Quantitative Research Questions and Confounding Variables

QuantitativeResearch Questions and Confounding Variables

QuantitativeResearch Questions and Confounding Variables

Thisdata set can be used for a study that analyses the factors thatexplain variation in number of patient discharges in Healthcare.There has been problem in discharges of individuals from onehealthcare to another and the explanations and reasons behind thedelays are varied from one healthcare to another (Naidu, 2009). Incarrying out such a study: two main research questions that willguide the study are:

  1. Is there significant difference in the number of discharges in the different health cares?

  2. What explains the significant differences or predicts the variations in the discharges number in the health cares?

Theindependent variables in this study entails those that can be used toexplain the factors that influence the variations in dependentvariables. One of the independent variable that is used to explainthe variation in the number of discharges is the total number ofdischarges in each facility. The other dependent variable is theaverage covered charges in each of the facility. On the other hand,the dependent variables used in this study include average totalpayments and average Medicare payments.

Althoughthis study is quantitative in nature, it is not easy to rule out somevariables, such a variable in this study is the type of hospital(Hospital referral region description). This variable becomes aconfounding variable in the study as it impacts on the outcomes. Eventhough, the study is quantitative in nature, this confoundingvariable appears cannot be overlooked as the hospital type, the stateresponsible for care, and service location of some of the facilitiesimpact on the variation of discharge in each of the facilities(Schlesselman, 1978).

References

Naidu,A. (2009). Factors affecting patient satisfaction and healthcarequality. Internationaljournal of health care quality assurance,22(4),366-381.

Schlesselman,J. J. (1978). Assessing effects of confounding variables. AmericanJournal of Epidemiology,108(1),3-8.