The most important part of any research project is the planning process. This statement is as true for data analysis
as for any of the other steps in the research process. The development of your statistical analysis plan should not be
delayed until after you have your data in hand. Rather, the investigator should select the statistics to describe the sample
and to analyze the data for each research question or hypothesis before initiating the study. Most grant applications
will require this information, but these decisions should be made regardless of application for funding. Investigators should plan to first describe their sample. They should identify the important demographic characteristics of the sample, such as sex, age, and race. These variables will be the same for most studies. Other sample characteristics, such as diagnosis, weight, height, Glasgow Coma Scale, and so forth, also may be important to provide. Descriptive statistics will describe these variables. Next, investigators should plan the analyses for each research question/hypothesis. A table may be useful for this activity. In the first column should be the research question/hypothesis;
in the second, all relevant variables (and timing information if needed); and in the third, the statistical
test to be used. This process helps investigators ensure that they are collecting all needed data, at the right time, to
answer their question. After all study data have been collected is not the time to discover that an important piece of
data has been missed. Investigators uncomfortable with statistical analysis should
consult a statistician early in the planning phase. A statistician will help them determine what statistical analyses are most
appropriate for answering the research questions/hypotheses, taking into consideration the types of data to be collected.
Although statisticians may seem intimidating, investigators should consider them an important member of the research
team and avail themselves of their expertise. To help diminish the stress of a statistical consultation,
investigators should prepare a list of questions before the meeting. In creating the list of questions, they need to start
with the research question.1 If the investigators have an idea of what statistics to use, then the questions for the statistician are related to whether the proposed analyses are appropriate and what other statistics should be considered.
If the investigators have no idea of what statistics to use, the first question should be what statistics are appropriate
for the research questions being addressed. Investigators should take advantage of the meeting with
the statistician to find out why the analysis is appropriate and to increase their knowledge of statistics. They need to
be able to defend their choice of statistics at presentations and within publications. Several advantages result from having a plan for data analysis before starting the study. The most obvious is that the investigators are not left wondering what to do with all of the data they now have in their computer. A plan speeds the process of data analysis. If a computer program will be used, the commands for the analysis can even be written before data collection is complete. In this case, as soon as all of the data are entered, the investigators run the predetermined programs, and the analysis is ready for interpretation. The second advantage of planning the statistical analysis before the study is an increase in scientific integrity. The investigators who have a plan ahead of time are less likely to bend the analysis to suit their purpose. A plan also prevents the process of repeating analyses until something is found that is statistically significant. A post hoc (after the fact) approach to statistical analysis is inappropriate and increases the chance of making a type I statistical error (see
a future issue of this series on hypothesis testing for a discussion of type I errors).2 If post hoc analyses are used, a technique such as Bonferroni adjustment is needed to decrease the chance of a type 1 error.2 Before beginning a study, the investigators should identify the computer, the data entry method,3 and the data analysis software they will use for the study. They also should spend time during the early phases of the project becoming familiar with the software to be used. Data analysis will proceed more smoothly if the investigators do not need to stop and ask for technical assistance.
The most important part of any research project is the planning process. This statement is as true for data analysisas for any of the other steps in the research process. The development of your statistical analysis plan should not bedelayed until after you have your data in hand. Rather, the investigator should select the statistics to describe the sampleand to analyze the data for each research question or hypothesis before initiating the study. Most grant applicationswill require this information, but these decisions should be made regardless of application for funding. Investigators should plan to first describe their sample. They should identify the important demographic characteristics of the sample, such as sex, age, and race. These variables will be the same for most studies. Other sample characteristics, such as diagnosis, weight, height, Glasgow Coma Scale, and so forth, also may be important to provide. Descriptive statistics will describe these variables. Next, investigators should plan the analyses for each research question/hypothesis. A table may be useful for this activity. In the first column should be the research question/hypothesis;in the second, all relevant variables (and timing information if needed); and in the third, the statisticaltest to be used. This process helps investigators ensure that they are collecting all needed data, at the right time, toanswer their question. After all study data have been collected is not the time to discover that an important piece ofdata has been missed. Investigators uncomfortable with statistical analysis shouldconsult a statistician early in the planning phase. A statistician will help them determine what statistical analyses are mostappropriate for answering the research questions/hypotheses, taking into consideration the types of data to be collected.Although statisticians may seem intimidating, investigators should consider them an important member of the researchteam and avail themselves of their expertise. To help diminish the stress of a statistical consultation,investigators should prepare a list of questions before the meeting. In creating the list of questions, they need to startwith the research question.1 If the investigators have an idea of what statistics to use, then the questions for the statistician are related to whether the proposed analyses are appropriate and what other statistics should be considered.If the investigators have no idea of what statistics to use, the first question should be what statistics are appropriatefor the research questions being addressed. Investigators should take advantage of the meeting withthe statistician to find out why the analysis is appropriate and to increase their knowledge of statistics. They need tobe able to defend their choice of statistics at presentations and within publications. Several advantages result from having a plan for data analysis before starting the study. The most obvious is that the investigators are not left wondering what to do with all of the data they now have in their computer. A plan speeds the process of data analysis. If a computer program will be used, the commands for the analysis can even be written before data collection is complete. In this case, as soon as all of the data are entered, the investigators run the predetermined programs, and the analysis is ready for interpretation. The second advantage of planning the statistical analysis before the study is an increase in scientific integrity. The investigators who have a plan ahead of time are less likely to bend the analysis to suit their purpose. A plan also prevents the process of repeating analyses until something is found that is statistically significant. A post hoc (after the fact) approach to statistical analysis is inappropriate and increases the chance of making a type I statistical error (seea future issue of this series on hypothesis testing for a discussion of type I errors).2 If post hoc analyses are used, a technique such as Bonferroni adjustment is needed to decrease the chance of a type 1 error.2 Before beginning a study, the investigators should identify the computer, the data entry method,3 and the data analysis software they will use for the study. They also should spend time during the early phases of the project becoming familiar with the software to be used. Data analysis will proceed more smoothly if the investigators do not need to stop and ask for technical assistance.
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