Question

10. What is the main difference in appearance between a bar graph and a histogram? 11. Name the type of observational study (cohort, retrospective, prospective, cross-sectional) AND why you selected this type. A hospital administrator is trying to establish a correlation between ages of patients and days in the hospital. He/she decides to review patient charts from the last two years a. b. A hospital administrator is trying to establish a correlation between ages of patients and days in the hospital. He/she randomly selects 100 charts from all the charts available within the hospital. 12. What exactly, in your own words, is a lurking variable? Give an example. 13. Name one way (per class notes) in which you can eliminate bias within a study. How does it do so? 14. Ratio, rate or proportion? AND why? Number of slip-and-fall accidents in 2018/Number of vehicular accidents in 2018 a. b. Number of patients with appendectomies in 2018/Number of patients having surgical procedures in 2018

Answer 1

11.a) A prospective study watches for outcomes, such as the development of a disease, during the study period and relates this to other factors such as suspected risk or protection factor(s). The study usually involves taking a cohort of subjects and watching them over a long period.  

12). A lurking variable is a variable that is not included as an explanatory or response variable in the analysis but can affect the interpretation of relationships between variables. A lurking variable can falsely identify a strong relationship between variables or it can hide the true relationship. For example, a research scientist studies the effect of diet and exercise on a person's blood pressure. Lurking variables that also affect blood pressure are whether a person smokes and stress levels.

In statistical models, the error term explains lurking variables that affect the process. To discover lurking variables, you must take the time to understand your data and the important variables that can affect a process. You can also create a plot of the data to look for non-linear trends that can identify the presence of lurking variables.

ex.Have you ever heard the statistic about ice cream sales and murder? It seems that as ice cream sales increase murder rates increase as well. One logical conclusion is that these variables may have a causal relationship. That hypothesis however seems rather odd.

This is an example where correlation and causation become confused. It's unlikely that murders cause people to buy ice cream and equally unlikely that mint chocolate chip leads to homicide.

This is a great example of a lurking variable. There seems to be one or more additional variables which may explain why we see the increases in ice cream sales and murder. A common explanation is seasonality, as temperatures rise people tend to be outside more which makes it more likely they'll buy ice cream and get violent.

Correlation is not causation. Correlation is a great indicator for the need to consider lurking variables.

13). The nature of qualitative data makes it difficult, if not impossible, for the person doing the analysis to separate himself or herself from the data. There are ways, however, to try to maintain objectivity and avoid bias with qualitative data analysis:

1. Use multiple people to code the data.

If there is some consistency between your interpretation and that of others, then it is more likely that there is some truth by agreement in your interpretations.

2. Have participants review your results.

Ask the people who provided the data whether your interpretations seem to be representative of their beliefs.

3. Verify with more data sources.

This is sometimes called triangulation. If you can find other sources of data that support your interpretations, then you can have more confidence that what you've found is legitimate.

4. Check for alternative explanations.

Consider whether there are other reasons why you obtained your data. If you can rule out or account for alternative explanations, your interpretations will be stronger.

5. Review findings with peers.

Ask others to review your conclusions. Sometimes others will see things that you missed or can identify gaps in your argument that need to be addressed. They also can provide affirmation that your conclusions are sound and reasonable given your data.

14). they both are ratios because it is  The value obtained by dividing one quantity by another

BASIS FOR HISTOGRAM BAR GRAPH COMPARISON Meaning Histogram refers to a graphical Bar graph is a pictorial representation, that displays data by way of bars to show the uses bars to compare frequency of numerical data.different categories of representation of data that data. Indicates Distribution of non-discrete Comparison of discrete ariables variables Presents Quantitative data Categorical data Bars touch each other, hence Bars do not touch each Spaces between bars other, hence there are there are no spaces spaces between bars. Elements Elements are grouped together, so that they are considered as Elements are taken as individual entities. ranges. Can bars be 0 es reordered? Width of barsNeed not to be same Same