Consider to communities: Alpha and Omega. Alpha has 10 residents, 5 who earn $60,000 and 5 who earn $20,000. Omega also has 10 residents, 5 who earn $300,000 and 5 who earn $50,000 Part 1. The income inequality ratio in Alpha is and the income inequality ratio in Omega is
Part 2. Alpha and Omega each have 10 residents. The top two residents in Alpha have an average income of $90,000, and the bottom two residents in Alpha have an average income of $30,000. The top two residents in Omega have an average income of $250,000, and the bottom two residents in Omega have an average income of $50,000. How much would the top two residents in Alpha have to earn in order for Alpha to have the same income inequality ratio as Omega? The top two Alpha residents would need to earn $ each Part 3 (1 point) Suppose Alpha's community grew to 15 residents with the following incomes: 2 residents' income $175.000 300,000 260,000 65,000 1 resident's income 1 resident's income 3 residents' income 3 residents' income 2 residents' income 1 resident's income 1 resident's income 1 resident's income 26,000 105,000 650,000 18,000 140,000 The new income inequality ratio in Alpha is:. (Round to two decimal places.)

Reliability and validity reliability and validity are two important considerations that must be made with any type of data collection. reliability refers to the ability to consistently produce a given result. in the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. to illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. if the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. while the scale is highly reliable in producing consistent results (e.g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. this is where validity comes into play. validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure. while any valid measure is by necessity reliable, the reverse is not necessarily true. researchers strive to use instruments that are both highly reliable and valid.