Dr Tabor's Research

Luker describes sampling through canonical social science research as a “systemic random probability sample”, with the goal of producing results that can then be apply and a general way. For example, with a research question that focuses on voting in a election, A canonical social science research question may trying to determine how many voters are expected to vote a certain way in a election. By using randomly sampling a certain number of voters could be surveyed the result can provide a general description of the voting trend of an entire populations with similar characteristic as the sample group. While I agree with Luker that sampling is important to both canonical qualitative and qualitative social science research, in the case of qualitative research, the word has different meaning.

According to Schutt (2008), sampling is defined as a subset of population used in a study to be a representation of the population as a whole. My final project is a pre-hire assessment which analyzes potential risky pattern behaviors and emotions in the work place. One of the most important considerations related to sampling that will need to be addressed in my final project is defining the population that will be taking the assessment.

Explain the importance of random sampling. What problems/limitations could prevent a truly random sampling and how can they be prevented?

The two examples of surveys are cluster sampling and stratified sample. Cluster sampling is a probability sample that is a random select masses of individuals having certain appearance in common, for example all students taking a history class. Stratified sample is probability sample that is random with a minor group that are relational represented, for example students in a english class spend more money on books than engineering students so if we used large percentage of english or engineering students then the result won't be accurate.

The main purpose of Unit 3 of The Basics of Introductory Statics, Chapters 11-16 is to introduce the students to inferential statistics, ANOVA, sampling, analysis of variance and chi square. Inferential Statistics is a work with the relationship between samples and populations in other words, the strength of the effect and error. There are many kinds of sample(s) when working in Unit 3, there is related samples, distribution sampling, population samples and independent samples. So, you may ask what is considered a sample in statistics? In statistics, a sample refers to a set of observations drawn from a population.

Responders rate how much they experienced each word on a scale from 1 to 5: 1 “very slightly or not at all”, 2 “ a little”, 3 “moderately”, 4 “quite a bit” and 5 “extremely”. Score is obtained by adding the numbers and provides separate totals for positive affect (PA) and for negative affect (NA). The scale also offers a time specification, varying from the present moment to a general perspective. So the instructions for filling the questionnaire would read “indicate to what extent you feel this way (right now, today, this week, this year or in

In this study, it uses surveys to analyze 135 seabirds to find that today about 90 seabirds have traces of plastic in their stomachs. A survey is what allows us to ask/study large numbers or people (in this case seabirds) questions about attitude and behavior (textbook p.44). The sample size for this specific study would be 135 which is the subset of a population being studied. The population in this case would be all seabirds, which is the entire group from which the sample was taken

Although this is convenient for the researchers. As the researcher states in the limitations, the conclusion may be different for less experienced participants or those who were trained differently. There is no explanation as to why the participants chose to take part in the study, nor why others chose to decline the opportunity. This would be valuable information as there may be a specific group of people that decide to volunteer for research studies. The article is also unclear about how the volunteers came to know about the research and what they were told before the research commenced. Convenience sampling is most commonly used in larger- scale studies and therefore seems an outlandish method to use, as only 10 participants were used in this

A sample is used when a portion of the target population must be used; neither time nor money allows the entire population to be used. A census would be the other form of data collection because the entire population is used. The portion used in the sample must be carefully selected to represent that population. If sampling is chosen, the researcher must determine which and how many people to interview, which and how many events to observe, or which and how many records to inspect (Cooper, 2014, p. 112). Choosing which sample method to be used is very important because this will be the basis for the way data is collected. If the wrong sample method is chosen the data will not be accurate, and in turn the analysis and the outcome will also be incorrect. For the purpose of this research, BP will use probability method; this is the best method to use because it will give you the most powerful statistical analyses on the results ("choosing a sampling method," 2013, para. 1). The sampling frame within the probability method used will be stratified sampling. Even though

In the real world, we are constantly looking for ways to make connections between things. When you try to find a correlation between two variables, the variables are known as bivariate data. This is done so that we can analyze things like the connection between a number of ice cream cones bought each day compared to its temperature. In this study, we are calculating the correlation coefficient of the number of chirps a cricket makes per second and the temperature. This is another form of bivariate data since we are measuring the temperature, and we are measuring the chirps per second. For each temperature measure and corresponding chirps per second, we can graph it on a scatter plot and analyze it from there using things like the correlation

The Likert scale, a variation of the summated rating scale, is used to measure a participant’s attitude towards statements as either favorable or unfavorable (Cooper & Schindler, 2014, p.278). In this particular problem, my opinion of the educational degree program in which I am enrolled will be evaluated. Five response categories are being used in the survey, which includes Strongly Agree, Agree, Neither Agree or Disagree, Disagree, and Strongly Disagree. Each response will be valued with a number from 1-5, reflecting the degree of a participant’s positive or negative attitude. Normally, Strongly Agree (SA) represents the most positive attitude and has a scale value of 5, but there is an exception to this criterion if the statement is worded negatively. For example, questions a, d, and f below are considered to be negative statements. If a negative statement occurs, assigned numerical values are reversed to ensure consistent results, whereas a scale value of 5 represents the most negative attitude and a value of 1 is most positive (p. 278). Likert scales produce ordinal data, but not all in the world of research believe that these scales are used correctly. Some researchers/experts believe that ordinal data does not produce mean values as normal practice would suggest. As a result of this belief, drawn conclusions from Likert scales are skeptical in their minds and should be evaluated with caution.

Within this population we are looking for a sample, which is the random information we

Sample does not has an acknowledge chance of being selected as in convenience or voluntary response surveys. Most researchers may be bounded by time, and money. Such limitations do not allows the researcher to randomly sample the entire population so they choose non-probability sampling in which all the members in the population do not receive equal chances of being selected.

You can find your scatterplot in your output file. It will look something like the graph below. You will see a bunch of dots. Your scatterplot can tell you about the relationship between variables, just like Pearson’s r. With it, you can determine the strength and direction of the relationship between variables.

This ‘random sampling error’ indicated that there was no cross section of the target group (generation Y) and in turn was a sample selection error. There were 3 respondents whose results were not analyzed, as they did not fall into the target group of generation Y and this was an administrative error. This is another common research problem is survey non-response. Marketers can unintentionally design surveys which many customers choose not to respond to.