Sampling and Data Collection in Research Paper

Sampling and Data Collection in Research Paper

Dalarse Demby

Research and Statistics in Human Services

Professor Vanessa Byrd

April 10, 2017

Research in the Human Services field is vital. In fields such as psychology and social work, Treatment methods and other important aspects are derived through research. Past practice and past history sheds light on things such as triggers, trends and overall statistics. Research is made of several components; Sampling, Data Collection and the Outcome. All three of these components are necessary in ensuring accuracy in your research.

Sampling is comprised of three parts as well. Step one of sampling is selecting population. Your population is the subject that you intend to collect data for. For example one may be researching Depression. Depression is the topic for in which one is seeking information. The next step in sampling is to select your sample frame. This narrows your target by choosing specifics such as adolescents between the ages 13-18. At this point you know what you’re looking for and in who you’re looking for it. The last step is choosing the method in which you will sample. In most studied there are questionnaires or interviews; however there is no limit in which you collect research. One can conduct a study on participants or conduct a study through published literature. The purpose of sampling is to ensure the accuracy of the data you collect.

A simple random sample is a sample selected in such a way that every possible sample of the same size is equally likely to be chosen. In example playing a game as simple as Duck duck Goose. It is very likely that the same participant may be selected. A stratified random sample is obtained by separating the population into their own separate group using a sequence or like terms. For example take varsity basketball and Junior varsity basketball. The stratified sample would look say Junior varsity consist of girls and boys, listed from 6th through 8th grade. This could even be broken down by team positions. High varsity would be listed as boys and girls from ages 14 through 18. A cluster sample is a simple random sample of groups or clusters of elements.

This method is useful when it is difficult or costly to develop a complete list of the population members or when the population elements are widely dispersed geographically. This type of sampling can lead to an error which is usually caused by the method of data collection being used in the research. If information is recorded incorrectly, measurements are off or if honest answers were not giving this could result in error.  

There are four types of data and measurements scales; Nominal, Ordinal, Interval and Ration. Nominal is that of labeling collected data which has no quantitive value. A nominal variable is meant to be descriptive. It differentiates between labels such as male or female. There is no number value associated with a nominal variable. Next is Ordinal which indicates the order of value in a variable. However the exact difference is not known. Such as large is bigger than medium and medium is bigger than small, however there is no way to know how much of a difference is between each variable. Interval scales are numeric variables for in which you know both the order and the value difference in the order. For example ten is 5 units larger than 5. Although interval scales are the easiest to understand they may not be ideal for all research projects. Such as ratios which is the last scale of measurement. Ratio. Ratios not only give you the exact value between units and they also have an absolute zero which allows for a wide range of both descriptive and inferential statistics. Ratio is the most commonly used in research because These variables can be meaningfully added, subtracted, multiplied, divided (ratios).  Central tendency can be measured by mode, median, or mean; measures of dispersion, such as standard deviation and coefficient of variation can also be calculated from ratio scales.