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Qualitative methodology strength and weaknesses
Qualitative research methodology
Qualitative methodology strength and weaknesses
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Introduction
For this SPSS final assignment, we will follow our step by step to create supporting statistical output that will include graphs and tables that will assimilate our text and the appropriate data set in their correct place within our research document. Then analyze and explain each section and the variables so that we can gain a much better understanding of the one-way ANOVA and GPA.
Description of Data File
For our data set, we will explore the association amongst quiz 3 and the section that was given to us to evaluate for unit 10 Assignment 1. We will create a sample size which is 105 for this data set. Our predictor variable will be referred to within the class section because there are 3 class sections which make up our
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1.576 2 102 212
Research Question, Hypotheses, and Alpha Level
The question for our research will be very relevant to our statistical test, therefore, the question will be: Is there going to be any significant dissimilarity amongst quiz 3 in different sections of our data set? Then the null hypothesis question is: Are there (no) any difference in the Quiz 3 by the sections. Then our alternative hypothesis will be the quiz 3 by the section. Therefore, we will have an alpha level of 5%.
Interpretation
Figure 5
So, for figure 5 which is the means plot, we use the means plots to see if our mean will be different with the groups of data. Because when we are ale to see the visual interpretation of this section, we will come to the following conclusions, which we can the mean scores for the section that is higher than our mean scores for the section 2 and 3.
Means Plots
Figure 6
Descriptives
For this section, this is where things get a little harder to explain. The above chart represents what we call the descriptive for the one-way ANOVA. This form helps us to conduct the analyzation of the above chart so that we can determine the standard deviation and the mean scores for the quiz 3 and each section. Therefore, once we know what the section is we can show their
In regards to figure 1, the means were calculated using a simple formula which consisted of finding (x1+x2+x3)/3 (Lab Manual). In the formula, x1 was the recorded threshold from trial 1, x2 was the recorded threshold for trial 2, and x3 was the recorded threshold from trial 3. The formula was repeated and calculated for each body part. Once the means were calculated, they were placed into a graph and displayed in figure 1.
Many statistical ideas were mentioned in the Barron’s guide. In the topic called Graphing Display the Barron’s guide discusses the different types of graphs, measures of center and spread, including outliers, modes, and shape. Summarizing Distributions mentions different ways of measuring the center, spread, and position, including z-scores, percentile rankings, and the Innerquartile Range, and its role in finding outliers. Comparing Distributions discusses the different types of graphical displays and the situations in which each type is most useful or appropriate. The section on Exploring Bivariate Data explains scatter plots in depth, discussing residuals, influential points and transformations, and other topics specific to scatter plots. Conditional relative frequencies and association, and marginal frequencies for two-way tables were explained in the section entitled Exploring Categorical Data. Overview of Methods of Data Collection explained the difference between censuses, surveys, experiments, and observational studies. Surveys are discussed more in depth in Planning and Conducting Surveys, including characteristics of a well-designed and well-conducted survey, and sources of bias. Planning and Conducting Experiments explains experiments in depth; going over confounding, control groups, placebo effects, and blinding, as well as randomization. Basic rules for probability are discussed in Probability as Relative Frequency, including the law of large numbers, addition rule, and multiplication rule. Other topics discussed in this section include the different types of probability calculations. Combining Independent Random Variables discusses manners in which two variables can be compared to each other and things to be wary of while doing so.
The analysis that was used in this study is called a two-way ANOVA. A two-way ANOVA was used since there are multiple independent variables affecting the dependent variable. The independent variable for this study are theory of intelligence level and perfectionism level. The ? broke down perfection level into three distinct categories such as Adaptive, Maladaptive or Non. The dependent variable includes HAQ-II scores which rates nursing home residents therapeutic relationship to their counselors. In this case, A two-way ANOVA was used in order to depict if a main effect existed or if the independent variable correspond to dependent variable. Once significance is found a post hoc analysis called Tukey is used to determine significant differences.
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The scientific findings needs to be used are the following, variable which is a logical set of attributes. The attributes is a characteristic or quality of something. For example, the attributes towards my study, would be the ages of both sex genders from college students and parent 's. Due to the fact, if there 's a chance of inheriting alcohol behavior to consume during the adolescence to young adulthood. "The implication of the level of measurement would be analyses require a minimum level of measurements and some variables can be treated as multiple level of
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...r they complete the short survey, the experimenter will write on the top of the paper; which day the survey was distributed and whether or not they will receive a prize. For day one, we will be conducting the study in business attire, Level 1 of the Independent Variable 2, and half of our participants will be told of a prize at the end of the survey, Independent Variable 1. We will be measuring the ratings of the survey, Dependent Variable, and see if our attire has anything to do with their answers. Day two will be the same thing; the only differences will be that we, the experimenters, will be in workout clothing rather than business attire and that we will be distributing our surveys in a different area. We will again be measuring the effect of our workout attire, Level 2 of the Independent Variable 2; on the ratings of the survey our participants will receive.
One-way analysis of variance (one-way ANOVA) is a technique that is used to determine whether there are statistically significant differences between the means of two or more samples (using the F distribution) when there is only one independent variable. In this case, we used a one-way ANOVA to understand whether students' thoughts on those immigration questions differed based on ethnicity (dividing ethnicty into three indepedent groups (Asian, Hispanic and White students). So, we have three categories, Asian, Hispanic, and Asian. So, our X variable is the ethnicity, and its categorical. The outcome is their opinions on immigration questions, which in this case, it's a one to five, where five is strongly agree, one is strongly disagree. So,
We will be using a random sample of 100 students - 50 boys and 50
The study of evaluation of statistical results made me able to interpret the result in an effective manner in order to clarify the test results and significance of the study. Through the study of these five elements, hopefully I will be able to utilize the knowledge of course in practical life and implement the various elements of statistics in research related to some particular topic Petocz & Reid, (2003). Similarly, by studying this course one could apply the knowledge by evaluating stock exchange data. Once thing I do know is that analysis with inappropriate statistical tests leads us to draw inappropriate and incorrect
In order to obtain an A in this examination, students will demonstrate an in-depth understanding of each of the analyses. These analyses will typically include at least one of the more complex analyses (e.g., M...
The test is administered in similar conditions and similar timing. And the scores of both the groups are analysed for typical statistical parameters such as means and standard deviations.
To test my first hypothesis i.e. as pupils get older the boys and girls get heavier and taller. I will carry out a stratified sample of 60 boys and girls. The reason why I will do a sample is because it will show the different proportions of people in each year group and gender. Therefore my data will be representative of Mayfield High School. Once I have collected the data I will then organize the heights and weights of the girls and boys in a grouped frequency table. I will then use this table to find the mean, mode, median of the results of the heights and weights of these males and females. I will then construct a cumulative frequency graph to find and locate the median, lower quartile and upper quartile. This will then be used to draw a box plot for the heights and weights of male and females. This data will be used to help me to conclude my first hypothesis.
There are 44 test scores from students and I am ask to analyze the data using different methods of graph. Talk about advantages and disadvantages of each graphs lastly describing the “Middle” and the “Spread” of the compiled data and how is it significant overall. There are many different types of graphs mathematician can use to compiles data to help them get a better understanding of everything by seeing it visually before start doing all the calculation and draw conclusion. The 4 types of graphs that I am focusing on this test scores from students are Stem-and-leaf, Histogram, Pie graph, and Box-and-whisker.
4. Question d: Explain the variables you should take into account when assessing page 4