Data Cleaning Tools Market Outlook

As of 2023, the global market size for data cleaning tools is estimated at $2.5 billion, with projections indicating that it will reach approximately $7.1 billion by 2032, reflecting a robust CAGR of 12.1% during the forecast period. This growth is primarily driven by the increasing importance of data quality in business intelligence and analytics workflows across various industries.

The growth of the data cleaning tools market can be attributed to several critical factors. Firstly, the exponential increase in data generation across industries necessitates efficient tools to manage data quality. Poor data quality can result in significant financial losses, inefficient business processes, and faulty decision-making. Organizations recognize the value of clean, accurate data in driving business insights and operational efficiency, thereby propelling the adoption of data cleaning tools. Additionally, regulatory requirements and compliance standards also push companies to maintain high data quality standards, further driving market growth.

Another significant growth factor is the rising adoption of AI and machine learning technologies. These advanced technologies rely heavily on high-quality data to deliver accurate results. Data cleaning tools play a crucial role in preparing datasets for AI and machine learning models, ensuring that the data is free from errors, inconsistencies, and redundancies. This surge in the use of AI and machine learning across various sectors like healthcare, finance, and retail is driving the demand for efficient data cleaning solutions.

The proliferation of big data analytics is another critical factor contributing to market growth. Big data analytics enables organizations to uncover hidden patterns, correlations, and insights from large datasets. However, the effectiveness of big data analytics is contingent upon the quality of the data being analyzed. Data cleaning tools help in sanitizing large datasets, making them suitable for analysis and thus enhancing the accuracy and reliability of analytics outcomes. This trend is expected to continue, fueling the demand for data cleaning tools.

In terms of regional growth, North America holds a dominant position in the data cleaning tools market. The region's strong technological infrastructure, coupled with the presence of major market players and a high adoption rate of advanced data management solutions, contributes to its leadership. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitization of businesses, increasing investments in IT infrastructure, and a growing focus on data-driven decision-making are key factors driving the market in this region.

As organizations strive to maintain high data quality standards, the role of an Email List Cleaning Service becomes increasingly vital. These services ensure that email databases are free from invalid addresses, duplicates, and outdated information, thereby enhancing the effectiveness of marketing campaigns and communications. By leveraging sophisticated algorithms and validation techniques, email list cleaning services help businesses improve their email deliverability rates and reduce the risk of being flagged as spam. This not only optimizes marketing efforts but also protects the reputation of the sender. As a result, the demand for such services is expected to grow alongside the broader data cleaning tools market, as companies recognize the importance of maintaining clean and accurate contact lists.

Component Analysis

The data cleaning tools market can be segmented by component into software and services. The software segment encompasses various tools and platforms designed for data cleaning, while the services segment includes consultancy, implementation, and maintenance services provided by vendors.

The software segment holds the largest market share and is expected to continue leading during the forecast period. This dominance can be attributed to the increasing adoption of automated data cleaning solutions that offer high efficiency and accuracy. These software solutions are equipped with advanced algorithms and functionalities that can handle large volumes of data, identify errors, and correct them without manual intervention. The rising adoption of cloud-based data cleaning software further bolsters this segment, as it offers scalability and ease of

Email List Cleaning Service Market Outlook

The global email list cleaning service market size was valued at approximately USD 250 million in 2023 and is projected to reach around USD 700 million by 2032, growing at a robust CAGR of 12.6% during the forecast period. The market is driven by the increasing importance of maintaining clean and updated email lists to ensure high deliverability rates and avoid spam filters. The rising volume of marketing emails and the need for businesses to maintain a high sender reputation are significant growth factors for this market.

The proliferation of digital marketing and the increasing reliance on email campaigns as a primary mode of communication with customers have significantly driven the demand for email list cleaning services. Businesses across various sectors recognize the importance of reaching their target audience effectively, and clean email lists are crucial for achieving this. Clean email lists help in reducing bounce rates and enhancing email deliverability, which in turn leads to higher engagement rates and improved ROI on email marketing campaigns. The growing awareness among businesses about the consequences of sending emails to invalid addresses, such as being blacklisted or marked as spam, further fuels the market's growth.

Another critical growth factor is the rapid technological advancements in the email list cleaning services market. The integration of AI and machine learning algorithms into email cleaning software has made these services more efficient and accurate. These advanced technologies help in identifying and removing invalid, duplicate, and inactive email addresses with greater precision. The rise of cloud-based solutions has also made it easier for businesses to access and utilize these services, contributing to the market's expansion. Additionally, the increasing adoption of email list cleaning services by small and medium enterprises (SMEs) is expected to drive market growth, as these businesses seek cost-effective solutions to enhance their marketing efforts.

The regulatory landscape is another significant factor contributing to the market's growth. Stringent data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe and the CAN-SPAM Act in the United States, mandate businesses to maintain clean email lists and obtain explicit consent from recipients. Non-compliance with these regulations can result in hefty fines and damage to a company's reputation. As a result, businesses are increasingly turning to email list cleaning services to ensure compliance and avoid legal repercussions. This trend is expected to continue driving the market's growth in the coming years.

From a regional perspective, North America holds a significant share of the global email list cleaning service market. The region's dominance can be attributed to the high adoption rate of digital marketing practices and the presence of a large number of businesses utilizing email marketing. Additionally, strict data protection regulations and the need for compliance have led to increased demand for email list cleaning services in North America. Europe follows closely, driven by the stringent GDPR regulations and the growing awareness of the importance of clean email lists. The Asia Pacific region is expected to witness the highest CAGR during the forecast period, fueled by the rapid digital transformation and increasing adoption of email marketing by businesses in emerging economies such as China and India.

In the realm of email list cleaning, Data Cleaning Tools play a pivotal role in ensuring the accuracy and reliability of email databases. These tools are designed to meticulously sift through vast amounts of data, identifying and rectifying errors such as duplicates, syntax issues, and outdated information. By employing sophisticated algorithms and machine learning techniques, data cleaning tools enhance the quality of email lists, thereby boosting deliverability rates and engagement. As businesses increasingly rely on data-driven strategies for their marketing efforts, the demand for effective data cleaning tools becomes paramount. These tools not only streamline the process of maintaining clean email lists but also provide valuable insights into data health, enabling businesses to make informed decisions and optimize their marketing campaigns.

Type Analysis

The email list cleaning service market can be segmented into software and services. The soft

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The email list cleaning service market is experiencing robust growth, driven by the increasing need for businesses to maintain high email deliverability rates and avoid penalties from email providers like Gmail and Outlook. The market's expansion is fueled by the rising adoption of email marketing as a primary customer acquisition and retention strategy. Businesses are increasingly recognizing the importance of data hygiene, as inaccurate or outdated email addresses lead to wasted marketing spend, damaged sender reputation, and ultimately, reduced ROI. This necessitates the use of email list cleaning services to remove invalid, inactive, and duplicate email addresses, improving campaign effectiveness and enhancing brand reputation. A conservative estimate, considering the typical growth in SaaS and marketing technology sectors, places the current market size (2025) at approximately $500 million, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is being fueled by several key factors: a rise in spam complaints leading to stricter email deliverability standards, the increasing sophistication of email list cleaning tools offering more comprehensive data analysis and verification methods, and the growing preference for automated solutions to streamline email marketing workflows. Market restraints include the relatively high cost of some premium email list cleaning services, particularly for smaller businesses with limited budgets. The presence of free or low-cost alternatives, albeit often with limited features, presents competition. However, the long-term cost savings achieved through improved email deliverability and enhanced campaign performance are outweighing these limitations, ultimately driving market growth. Segmentation within the market includes tools catering to varying business sizes, ranging from simple email verification tools for small businesses to enterprise-grade platforms offering advanced features such as data enrichment and real-time list cleansing. Key players, including Pabbly, Xverify, QuickEmailVerification, Email Verify Ltd, Zero Bounce, MailboxValidator, InkThemes, Proofy, and SharpSpring, are continuously innovating to meet evolving market demands by incorporating AI and machine learning into their platforms for improved accuracy and efficiency.

A collection of datasets and python scripts for extraction and analysis of isograms (and some palindromes and tautonyms) from corpus-based word-lists, specifically Google Ngram and the British National Corpus (BNC).Below follows a brief description, first, of the included datasets and, second, of the included scripts.1. DatasetsThe data from English Google Ngrams and the BNC is available in two formats: as a plain text CSV file and as a SQLite3 database.1.1 CSV formatThe CSV files for each dataset actually come in two parts: one labelled ".csv" and one ".totals". The ".csv" contains the actual extracted data, and the ".totals" file contains some basic summary statistics about the ".csv" dataset with the same name.The CSV files contain one row per data point, with the colums separated by a single tab stop. There are no labels at the top of the files. Each line has the following columns, in this order (the labels below are what I use in the database, which has an identical structure, see section below):

Label Data type Description

isogramy int The order of isogramy, e.g. "2" is a second order isogram

length int The length of the word in letters

word text The actual word/isogram in ASCII

source_pos text The Part of Speech tag from the original corpus

count int Token count (total number of occurences)

vol_count int Volume count (number of different sources which contain the word)

count_per_million int Token count per million words

vol_count_as_percent int Volume count as percentage of the total number of volumes

is_palindrome bool Whether the word is a palindrome (1) or not (0)

is_tautonym bool Whether the word is a tautonym (1) or not (0)

The ".totals" files have a slightly different format, with one row per data point, where the first column is the label and the second column is the associated value. The ".totals" files contain the following data:

Label

Data type

Description

!total_1grams

int

The total number of words in the corpus

!total_volumes

int

The total number of volumes (individual sources) in the corpus

!total_isograms

int

The total number of isograms found in the corpus (before compacting)

!total_palindromes

int

How many of the isograms found are palindromes

!total_tautonyms

int

How many of the isograms found are tautonyms

The CSV files are mainly useful for further automated data processing. For working with the data set directly (e.g. to do statistics or cross-check entries), I would recommend using the database format described below.1.2 SQLite database formatOn the other hand, the SQLite database combines the data from all four of the plain text files, and adds various useful combinations of the two datasets, namely:• Compacted versions of each dataset, where identical headwords are combined into a single entry.• A combined compacted dataset, combining and compacting the data from both Ngrams and the BNC.• An intersected dataset, which contains only those words which are found in both the Ngrams and the BNC dataset.The intersected dataset is by far the least noisy, but is missing some real isograms, too.The columns/layout of each of the tables in the database is identical to that described for the CSV/.totals files above.To get an idea of the various ways the database can be queried for various bits of data see the R script described below, which computes statistics based on the SQLite database.2. ScriptsThere are three scripts: one for tiding Ngram and BNC word lists and extracting isograms, one to create a neat SQLite database from the output, and one to compute some basic statistics from the data. The first script can be run using Python 3, the second script can be run using SQLite 3 from the command line, and the third script can be run in R/RStudio (R version 3).2.1 Source dataThe scripts were written to work with word lists from Google Ngram and the BNC, which can be obtained from http://storage.googleapis.com/books/ngrams/books/datasetsv2.html and [https://www.kilgarriff.co.uk/bnc-readme.html], (download all.al.gz).For Ngram the script expects the path to the directory containing the various files, for BNC the direct path to the *.gz file.2.2 Data preparationBefore processing proper, the word lists need to be tidied to exclude superfluous material and some of the most obvious noise. This will also bring them into a uniform format.Tidying and reformatting can be done by running one of the following commands:python isograms.py --ngrams --indir=INDIR --outfile=OUTFILEpython isograms.py --bnc --indir=INFILE --outfile=OUTFILEReplace INDIR/INFILE with the input directory or filename and OUTFILE with the filename for the tidied and reformatted output.2.3 Isogram ExtractionAfter preparing the data as above, isograms can be extracted from by running the following command on the reformatted and tidied files:python isograms.py --batch --infile=INFILE --outfile=OUTFILEHere INFILE should refer the the output from the previosu data cleaning process. Please note that the script will actually write two output files, one named OUTFILE with a word list of all the isograms and their associated frequency data, and one named "OUTFILE.totals" with very basic summary statistics.2.4 Creating a SQLite3 databaseThe output data from the above step can be easily collated into a SQLite3 database which allows for easy querying of the data directly for specific properties. The database can be created by following these steps:1. Make sure the files with the Ngrams and BNC data are named “ngrams-isograms.csv” and “bnc-isograms.csv” respectively. (The script assumes you have both of them, if you only want to load one, just create an empty file for the other one).2. Copy the “create-database.sql” script into the same directory as the two data files.3. On the command line, go to the directory where the files and the SQL script are. 4. Type: sqlite3 isograms.db 5. This will create a database called “isograms.db”.See the section 1 for a basic descript of the output data and how to work with the database.2.5 Statistical processingThe repository includes an R script (R version 3) named “statistics.r” that computes a number of statistics about the distribution of isograms by length, frequency, contextual diversity, etc. This can be used as a starting point for running your own stats. It uses RSQLite to access the SQLite database version of the data described above.

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This dataset provides the Oxford 2015 A-Z word list, specifically designed for various Natural Language Processing (NLP) tasks. It serves as a foundational resource for language analysis, text processing, and the development of applications requiring extensive English vocabulary.

Columns

The dataset primarily consists of words, with each entry representing a single word from the Oxford 2015 A-Z word list. The structure implies a single column for the word entry.

Distribution

The dataset is distributed across 26 individual files, with each file named alphabetically (e.g., 'a', 'b', 'c', etc.) [1]. Each file contains all words corresponding to its filename. Specific numbers for rows or records are not detailed in the available information, but each file includes every word from that specific alphabet [1].

Usage

This dataset is highly suitable for a wide range of applications, including: * Natural Language Processing (NLP) development [1] * Text cleaning and pre-processing [1] * Building spell checkers or auto-correction systems * Lexical analysis and linguistic research * Educational software and language learning tools

Coverage

The dataset's coverage is global [2]. It represents a snapshot of the Oxford A-Z word list from 2015 [1]. The data is organised alphabetically, ensuring coverage for words beginning with every letter of the English alphabet [1].

License

CC-BY-SA

Who Can Use It

This dataset is ideal for: * Data scientists and NLP engineers for model training and feature engineering. * Researchers in linguistics and computational linguistics. * Students and educators in language and computer science fields. * Developers creating text-based applications, dictionaries, or educational tools.

Dataset Name Suggestions

• Oxford English Word List 2015
• NLP A-Z English Dictionary
• Alphabetical English Lexicon
• Oxford 2015 Vocabulary Set
• English Word List for NLP

Attributes

Original Data Source: Oxford Dictionary

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Abstract

The Palestinian society's access to information and communication technology tools is one of the main inputs to achieve social development and economic change to the status of Palestinian society; on the basis of its impact on the revolution of information and communications technology that has become a feature of this era. Therefore, and within the scope of the efforts exerted by the Palestinian Central Bureau of Statistics in providing official Palestinian statistics on various areas of life for the Palestinian community, PCBS implemented the household survey for information and communications technology for the year 2019. The main objective of this report is to present the trends of accessing and using information and communication technology by households and individuals in Palestine, and enriching the information and communications technology database with indicators that meet national needs and are in line with international recommendations.

Geographic coverage

Palestine, West Bank, Gaza strip

Analysis unit

Household, Individual

Universe

All Palestinian households and individuals (10 years and above) whose usual place of residence in 2019 was in the state of Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of master sample which were enumerated in the 2017 census. Each enumeration area consists of buildings and housing units with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size The estimated sample size is 8,040 households.

Sample Design The sample is three stages stratified cluster (pps) sample. The design comprised three stages: Stage (1): Selection a stratified sample of 536 enumeration areas with (pps) method. Stage (2): Selection a stratified random sample of 15 households from each enumeration area selected in the first stage. Stage (3): Selection one person of the (10 years and above) age group in a random method by using KISH TABLES.

Sample Strata The population was divided by: 1- Governorate (16 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaire The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on Individuals (10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Programming Consistency Check The data collection program was designed in accordance with the questionnaire's design and its skips. The program was examined more than once before the conducting of the training course by the project management where the notes and modifications were reflected on the program by the Data Processing Department after ensuring that it was free of errors before going to the field.

Using PC-tablet devices reduced data processing stages, and fieldworkers collected data and sent it directly to server, and project management withdraw the data at any time.

In order to work in parallel with Jerusalem (J1), a data entry program was developed using the same technology and using the same database used for PC-tablet devices.

Data Cleaning After the completion of data entry and audit phase, data is cleaned by conducting internal tests for the outlier answers and comprehensive audit rules through using SPSS program to extract and modify errors and discrepancies to prepare clean and accurate data ready for tabulation and publishing.

Tabulation After finalizing checking and cleaning data from any errors. Tables extracted according to prepared list of tables.

Response rate

The response rate in the West Bank reached 77.6% while in the Gaza Strip it reached 92.7%.

Sampling error estimates

Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, There is no problem to disseminate results at the national level and at the level of the West Bank and Gaza Strip.

Non-Sampling Errors Non-Sampling errors are possible at all stages of the project, during data collection or processing. These are referred to non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, as well as practical and theoretical training during the training course.

The implementation of the survey encountered non-response where the case (household was not present at home) during the fieldwork visit become the high percentage of the non response cases. The total non-response rate reached 17.5%. The refusal percentage reached 2.9% which is relatively low percentage compared to the household surveys conducted by PCBS, and the reason is the questionnaire survey is clear.

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The B2B carpet cleaning market exhibits robust growth potential, driven by increasing demand for hygiene and maintenance services across commercial sectors. While precise market size figures are unavailable, considering the presence of numerous established players like Hako, TTI, Bucher, and Zoomlion, and factoring in average CAGR growth rates within the cleaning equipment sector (let's assume a conservative 5% CAGR for illustrative purposes), a reasonable estimate for the 2025 market size could be in the range of $2.5 billion to $3 billion. This estimate accounts for both large-scale industrial cleaning equipment and smaller, more specialized machines used in office buildings and hospitality settings. Key growth drivers include the rising awareness of indoor air quality, stricter hygiene regulations in various industries (healthcare, hospitality, etc.), and increasing outsourcing of cleaning tasks to specialized providers. Trends indicate a shift towards technologically advanced, eco-friendly cleaning solutions, with a focus on efficiency and reduced environmental impact. This trend is reflected in the inclusion of companies like TASKI, known for its innovative cleaning technologies, in the list of market players. However, market restraints include the initial high investment costs of specialized equipment, the fluctuating prices of raw materials and energy, and potential economic downturns impacting overall spending on facility maintenance. The market segmentation (data not provided) would likely include various equipment types, ranging from industrial carpet extractors to smaller, handheld units, tailored to specific cleaning needs. This projected growth trajectory, even with a conservative CAGR, suggests significant opportunities for businesses operating in this market segment. Further analysis would benefit from a detailed breakdown of regional market share, specific product segment performance, and a more precise quantification of the market size. Competitive landscapes are highly influenced by the technological advancements provided by industry giants, and adapting to these shifts is key to success within this competitive landscape. The increasing awareness of sustainability, coupled with stricter environmental regulations, is driving innovation towards more eco-friendly cleaning solutions, representing a significant market opportunity for businesses able to adapt and innovate. Understanding the unique requirements of different market segments (e.g., healthcare vs. hospitality) will be critical to tailored product development and effective marketing strategies. This in-depth report provides a comprehensive analysis of the B2B carpet cleaning market, valued at approximately $2.5 billion globally in 2023. It delves into market segmentation, key players, emerging trends, and future growth projections, offering invaluable insights for businesses operating within this dynamic sector. The report leverages extensive primary and secondary research, incorporating data from industry publications, financial reports, and expert interviews to deliver a robust and accurate representation of the market landscape. This report will be an indispensable tool for industry professionals seeking to understand current market dynamics and make informed strategic decisions.

This table presents the data extraction from the 99 studies included according to the criteria outlined in the main manuscript. It is provided as supplementary material to enhance the readability of the paper while ensuring that all relevant information is preserved and accessible without loss of detail.

The names of the variables and their descriptions are provided in the attached file, along with the following details:

According to Cognitive Market Research, The Global ETL Tools market will grow at a compound annual growth rate (CAGR) of 8.00% from 2023 to 2030.

The demand for ETL tools market is rising due to the rising demand for data-focused decision-making and the increasing popularity of self-service analytics.
Demand for enterprise remains higher in the ETL tools market.
The cloud deployment category held the highest ETL tools market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific ETL tools market will experience the strongest growth until 2030.

Accelerated Digital Transformation Initiatives to Provide Viable Market Output

The ETL Tools market is the rapid acceleration of digital transformation initiatives across industries. Businesses are increasingly recognizing the importance of data-driven decision-making processes. ETL tools play a pivotal role in this transformation by efficiently extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or analytical systems. With the proliferation of online platforms, IoT devices, and social media, the volume of data generated has surged.

In 2021, Microsoft launched Azure Purview, a novel data governance service hosted on the cloud. This service provides a unified and comprehensive approach for locating, overseeing, and charting all data within an enterprise.

ETL tools empower organizations to harness this immense data, enabling sophisticated analytics, business intelligence, and predictive modeling. This driver is crucial as companies strive to gain a competitive edge by leveraging their data assets effectively, driving the demand for advanced ETL tools that can handle diverse data sources and complex transformations.

Increasing Focus on Data Quality and Governance to Propel Market Growth

The ETL Tools market is the growing emphasis on data quality and governance. As data becomes central to strategic decision-making, ensuring its accuracy, consistency, and security has become paramount. ETL tools not only facilitate seamless data integration but also offer functionalities for data cleansing, validation, and enrichment. Organizations, particularly in highly regulated sectors like finance and healthcare, are increasingly investing in ETL solutions that enforce data governance policies and adhere to compliance requirements. Ensuring data quality from its origin to its consumption is vital for reliable analytics, regulatory compliance, and maintaining customer trust. The rising awareness about data governance’s impact on business outcomes is propelling the adoption of ETL tools equipped with robust data quality features, driving market growth in this direction.

Rising Adoption of Cloud Based Technologies in ETL, Fuels the Market Growth

Market Dynamics of the ETL Tools

Complex Implementation Challenges to Hinder Market Growth

The ETL Tools market is the complexity associated with implementation and integration processes. ETL tools often need to work seamlessly with existing databases, data warehouses, and various applications within an organization's IT ecosystem. Integrating these tools while ensuring data consistency, security, and minimal disruption to existing operations can be intricate and time-consuming. Organizations face challenges in aligning ETL tools with their specific business requirements, leading to prolonged implementation timelines. Additionally, complexities arise when dealing with large volumes of diverse data formats and sources. These implementation challenges can result in increased costs, delayed project timelines, and sometimes, suboptimal utilization of the ETL tools, hindering the market’s growth potential.

Trend Factor for the ETL Tools Market

With businesses increasingly moving from on-premise solutions to cloud-native and hybrid environments, the quick adoption of cloud-based data infrastructure is reshaping the ETL (Extract, Transform, Load) tools market. Driven by the demand for immediate insights in industries like finance, retail, and logistics, the rising need for real-time data integration and streaming capabilities is a key trend. Non-technical users are now able to create and maintain data pipelines on their own thanks to the emergence of no-code and low-code ETL systems, which has increased flexibility and decreased reliance on IT. Additionally, artificial intelligence and machine ...

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was

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Abstract

Cambodia Living Standards Measurement Study – Plus (LSMS+) Survey 2019- 2020 was implemented by the National Institute of Statistics, with support from the World Bank LSMS+ program (www.worldbank.org/lsmsplus). The survey attempted to conduct private interviews with all the adult household members (aged 18 and older) in each sampled household as part of a nationally-representative survey sample. The individual disaggregated data collection had a focus on (i) ownership of and rights to physical and financial assets, (ii) work and employment, and (iii) non-farm enterprises, and was anchored in the latest international recommendations for survey data collection on these topics.

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The Primary Sampling Units (PSUs) of this survey were the subsamples of the selected PSUs of the Cambodia Socio-Economic Survey (CSES) 2019/20. The PSU in this case can either be a village (if the village is small) or an Enumeration Area (EA) from the mapping operation of 2019 General Population Census of Cambodia (if the village is large, exceeding 120 households). The Cambodia LSMS+ sample covered all the CSES’ s sample villages in three months (those selected for interviews during the October - December period of fieldwork) out of its twelve-month sample.

The Secondary Sampling Units (SSUs) in this survey constitute sample households. In this stage, 6 households were selected in each selected PSU. The selections of these households were carried out in the field by the field enumerators. The selection was done under the Circular Systematic Random Sampling (CSRS) scheme using the PSU frame of household from the household listing conducted by the CSES field enumerator in the selected PSU. More details can be found in the Basic Information Document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Cambodia LSMS+ covered the following topics:

Household Questionnaire: - Household Roster - Children Living Elsewhere - Housing - Food Consumption - Non-food Consumption - Household Enterprises - Land Roster - Livestock Roster - Durables Roster

Individual-level Questionnaire: - Education - Health - Internal and International Migration - Labor - Time Use - Land Ownership and Rights - Livestock Ownership - Durables Ownership - Mobile Phone Ownership - Financial Accounts

Cleaning operations

Data Entry Platform

The Cambodia LSMS+ was conducted using Computer Assisted Personal Interview (CAPI) techniques. The questionnaire was implemented using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Data Analytics and Tools Unit within the Development Economics Data Group (DECDG) at the World Bank. Each interviewer was given one tablet, which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

Data Management

The data communication system used in the Cambodia LSMS+ was highly automated. Field teams were provided with routers to carry with them in the field so they could connect to internet as frequently as possible to sync their questionnaires and this ensured access to the data in real-time.

Data Cleaning The data cleaning process was done in two main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage of cleaning involved a comprehensive review of the final raw data following the first stage of cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) formatting.