papers published between 2010 and 2015 in the Production and Operations area -- of which 61 and 36 were published in international and Brazilian journals, respectively. These papers contain 140 different applications of Factor Analysis. The research shows that confirmatory techniques are prevalent in international papers, as well as exploratory techniques to evaluate the problem of common method bias. Conversely, the papers in Brazilian journals typically use the exploratory technique in more traditional ways, such as to confirm the unidimensionality of the construct, or still to generate scores for use in other statistical techniques. Despite the textbooks for the AFE teaching focus exclusively on the use of AFE in the exploratory mode (to identify the number and meaning of the common factors), this use has been less frequent in published articles, both national and international. Moreover, the research shows that the inappropriate use of exploratory (rather than confirmatory) factor analysis in four Brazilian papers resulted in the “destruction of theory”. These findings suggest that national research have been using exploratory factor analysis in a questionable way; in this sense we propose scholars discuss this topic in order to disseminate the good practices.

Overview: This is a large-scale dataset with impedance and signal loss data recorded on volunteer test subjects using low-voltage alternate current sine-shaped signals. The signal frequencies are from 50 kHz to 20 MHz.

Applications: The intention of this dataset is to allow to investigate the human body as a signal propagation medium, and capture information related to how the properties of the human body (age, sex, composition etc.), the measurement locations, and the signal frequencies impact the signal loss over the human body.

Overview statistics:

Number of subjects: 30

Number of transmitter locations: 6

Number of receiver locations: 6

Number of measurement frequencies: 19

Input voltage: 1 V

Load resistance: 50 ohm and 1 megaohm

Measurement group statistics:

Height: 174.10 (7.15)

Weight: 72.85 (16.26)

BMI: 23.94 (4.70)

Body fat %: 21.53 (7.55)

Age group: 29.00 (11.25)

Male/female ratio: 50%

Included files:

experiment_protocol_description.docx - protocol used in the experiments

electrode_placement_schematic.png - schematic of placement locations

electrode_placement_photo.jpg - visualization on the experiment, on a volunteer subject

RawData - the full measurement results and experiment info sheets

all_measurements.csv - the most important results extracted to .csv

all_measurements_filtered.csv - same, but after z-score filtering

all_measurements_by_freq.csv - the most important results extracted to .csv, single frequency per row

all_measurements_by_freq_filtered.csv - same, but after z-score filtering

summary_of_subjects.csv - key statistics on the subjects from the experiment info sheets

process_json_files.py - script that creates .csv from the raw data

filter_results.py - outlier removal based on z-score

plot_sample_curves.py - visualization of a randomly selected measurement result subset

plot_measurement_group.py - visualization of the measurement group

CSV file columns:

subject_id - participant's random unique ID

experiment_id - measurement session's number for the participant

height - participant's height, cm

weight - participant's weight, kg

BMI - body mass index, computed from the valued above

body_fat_% - body fat composition, as measured by bioimpedance scales

age_group - age rounded to 10 years, e.g. 20, 30, 40 etc.

male - 1 if male, 0 if female

tx_point - transmitter point number

rx_point - receiver point number

distance - distance, in relative units, between the tx and rx points. Not scaled in terms of participant's height and limb lengths!

tx_point_fat_level - transmitter point location's average fat content metric. Not scaled for each participant individually.

rx_point_fat_level - receiver point location's average fat content metric. Not scaled for each participant individually.

total_fat_level - sum of rx and tx fat levels

bias - constant term to simplify data analytics, always equal to 1.0

CSV file columns, frequency-specific:

tx_abs_Z_... - transmitter-side impedance, as computed by the process_json_files.py script from the voltage drop

rx_gain_50_f_... - experimentally measured gain on the receiver, in dB, using 50 ohm load impedance

rx_gain_1M_f_... - experimentally measured gain on the receiver, in dB, using 1 megaohm load impedance

Acknowledgments: The dataset collection was funded by the Latvian Council of Science, project “Body-Coupled Communication for Body Area Networks”, project No. lzp-2020/1-0358.

References: For a more detailed information, see this article: J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, and A. Elsts. Dataset on the Human Body as a Signal Propagation Medium for Body Coupled Communication. Submitted to Elsevier Data in Brief, 2023.

Contact information: info@edi.lv

Scales are collected annually from smolt trapping operations in Maine as wellas other sampling opportunities (e.g. marine surveys, fishery sampling etc.). Scale samples are imaged and age, origin, and measurement data are collected as needed for specific growth-related research.

This part of the data release contains the water-level measurement data compiled and synthesized from various sources. This compilation includes two tables that contain all the water-level measurements that were considered in the development of the groundwater-level altitude maps (Input_VisGWDB), and a table of median-water-level data that were used to develop the groundwater-level altitude maps (MedianWaterLevelData). Also included in this part of the data release is a geologic unit code look-up table which defines the geologic units that wells are reported to be screened in for wells with water-level measurements. These digital data accompany Houston, N.A., Thomas, J.V., Foster, L.K., Pedraza, D.E., and Welborn, T.L., 2020, Hydrogeologic framework, groundwater-level altitudes, groundwater-level changes, and groundwater-storage changes in selected alluvial basins in the upper Rio Grande focus area study, Colorado, New Mexico, and Texas, U.S. and Chihuahua, Mexico, 1980 to 2015

Statistics illustrates consumption, production, prices, and trade of Instruments and Apparatus for Measuring or Checking The Flow or Level of Liquids in Georgia from 2007 to 2024.

Statistics illustrates consumption, production, prices, and trade of Instruments and Apparatus for Measuring or Checking The Flow or Level of Liquids in Estonia from 2007 to 2024.

This dataset provides the measurements of raw water storage levels in reservoirs crucial for public water supply, The reservoirs included in this dataset are natural bodies of water that have been dammed to store untreated water.This dataset must not be used to determine the implementation of low supply or high supply measures such as hose pipe bans being put in place or removed. Please await guidance from your water supplier regarding any changes required to your usage of water.Particularly high or low reservoir levels may be considered normal or as expected given the season or recent weather.This dataset does not remove the requirement for visual checks on reservoir levels that are in place for caving/pot holing safety.Some water companies calculate the capacity of reservoirs differently than others. The capacity can mean the useable volume of the reservoir or the overall volume that can be held in the reservoir including water below the water table.

The recent availability of digital traces generated by cellphone calls has significantly increased the scientific understanding of human mobility. Until now, however, based on low time resolution measurements, previous works have ignored to study human mobility under various time scales due to sparse and irregular calls, particularly in the era of mobile Internet. In this paper, we introduced Mobile Flow Records, flow-level data access records of online activity of smartphone users, to explore human mobility. Mobile Flow Records collect high-resolution information of large populations. By exploiting this kind of data, we show the models and statistics of human mobility at a large-scale (3,542,235 individuals) and finer-granularity (7.5min). Next, we investigated statistical variations and biases of mobility models caused by different time scales (from 7.5min to 32h), and found that the time scale does influence the mobility model, which indicates a deep coupling of human mobility and time. We further show that mobility behaviors like transportation modes contribute to the diversity of human mobility, by exploring several novel and refined features (e.g., motion speed, duration, and trajectory distance). Particularly, we point out that 2-hour sampling adopted in previous works is insufficient to study detailed motion behaviors. Our work not only offers a macroscopic and microscopic view of spatial-temporal human mobility, but also applies previously unavailable features, both of which are beneficial to the studies on phenomena driven by human mobility.

This research project was about developing and validating a new scale, named the Romance Quotient (RQ), which aimed to measure varying levels of romantic traits. Individuals who were 18-years-old or above and with English literacy were recruited online to complete a survey. The sample size is 812; this is the number of cases in the dataset "FinalData_RQ_SubApril2025". All variables' information is available in the dataset.

The data set contains the measurement data of about 400 level measuring points, which continuously measure the water level of surface waters (without the Elbe). They have a fine resolution (5 minutes cycle time) to correctly capture the apex of a tidal wave The data transmission takes place at about 100 of these measuring points daily online / via dial-up, with the other 300 the data is recorded by a data collector and collected regularly. The interactive map shows the locations of the measuring points. References to the respective measurement data of the level levels are stored at each measuring point. The download service provides the locations of the level measuring points and the references to the measurement data files in machine-readable form.

Town: this field indicates the town where the sensor is located. County: this field indicates the county where the sensor is located. Street_Name: this field indicates the name of the street where the sensor is located. Sensor_ID: this field contains an unique ID for the device that was used to capture the data. Latitude: this field contains the latitude of the device that was used to capture the data. Longitude: this field contains the longitude of the device that was used to capture the data. Date: this field contains the date of each given record (yyyy-mm-dd) Time: this field contains the timestamp of each record. Measure: this field describes the environmental parameter being measured. Units: this fields specifies the unit of measurement. Value: this field contains the level of the environmental parameter being measured. Year: this field contains the year in which the record was captured. Week_Year: this field contains the number of the week of the year in which the record was captured. Please note that: Records start on the 17th of January 2022. Records are captured every 10 minutes approximately. The name of each table contains the year and the number of the week in which the corresponding records were captured. Any gaps in the time series are due to sensor’s malfunctioning.

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Alongside these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs. In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH -BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labour Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made. The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank. The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Management Team, made up of two professionals from each of the three statistical organizations. The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labour) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analysed data.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war. At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected. Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.] The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample. The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated.

(b) SAMPLE DESIGN For reasons of funding, the smaller option proposed by the team was used, or Option B. Stratification of Municipalities The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame. Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed, and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure. However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue).

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY

An integrated approach to data entry and fieldwork was adopted in Bosnia and Herzegovina. Data entry proceeded side by side with data gathering to ensure verification and correction in the field. Data entry stations were located in the regional offices of the entity institutes and were equipped with computers, modem and a dedicated telephone line. The completed questionnaires were delivered to these stations each day for data entry. Twenty data entry operators (10 from Federation and 10 from RS) were trained in two training sessions held for a week each in Sarajevo and Banja Luka. The trainers were the staff of the two entity institutes who had undergone training in the CSPro software earlier and had participated in the workshops of the Pilot survey. Prior to the training, laptop computers were provided to the entity institutes, and the CSPro software was installed in them. The training for the data entry operators covered the following elements:

• Introduction to the LSMS Survey questionnaire; Introduction to the personal computers/ lap top computers; Copying data on diskette and printing of output
• The Data entry programme (CSPro). Understanding of the Round 1 data entry screens (Modules 1-10)
• Practice of Round 1 (data entry trainees enter questionnaires completed by interviewer trainees during practice interviews)
• Understanding of Round 2 Data entry screen (Modules 11-13)
• Practice of Round 2 Data entry screens (data entry trainees entered the questionnaires completed by interviewer trainees)
• Control Procedures; Copying