Sampling Case Study

The sample will consist of a total of 100 male and 100 female residents from Bakersfield, who are aged between 18-65. It will be selected by using the stratified sample technique, which will involve dividing residents into groups based on their gender and then using those groups to conduct our sampling. This will ensure that we are able to obtain an even sample of male and females and allow for determination of any difference between the two groups.

Participants were required to be 18 years or older to participate and were asked to complete an online survey in exchange for extra credit. All participation was voluntary, and students who elected not to partake in the study were provided an alternative extra credit option. In total, 48 females (60% of total) and 32 males (40% of total) between the ages of 18 and 49 (M = 25, Mdn = 23, SD = 6.85) completed the survey. The majority of participants were White/Caucasian (56%) . Other race options included Asian (15%),

How: (see methodology for additional information) We went to The Retreat on November 11 for one hour and asked students sitting alone at tables if they wished to participate in a short, anonymous survey. Survey topics for the control (no topic), neutral (milk preference), and controversial (abortion rights) were rotated per student

The population designated for this research study was second year and third year undergraduate students at a private university called Sunway

Implementing TQM could help people have a better customer service satisfaction. Now that people are more connected than ever to technology more and more people are doing their shopping on line. We want to know if people would rather shop for flowers in the store or would they order on line if the customer satisfaction and prices were better. TQM can help better the customer service satisfaction and continue to make it better.

The probability is one of the sampling techniques of choosing the equivalent elements. These are specified as random sampling. The sampling is helped to develop the sampling frame; it selects the elements as randomly.

Traditional procedures for conducting telephone surveys are challenged in today’s world by the popular use of call-blocking and screening services. Problems arise in determining if a telephone number is linked to a sample unit eligible for participation and also in completing interviews with participants. Researchers seek to revise survey methods and find ways to adjust for non-response and the increasing rate of unknown eligibility in telephone surveys. In “Using Telephone Exchange-Level Data to Adjust For Non-Response: Application in An Establishment Survey,” Williams and Lu suggested using household demographic summary information at the telephone exchange level to decrease non-response bias and increase accuracy in establishment survey results (Williams and Lu 3847).

As the population of this study was the Indian SMEs in Europe which were thousands in numbers; it was notpractical to reach all of them within the timeframe available to complete this dissertation. This made it inevitable to undertake sampling. Sampling is undertaken in order to select units or members that are representative of the population. There are mainly two kinds of sampling namely probability and non-probability sampling (Sekaran and Bougie, 2009). A probability sample is a sample in which every element of the population has a known and equal likelihood of being selected. Non probability sample is any sample in which little or no attempt is made to get a representative cross section of the population (Lamb et al. 2013).

We can see from the results that the selected individuals who participated in the survey consisted of both similar numbers from each gender. This was implemented to gain a higher and yet even number of responses, consequently resulting in a more efficient outcome. 2.

Sampling techniques provide a range of methods that enable the researcher to reduce the amount of data that needs to be collected by considering only data from a subgroup rather than all possible cases (Saunders et al., 2007:204). The researcher collected data from the identified population in a company where the researcher was employed in the payments department and IT provided technological solutions to the business unit.

Indeed, there are numerous of techniques used in sampling. However, there are two comprehensive types of sampling techniques. They are probability sampling and non-probability sampling techniques correspondingly referred to as random and non-random sampling techniques respectively (Cohen, Manion & Morrison, 2007). Probability sampling technique is ordinarily used in large quantitative type research projects. Needless to say, where large sample size is needed from across a broad spectrum of society, it becomes paramount therefore that random sampling technique becomes optimal.

#!/usr /bin/python import sys import operator from decimal import * def get_top_most_frequent_ngrams(n_grams, f): """ Get the top f most frequent n-grams """ sorted_n_grams = sorted(n_grams.items(), key=operator.itemgetter(1)) sorted_n_grams.reverse() return sorted_n_grams[0:f - 1] def extract_character_n_grams(doctext, n): """ Parse a document text and get all the character n-grams along with their frequencies as a dictionary """ n_gram_dict = {} i = 0 while (i + n) < len(doctext): n_gram = doctext[i:n + i] n_gram = n_gram.replace(' ', '_')

Participants were selected through convenience as well as snowball sampling. The subjects were either students in Dr. Shieva Davarian’s SOCL 109-02 Social Research Methods class or acquaintances of the researchers .The participants that were targeted were between the ages of 17 and 22 and were currently enrolled in a university or secondary education program in the United States of America. In total 25 people responded to the survey in which there were 17 female respondents, 7 male respondents, and 1 respondent who did not indicate their gender. However for six of the survey questions some of the participants were excluded due to response error. The study specifically gathered data from Loyola Marymount University, which is a

In this study, we tested the empirical effect that a firm’s capital structure has on its corporate value by using a multiple regression estimator framework. All the financial data are obtained from the China Stock Market and Accounting Research (CSMAR) Database. The sample used for this study are automobile firms listed on the A-share section of Shanghai and Shenzhen Stock Exchanges. Because of significant differences between A and B stock markets in China and the lack of horizontal comparability of financial statements for companies listed in different markets, only A-share listed companies are selected. The determination of sample size is based on the availability of data. The steps followed to create the data sample are:

A ranked random sampling procedure was conducted to recruit a diverse group of individuals. Eligible participants were first-year students who were U.S. citizens or permanent residents, living within 25 miles of the campus, and were younger than the age of