價格：免費
更新日期：2019-05-14
檔案大小：6.7M
目前版本：Genotype
版本需求：Android 2.1 以上版本
官方網站：https://daatechnology.com
Email：daaticas@gmail.com
聯絡地址：隱私權政策
DaaTech Data Sampling Tool is a generic Android App that uses Random Sampling Technique by a way of selecting a sample of observations from a population in order to make inferences about the population.
The Application is generic in the sense that, is can calculate and sample with the two main type of data.
Which are the Qualitative and Quantitative data types.
Quantitative Data Type
Under this setting, the user just have to set the targeted populating in a responsive text box.
Example, Select any item (Could be number) from A - T or 1 - 20.
Qualitative Data Type
This is the settings that makes this App different from any other, the user will Define (Set) the variables and use the "ADD" button to command the Application to choose any variable at Random.
Example, Select any item (Days of the Week, Months, or anything) defined by the user such as:
a. Youth, Children, Adults
b. List of Regions ( Eastern, Bono, Ashanti etc.)
In this simple to use App, the user have to type the target population or Define the variables and press on "RUN SAMPLE" to get a random sample results.
For Researchers, the Application works with any of the All the Main types of Random Sampling including:
Simple random sampling
Simple random sampling is the most straightforward approach for getting a random sample. It involves picking a desired sample size and selecting observations from a population in such a way that each observation has an equal chance of selection until the desired sample size is achieved
Stratified random sampling
Stratified random sampling involves breaking a population into key subgroups and obtaining a simple random sample from each group.
These subgroups (e.g., males under 30, females under 30, males 30 or over, and females 30 or over) are called strata. For example, if you want a sample size of 200, then you can pick samples of 50 from each strata. The required sample size for each stratum will be designed either to match known population proportions, or to over represent key subgroups of interest.
The main benefit of stratified sampling over simple random sampling is making sure that you have good sample sizes in key subgroups.
Cluster sampling
Cluster sampling is like stratified random sampling, except that the population is divided into a large number of subgroups (e.g., hundreds of thousands of small subgroups); then some of these subgroups are selected at random, and simple random samples are then collected within these subgroups. These subgroups are called clusters.
Typically, the purpose of cluster sampling is to reduce the costs of data collection. This is achieved by defining clusters according to ease of access (e.g., a suburb may be a cluster if door-to-door sampling, or a household may be a cluster if phone interviewing).
