Age and SLA
Paper, Order, or Assignment Requirements
- Introduction
- Literature review
- Significance of Research
- Research Question
- Research Methodology
- Ethical Consideration.
- Limitations of the study.
- Expected Results.
- References
Related posts:
- Biddle and Hamermesh (1990) built a multiple regression model to study the tradeoff between time spent in sleeping and working and to look at other factors affecting sleep: Sleep = β0 + β1 totwrk + β2 educ + β3 age + ε where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. Suppose the following equation is estimated: Sleep = 3500 – 0.15 totwrk – 11.20 educ + 2.29 age + ε Discuss what would happen to someone’s sleep if they choose to work more. Analyze whether the factors of totwrk, educ, and age are enough factors to explain the variation in sleep. Explain which additional factors should be explored in order to explain the variation in sleep. Provide your reasoning.
- Question: Q: Chapter Chapter 11 of Mertler and Vannata; answer exercises on pages 306 and 307: This exercise utilizes the SPSS data setprofile-e.sav, which can be downloaded from this Web site: www.Pvrczak.com/data Conduct a Forward: LR logistic regression analysis with the following variables: IV—age, educ, hrsl, sibs, rincom91, life2 (categorical) DV—satjob2 Note: The variable Iife2 is categorical such that dull = 1, routine/exciting = 2, and all other values are system missing. Develop a research question for the following scenario. Conduct a preliminary Linear Regression to identify outliers and evaluate multicollinearity among the five continuous variables . Complete the following: a. Using the Chi-Square table in Appendix B, identify the critical value atp< .001 for identifying outliers. Use Explore to determine if there are outliers. Which cases should be eliminated? b. Is multicollinearity a problem among the five continuous variables? Conduct Binary Logistic Regression using the Forward: LR method. IV—age, educ, hrsl, sibs, rincom91, life2 (categorical; last is the reference category) DV—satjob2 Note: Make sure that any outliers identified in Exercise 2a are removed from data before running the logistic regression. Also, designating life2 as a categorical covariate with the last category as the reference, essentially makes "routine/exciting" = 0 and "dull" = 1, so interpret the results accordingly. a. Which variables were entered into the model? b. To what degree does the model fit the data? Explain. c. Is the generated model significantly different from the constant-only model? d. How accurate is the model in predicting job satisfaction? e. What are the odds ratios for the model variables? Explain. Module 14 – Multi-level linear analyses: When do you use multi-level linear analyzes? Chapter 8 of Cronk (chapter below I wasn’t sure what was being asked) and answer all practice exercises; post your results here:
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