Sampling strategy

State your research hypothesis or guiding research question:

Hypothesis or question details

What are the variables you will be collecting data about?

Hypothesis:

A hypothesis should be in the form of a statement, not a question. A clearly stated hypothesis should make it obvious what the unit of analysis is. Your hypothesis should include two variables and the relationship between them that you expect to find when doing the research. Variables have variation; they have two or more attributes. Your hypothesis should usually make some kind of comparison between units with different attributes (for example, males versus females or older people versus younger people). Hypotheses usually include some comparative term, such as more, less, higher or lower. Social science hypotheses are probabilistic. That is they usually say something along the lines of "on average," "more likely" or "have a lower probability." This is because there are seldom any social characteristics that apply to every single person in a socially defined group.

Variable	Name	Attributes
Independent Variable (example):	Curriculum type	Common Core vs non-Common Core
Your Independent Variable:
Dependent Variable (example):	Academic performance	Number of 'A' grades received throughout high school (exact number)
Your Dependent Variable:

Research Questions

A good research question makes it clear who or what is being studied. Your unit of analysis should be reflected in the research question (and vice versa). The question should also refer to one or more variables. A good research question opens the door to understanding how these vary and what the sources of the variation might be. A good research question should not have a yes or no answer.

Copy or add rows as needed.

Unit of study	What do you want to know about these units?	How do you think the answers of different people will differ?

State the unit of analysis.

In order to answer these questions you must read the Fink chapter on sampling. (Also review the Patten section on Sampling). You must understand the sampling vocabulary.

What is the theoretical population of interest?

What will the study population be?

What would a reasonable sampling frame be for this population?

(This should be a list or other source of elements that actually exists even if you can't actually get a copy.) If no sampling frame is available, how would you create one? Or what will you do instead?

What type of sampling strategy would you use? Why?

Some possibilities are simple random sample, systematic sample, cluster sample, quota sample, chain referral (snowball sample), and judgement sample.) Justify this choice with a quote from Fink and/or Patten.

How many elements will you select for your sample?

Remember this is essentially asking you for the sample size.

Dealing with non response and other problems

What will you do if someone refuses to participate, has moved or is otherwise unavailable for the study? What about people who are not available when you plan to collect data from them (for example if they are absent from school, not home, or don't show up for an appointment)? Some issues to think about: How many times will you attempt to contact someone before giving up? How will you make sure you get a big enough sample even if some people refuse, are missing or don't participate for other reasons? (Note: In research it is essential to plan for problems before they happen.)

my proposal is the effect of internet addiction among college students vs, non college students.....fill in the table and answer questions.

Internet Addiction, Fatigue, and Sleep Problems Among Adolescent Students: a Large-Scale Study Abdulbari Bener 1:23 D . Erol Yildirim 4 . Perihan Torun. Funda Catan . Erkut Bolat] . Summani Alic . Salih Akyel' . Mark D. Griffiths Published online: 14 May 2018 C Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract The aim of the present study was to examine the association between Internet addiction (IA), fatigue, and sleep problems among university students. A total of 3000 Turkish students aged 18 to 25 years were approached and 2350 students (78.3%) participated in this cross-sectional study from April 2017 to September 2017 in public and private universities in Istanbul. Data were collected via a structured questionnaire including socio-demographic details, lifestyle and dietary habits, Internet Addiction Test (IAT), Fatigue Scale, and Epworth Sleepiness Scale (ESS). Descriptive statistics, multivariate, and factorial analyses were per- formed. The overall prevalence of IA among the studied population was 17.7%. There were significant differences between gender, family income, father's occupation, school perfor- mance, frequency and duration of watching television, physical activity, Internet use duration, and sleep duration (all p 0.6, so the sample size was deemed good enough for all the statistical tests carried out. Content validity, face validity, and reliability of the questionnaire were tested among 148 participants. A high level of validity and high degree of repeatability was found (kappa: 0.85 > 0.8). Measures The questionnaire comprised ve sections. The rst section included socio- demographic details of the students; the second section concerned lifestyle habits, extra physical activities, and several disorders; the third section comprised the Fatigue Scale; the fourth section comprised the Epworth Sleep Scale; the fmal section concerned Internet use and included Young's Internet Addiction Test (Young 2004). We used the Turkish translation of Young's Internet Addiction Test (IAT) developed by Cakir Balta and Horzum (2008). IAT comprises 20 questions to determine the level of addiction as mildly, moderately, or severely. It is evaluated on a scale up to 100'. up to 49 is categorized as normal, 5079 is categorized as problematic, and 80100 is categorized as signicantly problematic. Items were rated on a 6-point scale where 0 = does not apply, 1 = rarely, and 5 = always. The internal consistency (Cronbach's alpha) for the 20 items using the responses of all participants was 0.89. On the other hand, people were considered as Internet addicted if they use the Internet more than 35 h/week in Aslan's study (Aslan and Yazici 2016). For the purposes of this study, students were regarded as having Internet addiction if they fullled all of the following two inclusion criteria: an IAT score > 65 and Internet Viewing of :5 h/day. The Fatigue Scale comprises 14 items that determine widely seen physical and mental of 25 h/day. The Fatigue Scale comprises 14 items that determine widely seen physical and mental fatigue symptoms (Chalder et al. 1993). The 4-point Likert scale was applied where l = better than usual, 2 = no more than usual, 3 =worse than usual, and 4 =much worse than usual. Cronbach's alpha for physical fatigue items (18) was 0.85; and for mental fatigue items (9 14) was 0.82. The Epworth Sleepiness Scale (ESS) is used to assess average daytime sleepiness (Johns 2000). The validated ESS comprises 8 items scored on a 24-point scale. Scores ranging from between 1 and 10 are normal and scores between 11 and 24 are considered to be abnormal. Epworth score varies in the range of 024: 20 240(57.8) 1086(56.1) Family income $3.000 128(30.8) 318(16.4) Father education Primary 98(23.6) 438(22.6) 10.746 0.030 Intermediate 92(22.2) 363(18.8) Secondary 122(29.4) 515(26.6) University 103(24.8) 619(32.0) Father occupation Not working 40(9.6) 211(10.9) 81.898