When the author refers to the average child, they do not include children who are cognitively different than average. Average is only one statistical measure, and does not properly characterise each individual within the population studied. Outliers are often not included in the summary data analysis because they do not fall into the middle thirds, and do not fit the narrative of trends. Page four of the case study describes the middle, the area inbetween the first standard deviation, the average of the population. By choosing to not including analysis of those who fall outside of that measurement we are not able to see the picture that is presented in the data. This is not to say that there is no value in determining the average, but rather …show more content…
It is this bias that creates what we refer to as “invisible people.” Invisible people are those who are overlooked as they are outside of the norm, and are not included in a statistically meaningful way. To capture a snapshot on the topic to be understood, scientists, as well as authors, often generalize populations. The issue that presents itself is that there is not an average person, as the study by Daniels states. In his research 4,000 flying personnel were investigated, and there was not one who fit the characteristics of the “average person.” If we insist on only using research that includes the average person we would miss the truth of the what exists in the population. We must use our knowledge that no one person falls into the average, but instead utilize the it as a point of information. This can help us to characterize what does fall outside the norms, and can provide us a yardstick in determining the distance those measurements that …show more content…
The case study makes it clear that “Any minority group is, by definition, going to have only a small effect on the average.” While I agree with this statement, we need to walk away from the the weight that is typically held by the usage of average. The information that is important to us may not be accurately described by the usage of average. For example, “The average human has less than 2 legs.” While this statement is true, it does not tell much about what it means to us as
Joel Best’s Damned Lies and Statistics is a book all about recognizing statistics that are legitimate and others that are really quite horrible. The goal of this book is not that the average every day person be able to read a statistical table from a scholarly journal, but rather that anyone could personally value a statistic he or she may come across in a newspaper article or on a news program. Best was essentially effective in achieving his goal; however, he was effective to the point of overdoing his job of showing that there are bad statistics which give readers cause to evaluate them outside of hearing them on the news.
2. The researcher does not want or need to generalize the results to a population.
Age-equivalent scored also do not represent children who scored extremely high and extremely low on the given test. Age-equivalent scores are not estimated for the extreme scores at either end of the spectrum. Children that fall within these ranges are given a generalized age-equivalent score of below the lowest age derived or above the highest age. This results in inadequate information for all individuals that scores are reflected on these parts of the
Acknowledged by Muuss (1967) was the fact that age norms described within stages are approximations, and how such ideas could provide guidance and insight into a child’s learning limitations and abilities. Evidence to suggest that this knowledge can help to address limitations in child cognitive development exists (Ojemann & Pritchett, 1963; as cited in Muuss, 1967). Even so, these proposals are not noticeably grounded by concrete
Minorities have been the victims of prejudice and discrimination for many years (Dion, 2002). Certain minorities such as African American’s have been targeted and treated unfairly (Zastrow, 2007). For example, a Caucasian person can go into a store to shop without being followed or harassed however, an African American may not have the same experience. Throughout America in some instances Hernstein and Murray asserts that Caucasians are more intelligent, because IQ test demonstrate Caucasians average scores of 10 to 15 points higher than African Americans. Research revealed that those IQ test were racially imbalanced (Zastrow, 2007). American culture has been ambivalent, viewing race and ethnicity both as sources of pride, meaning, and motivation as well as sources of prejudice, discrimination, and inequality. Prejudice is a combination of stereotyped beliefs and negative attitudes (Markus, 2008).
Education is sometimes viewed as a pathway to a better lifestyle. Many people are more privileged and have higher education than others. Minority students, on average, perform less well than white students in school; although, Asian-Americans are an exception to this rule (Melissa Doak). Various resources show the statistical differences among ethnic races in their performance of education. According to the National Center for Educational Statistics, minorities with lower levels of education have higher unemployment rates and lower median incomes (National Center for Educational Statistics). Education in minority groups can greatly differ from other races, when education should be equal and available for all.
This essay examines the advantages and disadvantages of using a method primarily for gathering research on human subjects that can be examined for later use. It will give a basic outline of the methods of investigation, their uses and their suitability. I will also look at the scientific method as a whole and examine the criticisms of this method using the writings of Hume and Popper.
The articles, published after 1996, contain varied methods of research attainment, but share similarities such as being a self-survey, having a small sample size, and being
The study consisted of a significant number of females compared to males, which makes it invalid to conclude that the findings support the general population. A strength was that participants were selected at random. By doing so, the study remained unbiased, thus making the results more credible.
The father of quantitative analysis, Rene Descartes, thought that in order to know and understand something, you have to measure it (Kover, 2008). Quantitative research has two main types of sampling used, probabilistic and purposive. Probabilistic sampling is when there is equal chance of anyone within the studied population to be included. Purposive sampling is used when some benchmarks are used to replace the discrepancy among errors. The primary collection of data is from tests or standardized questionnaires, structured interviews, and closed-ended observational protocols. The secondary means for data collection includes official documents. In this study, the data is analyzed to test one or more expressed hypotheses. Descriptive and inferential analyses are the two types of data analysis used and advance from descriptive to inferential. The next step in the process is data interpretation, and the goal is to give meaning to the results in regards to the hypothesis the theory was derived from. Data interpretation techniques used are generalization, theory-driven, and interpretation of theory (Gelo, Braakmann, Benetka, 2008). The discussion should bring together findings and put them into context of the framework, guiding the study (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). The discussion should include an interpretation of the results; descriptions of themes, trends, and relationships; meanings of the results, and the limitations of the study. In the conclusion, one wants to end the study by providing a synopsis and final comments. It should include a summary of findings, recommendations, and future research (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). Deductive reasoning is used in studies...
Smith, P. K., Cowie, H., & Blades, M. (1998). Understanding children’s development, third edition. Malden: Blackwell Publishers Inc.
This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However, reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain group’s agenda. Although most statistical data presented may not seem to affect us personally in our daily lives, it can however have an impact. For example, statistics can influence the way people vote on certain issues.
In addition, various research has been done on the effects of the child’s age upon entering the classroom. To research the effect age could potentially have on children, a study was done in British Columbia that focused on 930,000 children between the ages of 6 to 12, from the years 1997 to 2008. They found that those born in December, typically some of the youngest in the class, “were 30 percent more likely to be diagnosed and 41 percent more likely to be treated with ADHD medication that boys born in January” (Dotinga). The research also showed similar results for girls. Although, the article claims that the findings, “…don’t prove definitively that any kids are...
Quantitative methods in the social sciences are an effective tool for understanding patterns and variation in social data. They are the systematic, numeric collection and objective analysis of data that can be generalized to a larger population and seek to find cause in variance (Matthews and Ross 2010, p.141; Henn et al. 2009, p.134). These methods are often debated, but quantitative measurement is important to the social sciences because of the numeric evidence that can be used to drive more in depth qualitative research and to focus regional policy, to name a few (Johnston et al. 2014). Basic quantitative methods, such as descriptive and inferential statistics, are used regularly to identify and explain large social trends that can then
Researchers, professionals and others use statistics to prove their claims or findings. Even though statistics are not an absolute fact because the conclusion is mostly drawn from a sample group – representative of a specific population subjected to the research, it is commonly used as the basis of decision making or alternating choices in daily living, studies, works, scientific research, politics and other planning. The inventor of a documentary film called “An inconvenient truth”, Mr. Al Gore, for instance, in his campaign to educate people about the climate change, used statistics to alert people that everyone on earth is polluting the environment and should participate in solving the problem. He collected data from many different countries with an in...