Pen-and-paper homework and project-based learning are both commonly used instructional methods in introductory statistics courses. However, there have been few studies comparing these two methods exclusively. In this case study, each was used in two different sections of the same introductory statistics course at a regional state university. Students’ statistical literacy was measured by exam scores across the course, including the final. The comparison of the two instructional methods includes using descriptive statistics and two-sample t-tests, as well authors’ reflections on the instructional methods. Results indicated that there is no statistically discernible difference between the two instructional methods in the introductory statistics course.

Abstract

The Project for Statistics on Living standards and Development was a coutrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Universe

All Household members.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 9,000 households

The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.

The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.

In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.

In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described abovefor the households in ESDs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demography, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire referred to above, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

Context

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Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

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Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This statistic displays the number of animals used in scientific research in Ireland in 2023, by project purpose. In that year, approximately 75 thousand animals were involved in regulatory use and routine production.

Load and view a real-world dataset in RStudio

• Calculate “Measure of Frequency” metrics

• Calculate “Measure of Central Tendency” metrics

• Calculate “Measure of Dispersion” metrics

• Use R’s in-built functions for additional data quality metrics

• Create a custom R function to calculate descriptive statistics on any given dataset

The statistic reveals the share of projects regarded as successful by chief information officers (CIOs) over the last 12 months, as of 2016. As of that time, 42 percent of respondents indicated that the projects for offshoring IT functions completed in the last 12 months had been successful.

Financial overview and grant giving statistics of Rachels Fun for Everyone Project

Statistics of approved projects of Innovation and Technology Fund

Annual Statistics of Approved Projects under General Support Programme

In the PID database, the URL of the data object, such as PDF, video or data set, is stored. This is basically considered a service. A handle software is run to allow us to create these database entries to generate prefixes and suffixes. This process is carried out for many customers in ePIC (Persistent Identifier Consortium for eResearch), where they cooperate for handle servers. What is currently needed is a service on top of handle servers that provides a descriptive overview. An important issue here is that it isn’t sufficient to create PID prefixes and PIDs themselves. It should be easily understandable to the users how many prefixes are created and how many PIDs are in those prefixes. (This project work report was conducted in preparation for the Master thesis and was supervised by Dr. Sven Bingert and Triet Doan)

The Elementary-Secondary Education Statistics Project (ESESP) is a national survey that enables Statistics Canada to provide information on enrolments (including minority and second language programs), graduates, educators and finance of Canadian elementary-secondary public educational institutions.

Financial overview and grant giving statistics of Project Cool Breeze

BACKGROUND The data contained in the compressed file has been extracted from the Marketing Carrier On-Time Performance (Beginning January 2018) data table of the "On-Time" database from the TranStats data library. The time period is indicated in the name of the compressed file; for example, XXX_XXXXX_2001_1 contains data of the first month of the year 2001.

RECORD LAYOUT Below are fields in the order that they appear on the records: Year Year Quarter Quarter (1-4) Month Month DayofMonth Day of Month DayOfWeek Day of Week FlightDate Flight Date (yyyymmdd) Marketing_Airline_Network Unique Marketing Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. Operated_or_Branded_Code_Share_Partners Reporting Carrier Operated or Branded Code Share Partners DOT_ID_Marketing_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Marketing_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Number_Marketing_Airline Flight Number Originally_Scheduled_Code_Share_Airline Unique Scheduled Operating Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users,for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Originally_Scheduled_Code_Share_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Originally_Scheduled_Code_Share_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Num_Originally_Scheduled_Code_Share_Airline Flight Number Operating_Airline Unique Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Operating_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Operating_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Tail_Number Tail Number Flight_Number_Operating_Airline Flight Number OriginAirportID Origin Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. OriginAirportSeqID Origin Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. OriginCityMarketID Origin Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Origin Origin Airport OriginCityName Origin Airport, City Name OriginState Origin Airport, State Code OriginStateFips Origin Airport, State Fips OriginStateName Origin Airport, State Name OriginWac Origin Airport, World Area Code DestAirportID Destination Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. DestAirportSeqID Destination Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. DestCityMarketID Destination Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Dest Destination Airport DestCityName Destination Airport, City Name DestState Destination Airport, State Code DestStateFips D...

An Investigation Designed to Teach Statistical Thinking in the Midst of the COVID-19 Pandemic: Are Teens Living Like Vampires?

To provide the public with a convenient channel for accessing information and expressing opinions, the National Development Council has established the "Public Policy Online Participation Platform - to supervise." It graphically presents government project information with the aim of expanding public participation, showcasing administrative performance, enhancing information value, promoting two-way interaction between the government and the public, and implementing open accountability. The case project data refers to the important annual work items promoted by each agency based on national development plans and annual administrative plans, as well as information on the basic data and implementation of case projects for the past five years provided by various agencies on the government's open data platform.

Dataset

This dataset was created by Cindy Zhao

Released under Apache 2.0

Contents

Annual statistics of approved projects under Technology Voucher Programme by type of business

Dataset

This dataset was created by Amir Hashemi

Contents

R code and txt files for climate relation evaluation. All data stems from http://data.giss.nasa.gov/gistemp/ Temperature datahttp://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt CO2 data:ftp://aftp.cmdl.noaa.gov/data/trace_gases/co2/flask/surface/co2_mlo_surface-flask_1_ccgg_month.txt Methane data: http://www.esrl.noaa. gov/gmd/obop/mlo/programs/esrl/methane/methane.html

About this dataset

Context

The dataset tabulates the Gate household income by age. The dataset can be utilized to understand the age-based income distribution of Gate income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Gate, OK annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Gate, OK household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Gate income distribution by age. You can refer the same here