Question
You will be completing the Methods section of the proposal. It is important that you are clear on your design This is because it will dictate which data analytic technique you will use to test your hypotheses.

For instance, will you use a T-Test for Independent Samples or a T-Test for Related Samples? An independent t-test would be used for a between subjects design (comparing people to each other). a t--test for related samples would be used in a within subjects design (comparing people to themselves as in a pre-post test design). Are you looking for a relationship between variables? You would use a pearson r.

If you have more than one hypotheses, one may be comparative but you may also have an associational (correlation) hypothesis in which you are looking for a relationship between the variables. For example,' the more people rock climb, the significantly stronger they become'. That would be posing a positive relationship between 2 variables. (as one variable increases, so does the other). Another example would be 'the healthier a person eats, the significantly more weight they lose'. That would be a negative relationship because as one variable increases, the other decreases.

In the Overview of Design section this week, ensure that you include the following information:
1. The type of design you have? (see Gliner pages 6-14) (e.g. qualitative or if quantitative: quasi-experimental comparative between subjects design)
If you have a Mixed-Methods or quantitative design, you will also need to include the following information:
2. Between or within subjects? (Gliner pages 261-272))
3. Operationally define your IVs and DVs
4. State the statistical test (s) you will use to test your hypothesis(s) and why you chose this one. (Gliner pages 275-285)
Participants and Sampling Plan:
1. Where are you going to collect your data?
2. What sampling technique will you use? Is it parametric or non-parametric (e.g. are you using a type of random sample). Why is this technique the most appropriate? (Gliner pages 115-130)
3. How many participants are you going to include in the sample?
4. What are the demographics of your sample? (e.g. men/women)
Measures:
Include all instruments that you plan to use to collect your data. When designing your instrument keep the following in mind: What is my objective? How long should it be? How many questions do I need to measure a construct such as honesty, job satisfaction, happiness, etc. ? (I would recommend at least 3 for each construct)
Remember to add demographic questions (even if the data is qualitative). After you initially write the questions, go back to your IVs and DVs and make sure that you asked questions to measure them according to how they have been defined. Questions should be unambiguous (e.g. not open to personal interpretation). Give examples so all respondents are answering in the same frame of reference for closed-ended questions. For measuring DVs, stay away from dichotomous questions such as yes/no. Those are nominal (categorical) questions that are not continuous and therefore have no variability (can't calculate a mean).
Bad Question
How many days in the past week have you done any exercise? When you consider exercise be sure to include walking, work around the house, or work on a job, if you think they constituted exercise.
A bit better Question
The next question is going to ask you about how ofter you've engaged in exercise. We want you to include walking, or anything you may do around the house or work you do on the job if you believe it is classified as exercise. So, using this definition, in the last week, on how many days did you do any exercise?
Best Question
The next set of questions are going to ask you about how ofter you've engaged in exercise. On a scale of 1-7, please rate how many days last week (Sunday-Saturday) that you engaged in walking for at least 30 minutes?
You would write a question for each type of exercise that you were interested in collecting data on.
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The main issue of interest in this study is the effects of socio-economic background (SES) of the students and its relationship with academic performance. Specifically, the study will investigate the transmitting mechanisms associated with the socio-economic background that aid in improving or deterring a student’s academic performance. For example, a student skips school due to the inability to pay school fees. Studies in both children and adults have shown that students from low social economic backgrounds are more likely to perform poorly academically (Ahmar & Anwar, 2013, and Duan, Guan, & Bu, 2018). Investigating this problem is crucial for numerous reasons. Conducting a scientific inquiry may help to fill gap in the research and bring attention to controversies and debates on the topic. In addition, the findings generated from the study may spark further research thus expanding the current body of research. A further rationale for investigating this topic is that policymakers in the United States may gain an understanding of the effect of an American students’ socioeconomic background on their academic performance. This may in turn help authorities realize the need for policies and programs to address any negative link between low socioeconomic status among the Hispanics and their students’ academic performance.
Research Proposition
The central research question that this study aims to answer is: Is there a noteworthy relationship between students’ socioeconomic backgrounds and their academic performance? This study will also address the following research sub-questions:
1. What are the impacts of socioeconomic status on academic achievement?
2. What are the mechanisms of transmission involved in the link between socioeconomic status and student's school performance?
3. What impacts do the effects of socioeconomic background on school performance and their mode of transmission have on school programs and policies?
Transmission refers to the transfer of the socioeconomic challenges/advantages to the academic performance of the students. This mixed methods study will use blend of positivist and interpretivist approach, and a within-subjects research design. This design is the best fit considering that it will allow the researcher to address both the qualitative and quantitative aspect of the study. The primary population of interest is male and female college students, who are 18 years and older attending one of the schools in Escondido, California with the focus being on those learning centers that have embraced diversity and inclusivity. Simple random sampling, which provides each member of the targeted population a chance to become a member of the sample, will be used to select a sample of 100 students. Qualitative data will be collected using probing questionnaire survey. Polit and Beck (2004) claim that in-depth responses, which the questionnaire affords, are beneficial, as they allow the investigator to gain an entrance into the respondents’ world, thereby, having full access to their encounters as lived. To collect the quantitative data, the records on combined income of the parents of the students will be derived. The students will also undertake language test and their performance scored. Consequently, the income records will be correlated using Pearson r test with the test scores to determine the relationship between SES and GAP.
Literature Review
Socioeconomic Statuses, Gender, and Academic Performance
The degree of empirical evidence on the connection between a learner’s socio-economic background and their academic performance is burgeoning. One of the most relevant studies is one performed by Ghaemi and Yazdanpanah’s (2014) in which the purpose was to establish the effects of socioeconomic status on the academic performance of the EFL (English as a Foreign Language) sophomore learners. To accomplish this objective, the researchers sampled 120 students who studied English translation at the National University of Rafsanjan in Iran. The researchers used a convenient sample with participants ranging from 19 to 29 years of age. The researchers used a questionnaire to collect the data which they included questions regarding demographic issues (e.g. parents' education levels and income among others). The researchers found that socioeconomic status and academic performance among junior university students correlated negatively. However, the outcomes are different for the mid/high SES students. What the researchers meant was that the socioeconomic class from which junior university students belonged to did not have any effect on their academic performance. However, for the mid/high SES students, the socioeconomic class of the learners was a major influencing factor in their academic performance.
Tomul and Polat (2013) investigated the relationship between socioeconomic status of tertiary education learners on their educational attainment and the association of the underlying determinants. The researchers sampled 691 undergraduate senior students enrolled in the Süleyman Demirel University. Tomul and Polat (2013) used weighted quantitative composite scores
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