Data Collection Methods Essay Example
Data Collection Methodology and Research Instruments
Primary data collection was by questionnaires. There were interviews for patients within 24-48 hours after admission. RA nurses administered the interviews independently in order to get demographic information. This was by a pain intensity scale, HQLI, CAS, and dyspnea intensity scale. The RA nurse did simultaneous interviews with the caregivers. The assessments of the three symptoms revealed the problems that the patient was encountering, in order to tailor the intervention measures for group III to the situations of individual patients. The patients filled the MSAS to find out the distress, which arose because of symptoms and not pain, constipation, or dyspnea.
As a post intervention measure the patients, in the 16th day of hospice admission and in the day, 30 reported the intensity of symptoms, and then completed the HQLI and MSAS. The co- investigator performed a random survey each month to ensure data integrity. There was also a criterion of ensuring that the hospice patients aged about 70 and with a high school education.
The Authors View on the Reliability and Validity of Research Instruments
A comparison supported the predictive validity of every measure. The comparison among the visual analog scale, a box scale, a rating behavior scales, verbal rating of a five-point scale, and NRS. It produced similar results for the correct responses.
Test-retest reliability worked well for the study. Other studies, which range between 0.89 and 0.92 supports the one-item rating as a reliability measure. A concurrent validity of other measures indicates a range of 0.88 and 0.94.
Limitations, Which the Authors Faced During Data Collection and How They Minimized
Research with the hospice was difficult. The researcher encountered problems when dealing with patients who had serious ailments, yet the survey required them to fill the questionnaires, and answer all the interview questions despite their conditions.
Reported Demographic Information
The standard demographic data reported were age, gender, marital status, education level. The data mirror the national results. The average of the older patients involved in research, tally with the national average. The study patients were predominantly males, single women, and with average education.
The main research variables were dyspnea, intensity of pain, constipation, quality of life, and overall symptoms distress. To capture these variables, data collection happened on admission and later on days 16 and 30.
Inferential Tests Used in the Data Analysis
Descriptive statistics were central in data analysis. The results are in table and graphs. The researcher used both the measure of central tendency, the mean, and the measures of dispersion, standard deviation, standard error, and the tests of significance.
Use of Figures, Graphs, and Tables in an Understandable and Meaningful Way
The use of the figure to show the patients’ progress through the study provided a clear picture on the progress of research, from the start to the end. The graph on the symptom assessment scale (figure 2) reinforces well the table values. The tables present the analysis in a clear and a coherent way. It shows the mean, standard deviation, standard error and the p-value, which is important for testing the significance of the results.
Article 2 Answers
Description of Data Collection Procedure
The researcher obtained the information from analyzing the secondary phenomenologic data. The researcher used the secondary qualitative analysis to evaluate the original set of data, by comparing with the real experiences from the cancer survivors. The data assisted to answer the questions on the survivors of the breast cancer, the experiences about the distressing and unexpected symptoms, in the breast cancer treatment. Participants for data collection were volunteers whose survived breast cancers, after suffering for 1-18 years before the study.
How the Authors Addressed the Reliability and Validity of Their Methods
The authors addressed validity by comparing the real experiences of the breast cancer survivors and the secondary data from the existing medical records. Reliability was possible since the researchers chose many participants, and their responses compared. The comparisons assisted to reduce biasness and so increased validity and reliability of the study.
Limitations Faced By the Authors While Using Secondary Data for Analysis
The authors were likely to obtain inaccurate information from the records, which occurred due to biasness introduced in the records during the treatment period. In addition, there is a possibility that the recorded information changed during the treatment.
Demographic Information from the Authors
The author’s report on demographic information is that of sex (women survivors) of certain ages, the nurses, or medics, and the family member of the cancer survivors.
The main research variables were the cancer-distressing symptom encountered years after the treatment of breast cancer, unexpected experiences, and ongoing symptoms. The ongoing and unexpected symptoms included lingering symptoms and losing pre-cancer being.
Authors’ Choice to Exclude Inferential Tests in Data Analysis
This study focuses on qualitative data analysis. There was no coding of data, but the author analyzed the responses from the breast cancer survivors and pre-existing health records in a symptom distress figure.
Use of a Figure in an Understandable and Meaningful Way
The author just used a figure to represent the research findings. The figure clearly illustrates the symptom distress dimensions, which are temporal, situational, and attributive.