The biostatistics software market is experiencing robust growth, driven by the increasing adoption of data-driven approaches in pharmaceutical research, clinical trials, and academic studies. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume of complex biological data necessitates sophisticated software solutions for analysis and interpretation. Secondly, advancements in machine learning and artificial intelligence are enhancing the capabilities of biostatistics software, enabling more accurate and efficient data processing. Thirdly, regulatory pressures demanding robust data analysis in the pharmaceutical and healthcare sectors are boosting demand for validated and compliant biostatistics tools. The market is segmented by software type (general-purpose versus specialized) and end-user (pharmaceutical companies, academic institutions, and others). Pharmaceutical companies represent a significant portion of the market due to their extensive reliance on clinical trial data analysis. However, the academic and research segments are also exhibiting strong growth due to increased research activities and funding. Geographically, North America and Europe currently dominate the market, but Asia-Pacific is expected to witness substantial growth in the coming years due to increasing healthcare spending and technological advancements in the region. The competitive landscape is characterized by a mix of established players offering comprehensive suites and specialized niche vendors. While leading players like IBM SPSS Statistics and Minitab enjoy significant market share based on their brand recognition and established user bases, smaller companies specializing in specific statistical methods or user interfaces are gaining traction by catering to niche demands. This competitive dynamic will likely drive innovation and further segmentation within the market, resulting in specialized software offerings tailored to particular research areas and user requirements. The challenges the market faces include the high cost of software licensing, the need for specialized training for effective utilization, and the potential integration complexities with existing data management systems. However, the overall growth trajectory remains positive, driven by the inherent need for sophisticated biostatistical analysis in various sectors.

Producing a table that is not only yummy, but easy to digest! We'll review a few SPSS basics and talk about table interpretation, with a statistical test thrown in for fun. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-219.)

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

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

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

This study explores the multifaceted meanings of food and how they vary across the United States, China, and India. The research examines self-identity, social, and cultural dimensions of food and measures them using the FOODSCAPE scale. An online survey was used to gather data and MANCOVA analysis found that meanings associated with food vary between countries but many patterns emerged. We have deposited clean data in SPSS format, an Excel table mapping the survey questions to the SPSS variables, and the methodology section of the paper.

IntroductionOver-the-counter (OTC) medications are those obtained without a medical prescription from a healthcare professional. With the increasing availability of information from various sources, including social media, pharmacy students may be exposed to unreliable or inaccurate data. Incorrect medication use is particularly concerning due to its potential risk of causing adverse health effects.” Hence, this study aims to determine students’ knowledge and attitudes at Taif University’s pharmacy college.MethodsThis research utilized a cross-sectional online questionnaire-based study, employing data from a sample of 450 pharmacy students from Taif University in Saudi Arabia. Descriptive analysis included descriptive and differential analysis. The data were analyzed using statistical package for social sciences (SPSS) Version 27.ResultsThe majority of participants, 297 (88.2%), were aware that inappropriate use of over-the-counter medications might have negative implications. A total of 233 participants (51.8%) reported having previously used an OTC medication. Also, 293 (65.1%), were aware that using OTC medications beyond their expiration date was harmful. A total of 280 participants (62.2%) had a high knowledge of OTC medication, whereas 170 respondents (37.8%) had a low level of knowledge. A significant correlation was found between age, year of study, and the use of OTC medication p-values  0.05).ConclusionThe study found positive attitudes toward OTC medications. Due to increased pharmaceutical exposure and self-medication, upper-year students and OTC course graduates comprehend OTC medications better. The examination found safety protocol violations in expiration dates, prescription label interpretation, and storage. Therefore, the study provides useful information for future attempts. Also, this study may contribute to the literature and guide future research to fill knowledge gaps.

BackgroundThis study evaluates the diagnostic performance of three multimodal large language models (LLMs)—ChatGPT-4o, Gemini 2.0, and Claude 3.5—in identifying pneumothorax from chest radiographs.MethodsIn this retrospective analysis, 172 pneumothorax cases (148 patients aged >12 years, 24 patients aged ≤12 years) with both chest radiographs and confirmatory thoracic CT were included from a tertiary emergency department. Patients were categorized by age and pneumothorax size (small/large). Each radiograph was presented to all three LLMs accompanied by basic symptoms (dyspnea or chest pain), with each model analyzing each image three times. Diagnostic accuracy was evaluated using overall accuracy (all three responses correct), strict accuracy (≥2 responses correct), and ideal accuracy (≥1 response correct), alongside response consistency assessment using Fleiss’ Kappa.ResultsIn patients older than 12 years, ChatGPT-4o demonstrated the highest overall accuracy (69.6%), followed by Claude 3.5 (64.9%) and Gemini 2.0 (57.4%). Performance was significantly poorer in pediatric patients across all models (20.8%, 12.5%, and 20.8%, respectively). For large pneumothorax in adults, ChatGPT-4o showed significantly higher accuracy compared to small pneumothorax (81.6% vs. 42.2%; p 

RESEARCH HYPOTHESIS: Tannery workers have more respiratory problems and lung function impairments than the control group (Local community peoples). CURRENT FINDINGS: Our current findings endorse the null hypothesis statement. However, physician-diagnosed asthma was reported less among tannery workers than the control group. NATURE OF ATTACHED RAW DATA: The data was gathered through a questionnaire and a device of spirometry from the tannery workers and the control group. The raw data comprises information i.e. demographic factors and respiratory symptoms and lungs volume information of the participants. INTERPRETATION of DATA: Data was interpreted after analysis through SPSS (version 25) software. USE OF DATA: Data can be used to understand the respiratory as well as lung function status of the participants in the working and non working environment.

In the dissertation titled "Interactions with the Incorporeal in the Mississippian and Ancestral Puebloan Worlds," the author analyzed the Hawikku and Kechiba:wa Mortuary Data: Inhumation Body Treatment data set in an examination of the performance of mortuary ritual at Protohistoric period Zuni villages. The analysis of body treatment and the larger consideration of mortuary ritual were designed to understand the identities of the spirits of the dead in Mississippian period villages of the Georgia Coast and in Protohistoric era Zuni villages. Please see the dissertation for details about the analysis and analysis procedures: http://core.tdar.org/project/380979.

The Hawikku and Kechiba:wa Inhumation Body Treatment data set was analyzed to evaluate whether particular body treatments selectively memorialized elect members of the dead, or body treatment memorialized nearly all the dead uniformly. The data were used in a multiple correspondence analysis (MCA), which helps to characterize the relative differentiation and/or uniformity of body treatments. MCA is a multivariate statistical procedure that places cases (e.g., burials) with similar attributes (e.g., body treatment attributes) close to each other in a low-dimensional space; it places cases with different attributes far apart from each other in this space.

The Hawikku and Kechiba:wa inhumation body treatment analysis and MCA are presented in the dissertation's Chapter 6 "Prehispanic Ancestral Spirits of Hawikku and Kechiba:wa." The graphical results of the MCA are presented in Figures 6.7, 6.8, 6.9, 6.10, and 6.11.

The Hawikku and Kechiba:wa Inhumation Body Treatment data were extracted for analysis from the study's primary aggregated mortuary data set, available at the following URL: http://core.tdar.org/dataset/380985. These data include all cases that were used in the study's analysis. In addition, it includes all body treatment variables and the treatment (variable) attributes used in the MCA.

The Hawikku and Kechiba:wa inhumation body treatment data set that is curated here contains two data sheets: 1) the raw data used in the MCA, and 2) the resulting metrics from the MCA. The raw data record individual body treatment variables (e.g., articulation, body position, posture, orientation, etc.), with multiple categorical variable states (i.e., attributes) for each variable. These data were passed into the SPSS 20 MCA algorithm to create a graphical representation of the relative similarity and/or differences in body treatment among individual sets of remains. Please see the dissertation's Figures 6.7, 6.8, 6.9, 6.10, and 6.11.

The resulting metrics from the inhumation body treatment MCA contain data that pertain to the production of the MCA graphical space and to the additional analysis/interpretation of that space. Foremost, this data sheet contains each burial's MCA object score (i.e., each burial's coordinates for placement in the two-dimensional space). Second, it contains each burial's k-means cluster assignment (if applicable) within the coordinate space. Finally, it contains additional demographic and attribute data that may be useful for further exploration of mortuary patterning in the MCA space.

Abstract (en): Comparative Cities is a teaching package designed to introduce students to analysis of manuscript schedules of the nineteenth century census for social, urban, family, and demographic history. The files are designed for use with SPSS. It was initially developed at Brown University with assistance of a project grant from the National Endowment for the Humanities. The file is organized to illustrate contrasts among cities at different stages of industrialization and the demographic transition in Europe and America: Pisa, Italy (1841), Amiens, France (1851), Stockport, England (1841 and 1851), and Providence, R.I. (1850, 1865, and 1880). The rural district around Pisa and part of Providence County are also included. There are approximately 1400 cases with information for individuals in each of eleven subfiles. These are random samples from the original 1:10 house samples for the four places made to permit flexible and economical student use. Summaries imbedded in the file permit analysis at the individual, household, or nuclear unit level. There are 142 variables for each individual. The package also contains a coursebook with explanation of each variable, a dictionary with occupational titles that appear in the censuses, course syllabus, and other instructions for use. The files are being used in the separate ongoing research of the two principal investigators and should be used for instructional purposes only. This teaching package can be supplied as two card-image data files, two files of SPSS instruction cards, and associated printed documentation. The package has also been updated with several files designed to be used with microcomputers. Included in the updated materials are four text files (Contents of Tape, Coursebook, Explanatory Materials, and Dictionary of Occupational Titles and Codes), a file of SPSSx data definition statements for use with PC-SPSSx, and a file of data definition statements for use with the Consortium's ABC statistical analysis package. Nine separate sub-files, each derived from the original census data and designed for analysis on micro-computers which are equipped with PC-SPSSx or ABC, are also provided. Finally, the package includes two mainframe SPSSx "Export" files which contain all of the data collected for each city. While these latter files duplicate the SPSS files contained in the earlier Comparative Cities package, they have been modified for use with SPSSx. The original Comparative Cities Teaching Package files can still be supplied as well. These files are oriented towards use of SPSS Version 9 on mainframe computers. 2006-01-12 All files were removed from dataset 20 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 20 and flagged as study-level files, so that they will accompany all downloads.

The dataset was generated from a laboratory experiment based on the dot-matrix integration paradigm, designed to measure death thought accessibility (DTA). The study was conducted under controlled conditions, with participants tested individually in a quiet, dimly lit room. Stimulus presentation and response collection were implemented using PsychoPy (exact version number provided in the supplementary materials), and reaction times were recorded via a standard USB keyboard. Experimental stimuli consisted of five categories of two-character Chinese words rendered in dot-matrix form: death-related words, metaphorical-death words, positive words, neutral words, and meaningless words. Stimuli were centrally displayed on the screen, with presentation durations and inter-stimulus intervals (ISI) precisely controlled at the millisecond level.Data collection took place in spring 2025, with a total of 39 participants contributing approximately 16,699 valid trials. Each trial-level record includes participant ID, priming condition (0 = neutral priming, 1 = mortality salience priming), word type, inter-stimulus interval (in milliseconds), reaction time (in milliseconds), and recognition accuracy (0 = incorrect, 1 = correct). In the dataset, rows correspond to single trials and columns represent experimental variables. Reaction times were measured in milliseconds and later log-transformed for statistical analyses to reduce skewness. Accuracy was coded as a binary variable indicating correct recognition.Data preprocessing included the removal of extreme reaction times (less than 150 ms or greater than 3000 ms). Only trials with valid responses were retained for analysis. Missing data were minimal (<1% of all trials), primarily due to occasional non-responses by participants, and are explicitly marked in the dataset. Potential sources of error include natural individual variability in reaction times and minor recording fluctuations from input devices, which are within the millisecond range and do not affect overall patterns.The data files are stored in Excel format (.xlsx), with each participant’s data saved in a separate file named according to the participant ID. Within each file, the first row contains variable names, and subsequent rows record trial-level observations, allowing for straightforward data access and processing. Excel files are compatible with a wide range of statistical software, including R, Python, SPSS, and Microsoft Excel, and no additional software is required to open them. A supplementary documentation file accompanies the dataset, providing detailed explanations of all variables and data processing steps. A complete codebook of variable definitions is included in the appendix to facilitate data interpretation and ensure reproducibility of the analyses.

Background: Evidence-based practice (EBP) is a critical approach in healthcare that integrates the best available research evidence with clinical expertise and patient values. In physiotherapy, EBP ensures that treatments are effective and based on scientific evidence, leading to better patient outcomes. This study investigates the impact of knowledge, attitude, and practice towards evidence-based practice among postgraduate physiotherapy students in north India.

Material & Methods: An online survey study recruited 166 Postgraduate Physiotherapy students through convenient sampling. The study employed a self-developed questionnaire which experts validated. This questionnaire is used for data collection which included 2 domains i.e. knowledge, and attitude, and included 14 questions. Data analysis was done by using SPSS Software.

Result: The study found that most postgraduate physiotherapy students in North India are well-informed about evidence-based practice (EBP). Specifically, 89.2% use standardized treatments, 90.4% believe in adhering to EBP, 86.7% are aware of relevant databases, 84.3% understand different research designs, 88% grasp statistical interpretations of treatments, and 90.4% keep up with research. However, a small portion of students do not engage in these practices. The study found positive trends in students prioritizing evidence-based practices (88%) and adapting treatment plans (86.1%). However, knowledge gaps existed (e.g., assessing information quality: 86.1%) and updating knowledge was infrequent (81.9%). Additionally, 14.5% struggled to integrate information from various sources.

Conclusion: The study found that postgraduate physiotherapy students in North India are well-versed in and consistently practice evidence-based practice (EBP). This reflects their strong EBP knowledge, positive attitudes, and application in clinical settings, indicating a commitment to quality healthcare. However, ongoing efforts are necessary to maintain and enhance these practices.

Keywords: evidence-based practice; postgraduate; physiotherapy

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets

Abstract

The Kenya National Micronutrients Survey (NMS) 2011 was the first NMS to be carried by the Kenya National Bureau of Statistics. The purpose of this survey is to ensure the quality of HIV testing and the interpretation of results, both in the laboratory and in the community. Fort HIV testing, it is extremely important that "the correct results go to the right client". The identity of clients and the labelling of test devices should therefore be preserved properly.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered household members (usual residents), womens questinnaire( aged 15-49 years) resident in the household, children( aged 0-6-49months), School age children (aged 5-14 years) resident in the household and Men questionnire (aged 15-54 year).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size estimation The sample size required for each stratum was based on the estimated prevalence for each nutritional indicator, the desired precision for each indicator, an assumed design effect of 2.0, and a non-response of 10% (including refusals) at the household level and 10% at the individual levels for children 6-59 months of age and non-pregnant women. An additional non-response rate of 10% (for a total 30% non-response rate) was assumed for the men and SAC 5-14 years old.

Sampling design In 2010, Kenya ratified a new constitution which established 47 county governments. This change has highlighted the need for national surveys to collect information beyond the provincial level, and move towards collection of county-level estimates. However, obtaining county-level estimates with adequate precision were not considered feasible in KNMS due to limitations in sample size and resources. Therefore KNMS consisted of the three domains as defined earlier. The sampling frame for the 2010 KMNS was based on the National Sample Survey and Evaluation Programme (NASSEP IV) master sampling frame maintained by the Kenya National Bureau of Statistics (KNBS). Administratively, Kenya is divided into 8 provinces. In turn, each province is The Kenya National Micronutrient Survey 2011 subdivided into districts, each district into divisions, each division into locations and each location into sub-locations. In addition to these administrative units, during the last 1999 population census, each sub-location was subdivided into census Enumeration Areas (EAs) i.e. small geographic units with clearly defined boundaries. As defined in the 1999 census, Kenya has eight provinces, 69 districts, and approximately 62,000 EAs. The list of EAs is grouped by administrative units and includes information on the number of households and population. This information was used in 2002 to design a master sample with about 1,800 selected EAs. The cartographic material for each EA in the master sample was updated in the field. The resulting master sampling frame was NASSEP IV which is still currently used by KNBS. The NASSEP IV master frame is a two-stage stratified cluster sample format. The first stage is a selection of Primary Sampling Units (PSUs), which are the EAs using probability proportional to measure of size (PPMOS) method. The second stage involves the selection of households for various surveys. EAs are selected with a basis of one Measure of Size (MOS) defined as the ultimate cluster with an average of 100 households and constitute one (or more) EAs. Although consideration was given to development of a new master frame for KNMS, time and other resource constraints dictated that the sample frame of this survey was NASSEP IV. The KNMS sample was selected using a stratified two-stage cluster design consisting of 296 clusters, 123 in the urban and 173 in the rural areas. From each cluster a total of 10 households were selected using systematic simple random sampling. For the KNMS survey, an urban area was defined as "an area with an increased density of human-created structures in comparison to the areas surrounding it and has a population of 2,000 people and above". Using this definition, urban areas included Cities, Municipalities, Town Councils, Urban Councils and all District Headquarters. A rural area was defined as an isolated large area of an open country in reference to open fields with peoples whose main economic activity was farming. Every attempt was made to conduct interviews in the 10 selected households, and one additional visit was made to ascertain this compliance in cases of absence of household members to minimize potential bias. Non responding households were not replaced.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey covers household members questionnaire (usual residents), women questinnaire ( aged 15-49 years), preschool children questionnarie( aged 6-59 months), school age children questionnaire (aged 5-14 years) and men questionnire (aged 15-54 year). The hosehold member questionnaire includes: Identification, Interviewer Visits, Socio demographic characteristics, Socio-economic characteristics, Food fortification, Wheat flour fortification, Salt fortification, Sugar fortification, Oils/fats fortification, Interviewer's observations. The women questionnarie includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, WRA Health questions. The school age children questionnaire includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, Child Health questions, Dietary Diversity Score Questions, Infant Feeding Practice Questions children 6-35 months, Interviewer Observations, The preschool children questionnarie includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, Child Health questions, Interviewer Observations. The men questionnarie includes: Identification, Interviewer Visits, Health questions, Interviewer Observations.

Cleaning operations

The field questionnaires baring household characteristics, individual population characteristics, and anthropometrics measurements were double entered into a computer database designed using MS-Access application. Regular file back-up was done using flash disks and external hard disk to avoid any loss or tampering. Data comparison was done using Epi-info version 7.0. Data cleaning and validation was performed to achieve clean datasets. The datasets were exported into a Statistical Package format (IBM® SPSS® Statistics version 20.0). The laboratory results were entered in excel format and later exported into a Statistical Package format (IBM® SPSS®Statistics version 20.0). Data merging exercise was systematically conducted using the four datasets i.e. household characteristics, individual population characteristics, anthropometrics measurements, and laboratory results. Each of the five populations namely; Pre-school children (PSC), School aged children (SAC), Pregnant women (PW), Non-pregnant women (NPW), and Men were separately merged. Data merging was conducted as follows: STEP1: The 'laboratory results' file was first merged to the 'anthropometrics' file using 'LABLE NUMBER' as the unique identifier. STEP2: The merged 'laboratory + anthropometrics' file was merged to individual population characteristics file using a merging variable constructed by concatenating 'CLUSTER NUMBER + HOUSEHOLD NUMBER + LINE NUMBER' as the unique identifier. STEP3: The merged 'laboratory + anthropometrics + individual population characteristics' file was merged to the 'household characteristics' file using a merging variable constructed by concatenating 'CLUSTER NUMBER + HOUSEHOLD NUMBER + LINE NUMBER' as the unique identifier. Five master-files were backed-up for safe keeping and a copy was shared with the statisticians for analysis. All the questionnaires and laboratory forms were filed and stored in lockable drawers for confidentiality.

The validated data was exported to SPSS Version 20 for analysis.

A retrospective, hospital-based study was conducted at Al-Buluk Pediatric Hospital, Khartoum, Sudan, from January 2017 to December 2022. A total of 113 pediatric patients (40 males, 51 females) aged 4 months to 12 years were analyzed. Recent patient assessments were done using routine diagnostic protocols for SCA monitoring and manifestations. Physicians' insight into innovative techniques at the molecular level was used to enhance the medical performance of disease investigations. The online questionnaire showed the physicians' response and acceptance levels to introducing innovative techniques in integration with current clinical and laboratory spectrums. Data interpretation and statistical analysis were done using IBM-SPSS version 25 and MS Excel 2019.

The PDF contains the dataset that's used for SPSS Analysis to see the influence of personal and non-personal interpretation on visitors' cognitive capabilities at the Fatahillah Museum

Abstract

The 2011 Population and Housing Census is the third national Census to be conducted in Namibia after independence. The first was conducted 1991 followed by the 2001 Census. Namibia is therefore one of the countries in sub-Saharan Africa that has participated in the 2010 Round of Censuses and followed the international best practice of conducting decennial Censuses, each of which attempts to count and enumerate every person and household in a country every ten years. Surveys, by contrast, collect data from samples of people and/or households.

Censuses provide reliable and critical data on the socio-economic and demographic status of any country. In Namibia, Census data has provided crucial information for development planning and programme implementation. Specifically, the information has assisted in setting benchmarks, formulating policy and the evaluation and monitoring of national development programmes including NDP4, Vision 2030 and several sector programmes. The information has also been used to update the national sampling frame which is used to select samples for household-based surveys, including labour force surveys, demographic and health surveys, household income and expenditure surveys. In addition, Census information will be used to guide the demarcation of Namibia's administrative boundaries where necessary.

At the international level, Census information has been used extensively in monitoring progress towards Namibia's achievement of international targets, particularly the Millennium Development Goals (MDGs).

The latest and most comprehensive Census was conducted in August 2011. Preparations for the Census started in the 2007/2008 financial year under the auspices of the then Central Bureau of Statistics (CBS) which was later transformed into the Namibia Statistics Agency (NSA). The NSA was established under the Statistics Act No. 9 of 2011, with the legal mandate and authority to conduct population Censuses every 10 years. The Census was implemented in three broad phases; pre-enumeration, enumeration and post enumeration.

During the first pre-enumeration phase, activities accomplished including the preparation of a project document, establishing Census management and technical committees, and establishing the Census cartography unit which demarcated the Enumeration Areas (EAs). Other activities included the development of Census instruments and tools, such as the questionnaires, manuals and field control forms.

Field staff were recruited, trained and deployed during the initial stages of the enumeration phase. The actual enumeration exercise was undertaken over a period of about three weeks from 28 August to 15 September 2011, while 28 August 2011 was marked as the reference period or 'Census Day'.

Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultat.The post-enumeration phase started with the sending of completed questionnaires to Head Office and the preparation of summaries for the preliminary report, which was published in April 2012. Processing of the Census data began with manual editing and coding, which focused on the household identification section and un-coded parts of the questionnaire. This was followed by the capturing of data through scanning. Finally, the data were verified and errors corrected where necessary. This took longer than planned due to inadequate technical skills.

Geographic coverage

National coverage

Analysis unit

Households and persons

Universe

The sampling universe is defined as all households (private and institutions) from 2011 Census dataset.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample Design

The stratified random sample was applied on the constituency and urban/rural variables of households list from Namibia 2011 Population and Housing Census for the Public Use Microdata Sample (PUMS) file. The sampling universe is defined as all households (private and institutions) from 2011 Census dataset. Since urban and rural are very important factor in the Namibia situation, it was then decided to take the stratum at the constituency and urban/rural levels. Some constituencies have very lower households in the urban or rural, the office therefore decided for a threshold (low boundary) for sampling within stratum. Based on data analysis, the threshold for stratum of PUMS file is 250 households. Thus, constituency and urban/rural areas with less than 250 households in total were included in the PUMS file. Otherwise, a simple random sampling (SRS) at a 20% sample rate was applied for each stratum. The sampled households include 93,674 housing units and 418,362 people.

Sample Selection

The PUMS sample is selected from households. The PUMS sample of persons in households is selected by keeping all persons in PUMS households. Sample selection process is performed using Census and Survey Processing System (CSPro).

The sample selection program first identifies the 7 census strata with less than 250 households and the households (private and institutions) with more than 50 people. The households in these areas and with this large size are all included in the sample. For the other households, the program randomly generates a number n from 0 to 4. Out of every 5 households, the program selects the nth household to export to the PUMS data file, creating a 20 percent sample of households. Private households and institutions are equally sampled in the PUMS data file.

Note: The 7 census strata with less than 250 households are: Arandis Constituency Rural, Rehoboth East Urban Constituency Rural, Walvis Bay Rural Constituency Rural, Mpungu Constituency Urban, Etayi Constituency Urban, Kalahari Constituency Urban, and Ondobe Constituency Urban.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaire instruments were used for the Namibia 2011 Population and and Housing Census:

Form A (Long Form): For conventional households and residential institutions

Form B1 (Short Form): For special population groups such as persons in transit (travellers), police cells, homeless and off-shore populations

Form B2 (Short Form): For hotels/guesthouses

Form B3 (Short Form): For foreign missions/diplomatic corps

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) During data collection in the field b) Manual editing and coding in the office c) During data entry (Primary validation/editing) Structure checking and completeness using Structured Query Language (SQL) program d) Secondary editing: i. Imputations of variables ii. Structural checking in Census and Survey Processing System (CSPro) program

Sampling error estimates

Sampling Error The standard errors of survey estimates are needed to evaluate the precision of the survey estimation. The statistical software package such as SPSS or SAS can accurately estimate the mean and variance of estimates from the survey. SPSS or SAS software package makes use of the Taylor series approach in computing the variance.

Data appraisal

Data quality Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultation with government ministries, university expertise and international partners; the preparation of detailed supervisors' and enumerators' instruction manuals to guide field staff during enumeration; the undertaking of comprehensive publicity and advocacy programmes to ensure full Government support and cooperation from the general public; the testing of questionnaires and other procedures; the provision of adequate training and undertaking of intensive supervision using four supervisory layers; the editing of questionnaires at field level; establishing proper mechanisms which ensured that all completed questionnaires were properly accounted for; ensuring intensive verification, validating all information and error corrections; and developing capacity in data processing with support from the international community.

Driving factors of super-gentrification and their meanings.

This download goves updated with RecID Adjustment Weights for the 1891-1911 England and Wales censuses and corresponds to Supplementary material for the paper "The Population of Non-corporate Business Proprietors in England and Wales 1891-1911", by Bennett, Robert J., Montebruno, Piero, Smith, Harry J. as an outcome of the ESRC project ES/M010953: Drivers of Entrepreneurship and Small Businesses PI Prof. Robert J. Bennett. The material consists of three raw text files 1. 1891 Employment status & Weights 2. 1901 Employment status & Weights 3. 1911 Employment status & Weights Each file has the three following variables: 1. RecID: the ID for I-CEM2 as in Higgs, Edward and Schürer, Kevin (University of Essex) (2014) The Integrated Census Microdata (I-CeM) UKDA, SN-7481; K. Schürer, E. Higgs, A.M. Reid, E.M Garrett, Integrated Census Microdata, 1851-1911, version V. 2 (I-CeM.2), (2016) [data collection] UK Data Service SN: 7481 2. Employment status: 1 Worker 2 Employer 3 Own-account 3. Weights: the inverse of the probability of giving an answer to the Employment Status question of the censuses by Sex and Relationship to the head of the family. A detailed explanation of how these weights were calculated and how to use them in the context of data analysis of this censuses can be found in the accompanying working paper, Montebruno, Piero (2018) ‘Adjustment Weights 1891-1911: Weights to adjust entrepreneurs taking account of non-response and misallocation bias in Censuses 1891-1911’, Working Paper 11: ESRC project ES/M010953: ‘Drivers of Entrepreneurship and Small Businesses’, University of Cambridge, Department of Geography and Cambridge Group for the History of Population and Social Structure. The files can be opened by any text editor, database management system (Access) or statistical package (Stata, SPSS) This dataset should be cited as Adjustment Weights 1891-1911, "The Population of Non-corporate Business Proprietors in England and Wales 1891-1911", by Bennett, Robert J., Montebruno, Piero, Smith, Harry J. Please cite using its DOI.

This dataset contains Adjustment Weights for the 1891-1911 England and Wales censuses and corresponds to Supplementary material for the paper "The Population of Non-corporate Business Proprietors in England and Wales 1891-1911", by Bennett, Robert J., Montebruno, Piero, Smith, Harry J. as an outcome of the ESRC project ES/M010953: Drivers of Entrepreneurship and Small Businesses PI Prof. Robert J. Bennett.

The material consists of three raw text files

1. 1891 Employment status & Weights
2. 1901 Employment status & Weights
3. 1911 Employment status & Weights

Each file has the three following variables:

1. newRecID: the ID for I-CEM2 as in Higgs, Edward and Schürer, Kevin (University of Essex) (2014) The Integrated Census Microdata (I-CeM) UKDA, SN-7481; K. Schürer, E. Higgs, A.M. Reid, E.M Garrett, Integrated Census Microdata, 1851-1911, version V. 2 (I-CeM.2), (2016) [data collection] UK Data Service SN: 7481

2. Employment status: 1 Worker 2 Employer 3 Own-account

3. Weights: the inverse of the probability of giving an answer to the Employment Status question of the censuses by Sex and Relationship to the head of the family.

A detailed explanation of how these weights were calculated and how to use them in the context of data analysis of this censuses can be found in the accompanying working paper, Montebruno, Piero (2018) ‘Adjustment Weights 1891-1911: Weights to adjust entrepreneurs taking account of non-response and misallocation bias in Censuses 1891-1911’, Working Paper 11: ESRC project ES/M010953: ‘Drivers of Entrepreneurship and Small Businesses’, University of Cambridge, Department of Geography and Cambridge Group for the History of Population and Social Structure.

The files can be opened by any text editor, database management system (Access) or statistical package (Stata, SPSS)

This dataset should be cited as Adjustment Weights 1891-1911, "The Population of Non-corporate Business Proprietors in England and Wales 1891-1911", by Bennett, Robert J., Montebruno, Piero, Smith, Harry J. Please cite using its DOI.

Socio-demographic characteristics of CHW in the 2 communes.