Often times as researchers, we will take continuous variables and break it up into categories, typically for the purpose of grouping, or to measure an outcome measure. We usually decide on a reasonable cut-point and place the data in the appropriate category. Streiner (2002) asserts that this categorization results in lost information, reduced power of statistical tests, as well as an increased probability of a Type II error. In Streiner (2002) article, written primarily for researchers, he states that the only justified reasons for dichotomizing a continuous variable is when the distribution is highly skewed or the data shows a non-linear relationship with another variable. Often in research, we will take outcome data variable from something such as an anxiety or depression inventory, which uses a continuous measure, and we dichotomize that outcome variable. Typically it is above or below a predetermined cut-point and we do this to see if there was a reduction from previous baseline scores. Other examples may be placing subjects in different groups or categories by dichotomizing or trichotomizing scores from a continuous scale. Often the reasons for dichotomizing this data is to make the data easier to understand when they are in proportion or odds ratios, and therefore making it easier to determine who to treat and who not to treat. According to Streiner (2002) this can have severe ramifications in terms of power or sample size when these continuous variables are
4. In a well-designed study, what does it mean to say there is a statistically significant difference between groups?
The independent variable is married status (single vs. divorced vs. married); the dependent variable is happiness measured on a scale from 1 to 50. This situation is inappropriate—there are more than 2 groups
A distribution table can keep all of this information (numbers, row data) handy. A person can look, and say, oh, the participants in this survey were 75% male, or 25 % female. The managers can “see” the information and not just the raw data.
It is important to use outcome measures that are evidence based because they prove that something was successful or unsuccessful. Using Outcome measures that are evidence based allow clinicians see specifically how a treatment effected the clients.
the audience, and it is hard to put it to perspective. Therefore, a statistic is appealing to the
Methodological principles and health care organizations both can reduce health disparities providing it is recognized a health disparity is a health contrast in the way things turn out across subgroups of the population, in connection to social issues, economic issues related to having a good job, or environmental circumstances such as unsafe neighborhoods. Even though health disparities are alive and known, the Unites Stated will inevitably gain when every person has a fair chance at living a long, healthy, and constructive life. Health disparities adversely affect groups of people who have systematically experienced greater obstacles to health on the basis of their racial or ethnic group, religion, socioeconomic status, gender, age, mental health, cognitive, sensory, or physical disability; sexual orientation or gender identity; geographic location; or other characteristics historically linked to discrimination or exclusion
Read the “Numbers Can be Worth a Thousand Pictures: Individual Differences in Understanding Graphical and Numerical Representations of Health-Related Information” article prior to answering. Informed decision making in the mental health and medical professions requires the ability to understand and effectively communicate statistical information. For this discussion, address the following in your post:
One unaccounted for factor that is impacting the level of support for redistribution in the United States is the inseparable intersection of race and gender, or rather the dominance that patriarchal and racialized norms have on influencing American’s perceptions of poverty. The majority of the current literature on the subject of American’s support/opposition to economic redistribution tends to focus on identities such as race, class, and gender as separate entities. Applying the theories of hostile/benevolent sexism and racism, I argue that analyzing intersections of identities such as race and gender in conjunction, and as interdependent, will provide a clearer picture as to why Americans are less favorable towards redistribution, and
The data was analysed by a computer program, which was identified but not clarified the reason used. Polit and Beck (2006) argue researchers should clarify that the chosen instrument is the most appropriate for their study with evidence of validity and reliability. Futhermore if an established computer program that has already has established validity and reliability is used this should be outlined as stated by Wood et al, (2006) and in this case were not.
Based on Paul Tough’s “Who Gets to Graduate?”, both internal and external factors influence the success one has in college. Internal factors have to do with what one thinks about themselves and self esteem. Internal factors influence the way one thinks. It influences the way they feel about their college and about their feeling of belonging. External factors can either help with those internal feelings or make them worse. With whether someone internal factors are good or bad and whether their external factors are good or bad will decide if they will graduate or not.
Fukuyama describes Factor X states, “as essential human quality that is worthy of a certain minimal level of respect, regardless of our varying skin, color, looks or social class (186)”. Factor X also includes human essence, what it is to be human. During the beginning of the film Lucy did have Factor X. She was like an average human being, she showed different emotions such as fear, pain, and even love while talking to her mother on the phone and reminiscing on the good that remembered from her childhood. As the drugs begin to affect her body and alter her intelligence and other things.
However Mendelsohn and Von Hentig’s typology can be criticized due to the fact that they are merely based on their observations, Rather than a collective study of empirical evidence; or a reliable source which would consist of, a good amount of qualitative and or quantitative data, to produce a reliable outcome and source of information.
I would like to add that, dichotomous variables are categorical variables with two levels. These levels are different groups within the same independent variable. Examples of dichotomous variables are: coin (heads or tails), political afiliation ( democrat or republican), and economic status (rich or poor).
The output shows that cut, polish and certification have t-stats and corresponding p-values that are considerably greater than 0.05 (significance level). A stepwise regression drops these variables, indicating that they are not a good fit for the model (Appendix).
External influences: When looking at the external factors that can impact my bar there are many, in the food and beverage industry it is highly competitive market. Price is a major factor you have to be able to produce high quality food and beverages to your guest at reasonable prices. The cost of food and beverages is always fluctuating you have to adjust for this, which may include searching out new vendors and may include adjusting our menu. The economy can be a factor, if our economy slows down and people start losing their jobs you as a business owner will feel the effects. If this were to be something that happens I would have to let, go of some of my staff. A factor that fits in with economy is sports, in BC. Restaurants highly depend