In 2024, adults in the United States spent more time reading on weekends than weekdays, according to recent data. The average time spent reading in the U.S. amounted to *** hours (18 minutes) on weekends and holidays, while daily time spent reading on weekdays in 2024 remained belowpre-pandemic levels at just over a ******* of an hour.

The average daily time spent reading by individuals in the United States in 2023 amounted to **** hours, or **** minutes. According to the study, adults over the age of ** were the most avid readers, spending over ** minutes reading each day. Meanwhile, those aged between 15 and 19 years read for less than **** minutes per day on average. Reading and COVID-19 Daily time reading increased among most consumers between 2019 and 2020, part of which could be linked to the unprecedented increases in media consumption during COVID-19 shutdowns. The mean annual expenditure on books per consumer unit also increased year over year, along with spending on digital book readers. Book reading habits A 2020 survey on preferred book formats found that ** percent of U.S. adults favored print books over e-books or audiobooks. However, engagement with digital books is growing. Figures from an annual study on book consumption revealed that the share of adults who reported reading an audiobook in the last year almost doubled between 2011 and 2019, and e-book readership also grew overall during that period.

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• The Healthcare IT market size is expected to be worth around USD 1728 Bn by 2032
• According to a report by UNESCO, countries in South and South-West Asia have the highest number of illiterate adults in the world, estimated at 388 million.
• Approximately 36% of adult Americans possess only basic or below basic health literacy skills.
• Only 12% of Americans are considered proficient in their health literacy skills.
• Health literacy levels in China increased from 6.48% of the population in 2008 to 23.15% in 2020.
• A recent study analyzing global health literacy research from 1995 to 2020 identified the United States, Australia, and the United Kingdom as major contributors to the international collaboration network on health literacy.
• Mental health has been the most active research field in recent years in the context of health literacy.

In the past five decades, the global literacy rate among adults has grown from 67 percent in 1976 to 87.36 percent in 2023. In 1976, males had a literacy rate of 76 percent, compared to a rate of 58 percent among females. This difference of over 17 percent in 1976 has fallen to just seven percent in 2020. Although gaps in literacy rates have fallen across all regions in recent decades, significant disparities remain across much of South Asia and Africa, while the difference is below one percent in Europe and the Americas. Reasons for these differences are rooted in economic and cultural differences across the globe. In poorer societies, families with limited means are often more likely to invest in their sons' education, while their daughters take up a more domestic role. Varieties do exist on national levels, however, and female literacy levels can sometimes exceed the male rate even in impoverished nations, such as Lesotho (where the difference was over 17 percent in 2014); nonetheless, these are exceptions to the norm.

In 2023, the average adult literacy rates (15 years and older) in Latin America and the Caribbean amounted to 94.79 percent. Literacy rates in Latin America and the Caribbean have been slightly improving in all three age groups since 2014.

There is a gender gap in the global literacy rate. Although literacy rates have generally increased worldwide for both men and women, men are on average more literate than women. As of 2023, about 90.6 percent of men and a little less than 84.1 percent of women worldwide were literate. Adult literacy rate is defined as the percentage of people aged 15 years and above who can both read and write with understanding a short, simple statement about their everyday life. Youth literacy rate Not only does the literacy gender gap concern adults, it also exists among the world’s younger generations aged 15 to 24. Despite an overall increase in literacy, young men are still more literate than young women. In fact, the global youth literacy rate as gender parity index was 0.98 as of 2023, indicating that young women are not yet as literate as young men. Gender pay gap Gender gaps occur in many different spheres of global society. One such issue concerns salary gender gaps in professional life. Regarding the controlled gender pay gap, which measures the median salary for men and women with the same job and qualifications, women still earned less than men as of 2024. The difference was even bigger when measuring the median salary for all men and women. However, not everyone worries about gender pay gaps. According to a survey from 2021, 54 percent of the female respondents deemed the gender pay gap a real problem, compared to 45 percent of the male respondents.

Literacy or numeracy - Average scores and distribution of proficiency levels, by Aboriginal status (off-reserve), immigrant status, minority language status and sex, population aged 16-65, selected provinces and territories 2012.

Abstract

The PIRLS 2006 aimed to generate a database of student achievement data in addition to information on student, parent, teacher, and school background data for the 47 areas that participated in PIRLS 2006.

Geographic coverage

Nationally representative

Analysis unit

Units of analysis in the study are schools, students, parents and teachers.

Universe

PIRLS is a study of student achievement in reading comprehension in primary school, and is targeted at the grade level in which students are at the transition from learning to read to reading to learn, which is the fourth grade in most countries. The formal definition of the PIRLS target population makes use of UNESCO's International Standard Classification of Education (ISCED) in identifying the appropriate target grade:

"…all students enrolled in the grade that represents four years of schooling, counting from the first year of ISCED Level 1, providing the mean age at the time of testing is at least 9.5 years. For most countries, the target grade should be the fourth grade, or its national equivalent."

ISCED Level 1 corresponds to primary education or the first stage of basic education, and should mark the beginning of "systematic apprenticeship of reading, writing, and mathematics" (UNESCO, 1999). By the fourth year of Level 1, students have had 4 years of formal instruction in reading, and are in the process of becoming independent readers. In IEA studies, the above definition corresponds to what is known as the international desired target population. Each participating country was expected to define its national desired population to correspond as closely as possible to this definition (i.e., its fourth grade of primary school). In order to measure trends, it was critical that countries that participated in PIRLS 2001, the previous cycle of PIRLS, choose the same target grade for PIRLS 2006 that was used in PIRLS 2001. Information about the target grade in each country is provided in Chapter 9 of the PIRLS 2006 Technical Report.

Although countries were expected to include all students in the target grade in their definition of the population, sometimes it was not possible to include all students who fell under the definition of the international desired target population. Consequently, occasionally a country's national desired target population excluded some section of the population, based on geographic or linguistic constraints. For example, Lithuania's national desired target population included only students in Lithuanian-speaking schools, representing approximately 93 percent of the international desired population of students in the country. PIRLS participants were expected to ensure that the national defined population included at least 95 percent of the national desired population of students. Exclusions (which had to be kept to a minimum) could occur at the school level, within the sampled schools, or both. Although countries were expected to do everything possible to maximize coverage of the national desired population, school-level exclusions sometimes were necessary. Keeping within the 95 percent limit, school-level exclusions could include schools that:

• were geographically remote,
• had very few students,
• had a curriculum or structure different from the mainstream education system, or
• were specifically for students with special needs.

The difference between these school-level exclusions and those at the previous level is that these schools were included as part of the sampling frame (i.e., the list of schools to be sampled). Th ey then were eliminated on an individual basis if it was not feasible to include them in the testing.

In many education systems, students with special educational needs are included in ordinary classes. Due to this fact, another level of exclusions is necessary to reach an eff ective target population-the population of students who ultimately will be tested. These are called within-school exclusions and pertain to students who are unable to be tested for a particular reason but are part of a regular classroom. There are three types of within-school exclusions.

• Intellectually disabled students
• Functionally disabled students
• Non-native language speakers

Students eligible for within-school exclusion were identified by staff at the schools and could still be administered the test if the school did not want the student to feel out of place during the assessment (though the data from these students were not included in any analyses). Again, it was important to ensure that this population was as close to the national desired target population as possible. If combined, school-level and within-school exclusions exceeded 5 percent of the national desired target population, results were annotated in the PIRLS 2006 International Report (Mullis, Martin, Kennedy, & Foy, 2007). Target population coverage and exclusion rates are displayed for each country in Chapter 9 of the PIRLS 2006 Technical Report. Descriptions of the countries' school-level and within-school exclusions can be found in Appendix B of the PIRLS 2006 Technical Report.

Kind of data

Sample survey data [ssd]

Sampling procedure

The basic sample design used in PIRLS 2006 is known as a two-stage stratified cluster design, with the first stage consisting of a sample of schools, and the second stage consisting of a sample of intact classrooms from the target grade in the sampled schools. While all participants adopted this basic two-stage design, four countries, with approval from the PIRLS sampling consultants, added an extra sampling stage. The Russian Federation and the United States introduced a preliminary sampling stage, (first sampling regions in the case of the Russian Federation and primary sampling units consisting of metropolitan areas and counties in the case of the United States). Morocco and Singapore also added a third sampling stage; in these cases, sub-sampling students within classrooms rather than selecting intact classes.

For countries participating in PIRLS 2006, school stratification was used to enhance the precision of the survey results. Many participants employed explicit stratification, where the complete school sampling frame was divided into smaller sampling frames according to some criterion, such as region, to ensurea predetermined number of schools sampled for each stratum. For example, Austria divided its sampling frame into nine regions to ensure proportional representation by region (see Appendix B for stratification information for each country). Stratification also could be done implicitly, a procedure by which schools in a sampling frame were sorted according to a set of stratification variables prior to sampling. For example, Austria employed implicit stratification by district and school size within each regional stratum. Regardless of the other stratification variables used, all countries used implicit stratification by a measure of size (MOS) of the school.

All countries used a systematic (random start, fixed interval) probability proportional-to-size (PPS) sampling approach to sample schools. Note that when this method is combined with an implicit stratification procedure, the allocation of schools in the sample is proportional to the size of the implicit strata. Within the sampled schools, classes were sampled using a systematic random method in all countries except Morocco and Singapore, where classes were sampled with probability proportional to size, and students within classes sampled with equal probability. The PIRLS 2006 sample designs were implemented in an acceptable manner by all participants.

Sampling deviation

8 National Research Coordinators (NRCs) encountered organizational constraints in their systems that necessitated deviations from the sample design. In each case, the Statistics Canada sampling expert was consulted to ensure that the altered design remained compatible with the PIRLS standards.

These country specific deviations from sample design are detailed in Appendix B of the PIRLS 2006 Technical Report (page 231) attached as Related Material.

Mode of data collection

Face-to-face [f2f]

Research instrument

• PIRLS Background Questionnaires By gathering information about children’s experiences together with reading achievement on the PIRLS test, it is possible to identify the factors or combinations of factors that relate to high reading literacy. An important part of the PIRLS design is a set of questionnaires targeting factors related to reading literacy. PIRLS administered four questionnaires: to the tested students, to their parents, to their reading teachers, and to their school principals.

• Student Questionnaire Each student taking the PIRLS reading assessment completes the student questionnaire. The questionnaire asks about aspects of students’ home and school experiences - including instructional experiences and reading for homework, self-perceptions and attitudes towards reading, out-of-school reading habits, computer use, home literacy resources, and basic demographic information.

• Learning to Read (Home) Survey The learning to read survey is completed by the parents or primary caregivers of each student taking the PIRLS reading assessment. It addresses child-parent literacy interactions, home literacy resources, parents’ reading habits and attitudes, homeschool connections, and basic demographic and socioeconomic indicators.

• Teacher Questionnaire The reading teacher of each fourth-grade class sampled for PIRLS completes a questionnaire designed to gather information about classroom contexts for developing reading literacy. This questionnaire

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Reading, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 12,123, while the mean income for the highest quintile (20% of households with the highest income) is 144,286. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 183,290, which is 127.03% higher compared to the highest quintile, and 1511.92% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reading median household income. You can refer the same here

During a survey held in early 2021, it was found that 83 percent of adults aged between 18 and 29 years old had read a book in any format in the previous year, up by two percent from the share who said the same in 2019. The survey results showed that adults within this age category were more likely than older respondents to have read a book within the last twelve months.

Book readers in the U.S.

While it is mostly believed that book reading is a vanishing pastime, particularly among Millennials, surveys among consumers in the U.S. have shown the opposite. The share of book readers in the U.S. has varied from 72 percent to 79 percent between 2011 and 2016.

In regards to age of book readers in the country, a 2016 survey shows about 80 percent of respondents between the ages of 18 to 29 had read at least one book in the previous 12 months, the highest share amongst all age groups. About 73 percent of the respondents aged between 30 to 49 years old said they read at least one book in the last 12 months. The share among respondents between 50 and 64 years old stood at 70 percent, whereas 67 percent of respondents aged 65 plus stated reading book during the time measured. In terms of education level, book readers in the U.S. are more likely to have a college degree, or at least some college education – 86 percent and 81 percent respectively. Women in the U.S. read slightly more than men; 68 percent of male respondents started reading at least one book in the previous 12 months, against 77 percent of female respondents that said the same.

Despite the rise of digital platforms and the rising popularity of e-reading devices such as Kindle, Kobo and others, printed books still remain the most popular book format in the U.S., as 65 percent of Americans stated preference for printed books in 2016. E-books were consumed by 28 percent of respondents in 2016, whereas audio books were listened by 14 percent of the respondents. Millennials accounted for the largest share of printed book readers in the U.S. – 72 percent as of 2016.

In early 2021, a survey found that 59 percent of adults in the United States with high school education or less had read or listened to a book in the last year. By contrast, almost 90 percent of adults who had graduated college or pursued further education after college had engaged with a print, e-book, or audiobook in the 12 months leading to the survey.

United States US: School Enrollment: Primary: % Gross data was reported at 99.279 % in 2015. This records an increase from the previous number of 98.568 % for 2014. United States US: School Enrollment: Primary: % Gross data is updated yearly, averaging 98.820 % from Dec 1971 (Median) to 2015, with 41 observations. The data reached an all-time high of 105.806 % in 1990 and a record low of 88.391 % in 1972. United States US: School Enrollment: Primary: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Reading, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 21,085, while the mean income for the highest quintile (20% of households with the highest income) is 200,935. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 312,355, which is 155.45% higher compared to the highest quintile, and 1481.41% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reading town median household income. You can refer the same here

ABSTRACT This article presents the results of a cross-sectional descriptive research carried out in a university in south central Chile, in which teachers of Basic Education in training and in active participation, on graphic representations, due to the role they play in the teaching and learning process from the first year of Basic Education in the Chilean school curriculum. For the collection of information, an instrument validated statistically by the authors was applied, which showed a reliability (Cronbach's alpha) of 0.78, which allowed to evaluate the coding and decoding abilities of quantitative information summarized in univariate and bivariate graphical representations, making use of Kimura's taxonomy (1999), whose introduction at the school system level would generate the skills demanded by the national curriculum. The results show that, in general, teachers in training and active have high average percentages in the basic reading ability of univariate information, summarized in polygons of simple frequency. However, the percentages decrease to 61.3% in the future professors and to 50% in the professors in active, because it is a histogram, representation widely used to show the distribution of quantitative information in any course of statistics at national and international level. These results, novelty in the Chilean context, allow us to observe that the teachers in training and in active have not sufficiently developed the ability to make a joint reading of two quantitative variables summarized by means of a graphic representation, demanding innovation in the processes of instruction that guarantee an improvement in learning.

BackgroundThe capacity to listen effectively greatly influences learning. Speech understanding in noise, a crucial element of listening, involves the ability to comprehend and interpret speech despite surrounding auditory distractions or background noise. While numerous studies have examined the effect of noise on academic success, there is a noticeable lack of research focusing on how a student’s skill in understanding speech amidst noise connects to their unique educational achievements and overall learning journey. This study explores the relationship between listening ability and educational achievement.Design108 primary school aged children; 55 from Grade 3 (mean age of 8 years 9 months) and 53 from Grade 5 (mean age of 10 years 7 months) participated in this study. Children completed both a listening skills assessment, using the Sound Scouts platform and the National Assessment Program - Literacy and Numeracy (NAPLAN). Interactions between results were explored.ResultsA significant interaction between speech-in-noise ability and reading was seen in the combined cohort. When exploring interactions between variables within year levels, significant correlations between literacy (reading, grammar/punctuation, writing and spelling) outcomes, but not numeracy, were found in the Grade 3 children. No significant interaction between listening skills and academic achievement were observed in the Grade 5 cohort.SummaryThe link between speech-in noise ability and literacy development provides insight into overlapping processes in both skills and their developmental trajectories. This highlights the importance of identifying not only the role that noise itself plays in learning, but the skills that support the ability to manage classroom listening environments. This research has implications for early hearing screening programs, teaching approaches, and interventions focused on enhancing the learning environment for all students.

Abstract

Though the National Bureau of Statistics generates youth and adult literacy data regularly on annual basis, the survey was conducted with a wider scope to complement the existing data on literacy in Nigeria. The main purpose of the survey was to determine the magnitude, levels and distribution of adult literacy and obtain comprehensive data and information with a view identifying issues of concern, which need to be addressed in the promotion of adult literacy in Nigeria. Underlying this is the fact that literacy is fundamental to information dissemination, socio-economic development and poverty alleviation among others. It was the first attempt to carry out a stand alone survey on Literacy Survey Nigeria.

The objectives of the 2009 National Literacy Survey were to: - Determine the magnitude, level and distribution of mass literacy (persons aged 15 year and above) - Obtain comprehensive data and information on mass literacy from literacy providers and stakeholders in both private and public sectors - Identify issues of concern which need to be addressed in the promotion of mass literacy in the country - Determine the number of persons aged 6 – 14 that are out of school - Ascertain number of persons mainstreaming from non-formal to formal education or vice versa

Geographic coverage

The survey will cover all the 36 states and Federal Capital Territory (FCT). Both urban and rural areas will be canvassed

Analysis unit

Household level

Kind of data

Sample survey data [ssd]

Sampling procedure

2.1 Sample Design 2.1.1 Introduction of NISH Design 1993/99

The Multiple Indicator Cluster Survey (MICS) 1999 was run as a module of the National Integrated Survey of Households (NISH) design. NISH is the Nigerian version of the United Nations National Household Survey Capability Programme and is a multi-subject household based survey system. It is an ongoing programme of household based surveys enquiring into various aspects of households, including housing, health, education and employment. The programme started in 1981 after a pilot study in 1980. The design utilizes a probability sample drawn using a random sampling method at the national and sub-national levels.

The main features of the NISH design are:

Multi-Phase Sampling: In each state 800 EAs were selected with equal probability as first phase samples. A second phase sample of 200 EAs was selected with probability proportional to size.

Multi-Stage Sampling Design: A two-stage design was used. Enumeration Areas were used as the first stage sampling units and Housing Units (HUs) as the second stage sampling units.

Replicated Rotatable Design: Two hundred EAs were selected in each state in 10 independent replicates of 20 EAs per replicate. A rotation was imposed which ensured 6 replicates to be studied each survey year but in subsequent year a replicate is dropped for a new one, that is, a rotation of 1/6 was applied. This means in a survey year, 120 EAs will be covered in each state. In the Federal Capital Territory (Abuja), 60 EAs are covered.

Master Sample: The EAs and HUs selected constitute the Master Sample and subsets were taken for various surveys depending on the nature of the survey and the sample size desired. In any one-year, the 120 EAs are randomly allocated to the 12 months of the year for the survey. The General Household Survey (GHS) is the core module of NISH. Thus, every month 10 EAs are covered for the GHS. For other supplemental modules of NISH, subsets of the master sample are used. The MICS 1999 was run as a module of NISH.

2.1.2 Sample Size

The global MICS design anticipated a sample of 300-500 households per district (domain). This was based on the assumption of a cluster design with design effect of about 2, an average household size of 6, children below the age of 5 years constituting 15 percent of the population and a diarrhoea prevalence of 25 percent. Such a sample would give estimates with an error margin of about 0.1 at the district level. Such a sample would usually come from about 10 clusters of 40 to 50 households per cluster.

In Nigeria, the parameters are similar to the scenario described above. Average household size varied from 3.0 to 5.6 among the states, with a national average of about 5.5. Similarly, children below 5 years constituted between 15-16 percent of total population. Diarrhoea prevalence had been estimated at about 15 percent. These figures have led to sample sizes of between 450 and 660 for each state.

It was decided that a uniform sample of 600 households per state be chosen for the survey. Although non-response, estimated at about 5 percent from previous surveys reduced the sample further, most states had 550 or more households. The MICS sample was drawn from the National Master Sample for the 1998/99 NISH programme implemented by the Federal Office of Statistics (FOS).

The sample was drawn from 30 EAs in each state with a sub-sample of 20 households selected per EA. The design was more efficient than the global MICS design which anticipated a cluster sub-sample size of 40-50 households per cluster. Usually, when the sub-sample size was reduced by half and the number of clusters doubled, a reduction of at least 20 percent in the design effect was achieved. This was derived from DEFF = 1 + (m-1) rho where m is sub-sample size and rho is intra-class correlation. Therefore, the design effect for the Nigerian MICS was about 1.6 instead of 2. This means that for the same size of 600 households, the error margin was reduced by about 10 percent, but where the sample was less than 600 the expected error margin would be achieved.

It should be noted that sampling was based on the former 30 states plus a Federal Capital Territory administrative structure [there are now 36 states and a Federal Capital Territory].

2.1.3 Selection of Households

The global design anticipated either the segmenting of clusters into small areas of approximate 40-45 households and randomly selecting one so that all households within such area was covered or using the random walk procedure in the cluster to select the 40-45 households. Neither of the two procedures was employed. For the segmentation method, it was not difficult to see that the clustering effect could be increased, since, in general, the smaller the cluster the greater the design effect. With such a system, DEFF would be higher than 2, even if minimally. The random walk method, on the other hand, could be affected by enumerator bias, which would be difficult to control and not easily measurable.

For NISH surveys, the listing of all housing units in the selected EAs was first carried out to provide a frame for the sub-sampling. Systematic random sampling was thereafter used to select the sample of housing units. The GHS used a sub-sample of 10 housing units but since the MICS required 20 households, another supplementary sample of 10 housing units was selected and added to the GHS sample. All households in the sample housing units were interviewed, as previous surveys have shown that a housing unit generally contained one household.

Sampling deviation

There were no deviation from sample design

Mode of data collection

Face-to-face [f2f]

Research instrument

The study used various instruments to collect the data. Apart from the main questionnaire that was developed for the survey and targeted the households and individuals, there were other instruments for the conduct of the assessment tests. The main questionnaire was structured in English Language but the interviewers were trained to translate and conduct the interview in local languages.

The questionnaire contains nine parts (A - I). Part A: Identification information Part B: Socio demographic background (all members) Part C: Educational attainment
Part D: Educational attainment
Part E: Literacy in english
Part F: Literacy in any other language Part G: Literacy in english Part H: Literacy in any other language Part I: Knowledge and accessibility of literacy programme

Cleaning operations

The 2009 National Literacy Survey data was processed in 4 stages namely, manual editing and coding, data entry, data cleaning and tabulation.

• Manual Processing - Completed questionnaires started arriving at the NBS headquarters two weeks after training from the states. Manual processing started with the development of editing/coding guidelines which were used to train the officers on manual editing. Development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and Census and Surveys Processing System (CSPro) for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation.

The guidelines include errors that could be found in the completed questionnaires and how they could be corrected. These likely errors include omissions, inconsistencies, unreasonable entries, impossible entries, double entries, transcription errors and others found in the questionnaires. 10 officers were selected as editors, while 20 data entry staff were used in addition to 3 programers.

• Data Entry - The data entry was done manually, using the following steps: 1) Questionnaire reception 2) Office editing and coding 3) Data entry 4) Structure and completeness checking 5) Verification entry 6) Comparison of verification data 7) Back up of raw data 8) Secondary editing 9) Edited data back up After all clusters are processed, all data is concatenated together and then the following steps are completed for all data