Basic Quantitative Data Analysis and Research If you are afraid of numeric data and feel (correctly or incorrectly) that your math skills are not up to par, you may face this phase of research with some trepidation. It may help to know that your clinical knowledge and experience are as vital to data analysis as are your mathematical skills.

On the other hand, if you enjoy working with numeric data and feel prepared to do so, this will be like any other phase of research except that this phase requires a basic knowledge of statistical analysis and some computer skills. For more complex analysis you will need more advanced skills and/or a consultant in statistics.

Basic Quantitative Data Analysis and Research

In either case, this phase is one of discovery. It is exciting to finally see the results of your study:

ï  Did the intervention work? Are participants who received it better off than they were before? How much better off? If they are not better off, why arenít they?

ï  Was your hypothesis about the relationships of interest correct? If not, are there any clues why not? How strong is the relationship?

ï  Were there any surprises? Unanticipated results that need an explanation?

ï  Do the findings support the existing theory? Do they suggest a modification of the theory?

There are a few steps to take yet to obtain answers to these questions, but by the time you complete the data analysis, you will have some answers.

Beginning researchers usually need the guidance of an expert researcher or statistician to complete this phase adequately. Remember, however, that it is your study and that you have become the expert on your study topic. It is best to stay involved in the data analysis even if you cannot do it on your own. You need to understand what has been done and what the results mean. Analysis cannot be done mechanistically: the theory and meaning that underlie each measure must always be kept in mind and are essential to the interpretation of the results. Reed (1995) reminds us that ìData alone do not yield up the theory any more than brushes will produce a paintingî (p. 95).

A deliberate, thoughtful, step-by-step process for analyzing quantitative data is described in this chapter. The description is basic and does not require a high level of statistical skill.

Basic Quantitative Data Analysis and Research Data Cleaning

Now that all of the data have been entered into the database and the database has been imported into the analysis program (if you were using an external file), it is time to review it once more for any errors that could affect the outcomes of the analysis. The following are a few things to consider:

ï  Look at the data matrix on screen or in the printout. Are any lines too short (truncated) or too long? Are there any symbols that should not be there? For example, review of a large (7500-subject) data matrix that had been imported into SAS revealed #### had been entered as a value for the length of stay variable for several people. On another variable, gender, which was coded 1 = male and 2 = female, one value entered was y. In the first case, the #### probably means that the data were missing; a check of the original data confirmed this and a period (.), which is a SAS convention for missing data, was used to replace the ####s. In the second case, they were probably a typo. This was also confirmed, and a 2 for a female was used to replace them.

ï  Recheck the scoring of tests to be sure it was done correctly. Was reverse scoring done correctly? You can do a random check to verify this. Do not just look at the first few lines of data, but in the middle and at the end of the database as well.

ï  Make sure the correct coding categories were used. For example, it is critical that each person receiving treatment 1 be coded as a member of treatment group 1. One way to double-check this is to compare a randomly selected number of participantsí treatment group membership codes with the treatment group assignment noted on the test packet and on your tracking sheet.

Another way to double-check is to compare the value on one variable with the value on another. For example, you can use the assignment of ID numbers to indicate treatment group: everyone in treatment group 1 has a 1000ñ1999 ID number; everyone in treatment group 2 has a 2000ñ2999 ID number; and so forth. Another example would be to compare the country of origin or language spoken with the indicated ethnic group membership. Each study will have different variables that can be cross-checked in this manner.

Basic Quantitative Data Analysis and Research

ï  Look for outliers, any number (value) that is outside the expected range for a variable. For example, an age of 101 is possible; an age of 201 is not. If the maximum score on a scale is 60, then any value over that indicates an error, whether in scoring or in data entry. However, not every outlier is an error; some may be legitimate. For example, an adult weight of 425 may be a data entry error or it may be correct. Entries of this type should be checked before deleting them (Saracino, Jennings, & Hasse, 2013). To find outliers, you can look at the frequencies for each variable on screen or in a printout (if your database is not too large). Every variable needs to be checked, even if you have put limits on their range within the data entry programs.

ï  While you are going through the previous steps, watch for the frequency of missing data, especially for a pattern in the missing data. Handling missing data at this phase of the study presents some challenges, which are discussed in the next section.

MISSING DATA

Sources

Data may be missing at the item level (some items are left unanswered), construct level (all questions about gastrointestinal problems are left blank), or person level (some participants were not retested) (Newman, 2014). There are a number of reasons why data may still be missing despite efforts to fill in as much as possible during data collection:

ï  The participant may have declined to answer a question or take a particular test.

ï  The participant withdrew, moved, became ill, or died.

ï  An item or test was missed inadvertently.

ï  Participant fatigue, agitation, or another negative response made it necessary to omit all or part of the data collection.

ï  A participant deliberately omitted the test or item because he or she thought it was not applicable or did not understand the question.

ï  Poor directions or poorly worded questions elicited no response or the wrong response. Here is an example:

Basic Quantitative Data Analysis and Research

We asked long-term care facility staff to rate nursing home resident rehospitalizations as avoidable or unavoidable. To our astonishment, only 3.7% were rated avoidable. Conversation with nursing staff explained this extremely low rate: the nursing staff ratings were focused on the time at which the decision to rehospitalize the resident was made. Later in the project, which was designed to reduce avoidable rehospitalization, staff began to recognize that events in the days prior to hospitalization (increasing dehydration, for example) or capability of staff (to start infusions or intervene with anxious families of dying patients, for example) should also be considered, and they began to see more rehospitalizations as avoidable.

ï  The data were collected but missed during data entry. If the missing data can be retrieved from the original files, the problem is easy to resolve.

Once all of the above are done, however, the remaining missing data need to be evaluated in order to decide how to handle them (Cook & DeMets, 2008; Little & Rubin, 2007). The missing data can be categorized as one of the following:

ï  Missing completely at random (MCAR): This means there is no discernable pattern as to where data are missing. If so, then loss of information is more of a concern than is bias in the results.

ï  Missing at random (MAR): The missingness is rarely entirely random; it is more likely to have some reason or reasons behind it (Newman, 2014).

ï  Missing not at random (MNAR): Is there a pattern to the missing data? Is it a systematic problem? If so, this may introduce a bias into the data and subsequent analysis (Hardy, Allure, & Studenski, 2009). Little and Rubin (1987) emphasize that these patterns cannot be ignored but must be evaluated. There may be valuable information in the pattern of missing data. For example, Voutilainen, Kvist, Sherwood, and Vehvil‰inen-Julkunen (2014) evaluated the 12,446 missing responses on a patient satisfaction scale (the Finnish Revised Humane Caring Scale) completed by 5283 adult patients from four Finnish hospitals. They found that item nonresponse was clearly not random. Thirty-seven percent (1959) of the responding patients were responsible for all of the missing data. On average, these patients were older than those who completed all of the items. They also found that those who had been inpatients were more likely to complete all of the items than were those who had been outpatients. The researchers note the importance of understanding how these nonresponses affect the outcomes of the analysis.

It is important to identify the reasons why data are missing. Feedback from participants and the observations of data collectors may provide valuable information regarding the reasons for missing data. Analysis of the data may also provide information about the source of bias. The following are examples of the types of bias that might be found through analysis of the data:

Basic Quantitative Data Analysis and Research

ï  Tests of function such as the Independent Activities of Daily Living (IADLs) often ask the participant if he or she can prepare a meal or balance a checkbook. Many older married men do not know how to cook and so will answer ìnoî or skip the question even though they could prepare a meal if they were taught how to cook. Likewise, many older married women have let their spouses balance the checkbook and will answer ìnoî or skip the question about balancing a checkbook, although they could do it if taught.

ï  Another IADL function item asked participants if they played games of skill. Analysis of responses found a significant difference between ethnic groups (European Americans, African Americans, Afro-Caribbeans, and Hispanic Americans) in the number of participants who left this question unanswered. Here are the differences (Tappen, Rosselli, & Engstrom, 2010):

Ethnic Group  Percentage Who Never Did This Activity

African American       18%

Afro-Caribbean           23%

European American    15%

Hispanic American      9%

Items such as these need to be worded carefully to obtain the desired information. Do you want to know whether the person actually does this activity, whether the person could do it, or both?

These examples illustrate gender and ethnic group biases in the data that cannot be ignored. The participants with missing data were not necessarily unable to perform the task, although they did not perform the task. Replacing the missing data with the code for ìnot doneî could lead to the highly erroneous conclusion that more men than women were unable to function in the kitchen, more women than men were unable to do the simple math to balance a checkbook, and more African American and Afro-Caribbean people were unable to play games of skill. Instead, a very simple replacement of the missing data with the participantsí means on the remaining activities that they did perform or a calculation of their individual means on a smaller number of items that they did perform would have been a more reasonable approach. Further analysis of the results of using the participantsí means on other items suggested that it was acceptable in this instance. However, in many cases a more complex approach is advisable.

Basic Quantitative Data Analysis and Research Replacing Missing Values

It may seem best to drop any participant with missing data from the analysis. This is called complete case analysis (Vittinghoff et al., 2012). If only a few are missing data, this may not bias the results too much; however, if it was primarily men, for example, who would be dropped from the analysis, then the results may not accurately represent this gender. Or sicker, poorer patients may not return for follow-up visits, leading to the impression of more positive outcomes than really occurred. There are several ways to address the problem of missing data after your initial evaluation has been completed. None is perfect. Replacement of missing data with a value derived from existing information is called imputation. Alternative approaches include deletion with weighting of remaining cases and direct analysis of the incomplete data (Rubin & Little, 2002). Missing data may be confined primarily to a single variable such as incomeóthis is called a univariate pattern. Or it may be found in certain cases when participants have withdrawn from the study or refused to complete certain portions of itóthis is called unit nonresponse. Missing data may be systematic in these and other ways, or they may be missing completely at random, abbreviated as MCAR (Little & Rubin, 2007).

Little and Rubin (1987, p. 44) quote an earlier comment by Dempster and Rubin that is worth repeating:

The idea of imputation is both seductive and dangerous. It is seductive because it can lull the user into Ö believing that the data are complete Ö and it is dangerous because it lumps together situations where the problem . . can be legitimately handled in this way and situations where standard estimators applied to the real and imputed data have substantial biases.

With this caution in mind, we will proceed to consider some basic principles in handling missing data:

ï  Some missing data cannot be replaced. For example, if the participantís age is missing, there is no birth date from which to calculate it, and age was not recorded at a previous data collection point, that information is permanently missing and imputation cannot be done.

ï  Imputation uses existing information to estimate the missing values. With single-item scales such as the powerful and ubiquitous self-rating of health (ìFor your age would you say, in general, your health is excellent, good, fair, poor, or bad?î; Massey & Shapiro, 1982, p. 800), it is difficult to estimate the response that would have been selected by an individual participant. Missing items within a multi-item scale are more amenable to imputation.

ï  The easiest (and most ìseductive and dangerousî) approach to replacing missing data is to use the groupís mean (average) on the item. The problem is that there is little foundation for assuming that a particular participantís rating would be average. Further, if you replace too many missing values with the average, the variability among participants may be drastically reduced, an undesirable situation that may lead to erroneous conclusions.

Basic Quantitative Data Analysis and Research

ï  A somewhat more targeted and justifiable approach is to use the average of the individual participantís scores or ratings on the remaining items of a multi-item scale. For example, if an IADL scale has 25 items and 2 are missing, the average score on the 23 items can be used to fill in the 2 missing scores. This assumes that scores on individual items are highly related, which needs to be established because it is not always the case. (In fact, very highly correlated items on a scale are considered redundant and often deleted during scale development.)

ï  Missing values may be estimated from values at previous time points. Again, this is based on the (often shaky) assumption that that particular score has not changed. In fact, one of the primary reasons to employ multiple data collection points is to detect change over time. In some clinical trials, the last observation carried forward (LOCF) approach is used (Phillips & Haudiquet, 2003; Vittinghoff et al., 2012). In this instance, the data from the previous testing time point are used to replace the missing data. There are many problems associated with this approach. It is based on the assumption that no outcomes of interest would have changed between testing times, a difficult assumption to support in many instances (Cook & DeMets, 2008). More complex approaches have been developed to avoid these problems.

ï  Incomplete cases (participants) may be deleted, and the analysis may be done on those who completed the study. The problem with this approach is that the people who drop out are likely to differ from those who remain in important ways. Those who drop out may have had a disappointing response to the treatment being tested or they may be less persistent than those who completed. If, however, people were lost due to external circumstances unrelated to the study (a new job took them to another state, for example) then their loss may not affect the outcomes of the study.

ï  An alternative is to weight comparable complete cases to compensate for those that are lost (Little & Rubin, 2007).

ï  Complete observations (those without any missing data) may be used to fill in for those with missing data. In effect, those with complete data who match those with missing data on the characteristic of interest (the oldest, most severe asthma, highest weight or drug use, and so forth) are double or triple counted. This is a form of weighting responses. The approach is called inverse probability weighting (Vittinghoff et al., 2012).

ï  There are several more complex calculations that may be made to estimate the values of the missing data. Instead of using the mean of othersí scores, a regression analysis may be used to predict the values of the missing scores. This is called regression imputation. Other formulas for estimating the values of missing data include maximum likelihood and multiple imputations (Duffy & Jacobsen, 2005; Little & Rubin, 2007).

Expectation maximization (EM) is an algorithm that uses a formula designed for random missing information (Little & Rubin, 2007, p. 114). The first step involves computing expected values. In the second step, the effect of substituting these values is evaluated using maximum likelihood, a method of estimation used in factor analysis and structural equation modeling in a series of iterations until convergence is achieved (Munro, 2005, p. 439).

Multiple imputation avoids some of the disadvantages of using single imputation. Instead of using a single set of replacement values, somewhere between two and five (even 10 in some cases) different sets of replacement values (repetitions) can be contrasted and combined to obtain the best set of estimates for the missing values (Duffy & Jacobsen, 2005; Little & Rubin, 2007).

VISUAL REPRESENTATIONS

Now that all of the data have been entered into a database, the database has been cleaned, and missing data are filled in or replaced as much as possible, you are ready to initiate the actual analysis of the data. If your work has been careful and thorough up to now, this phase should go relatively smoothly and more quickly than you might at first imagine. Informally, this first stage of analysis is called ìeyeballingî the data.

Basic Quantitative Data Analysis and Research

Many statisticians caution eager researchers not to skip this initial phase of analysis. Much of what is done here is not included in a final report or published article on your study, but it is a valuable first look at characteristics of the sample (the participants), the outcomes of the study, and the relationship between the two. This first look may suggest relationships that were not anticipated and may cause you to use some additional analyses not anticipated when the study was designed. In other words, this may be a stage of discovery as well as evaluation.

Actually, you began eyeballing the data during the previous steps directed toward finding errors and dealing with missing data. Now you will eyeball the data once more to look at the characteristics of the results on each variable and how the variables are related one to another.

Graphics capabilities differ considerably from one data analysis package to another, but most should be able to generate the simple visual representations discussed here.

There are many ways to visually represent data. We will look at a few basic ones: stem and leaf, box plots, bar and pie charts, and plots. Data from a sample of 150 students at an urban university who were majoring in nursing and psychology will be used for illustrative purposes.

Stem and Leaf

You may have to use a little imagination to actually see these representations as a stem and leaf, but this is often the first graphic studied. An example can be seen in Figure 19-1. This is a stem-and-leaf representation of a sample of college students whose average age was 27 years with a wide range of 18 to 61. The ìstemî represents the number of years the students have lived in the United States, which ranged from 4 to 56 years. The ìleafî represents the number of students (frequency) in each category, from 4 to 56. You can see that the largest number had been in the United States for 22 years. (This is the mode or most frequent value, by the way.)

Basic Quantitative Data Analysis and Research

Figure 19-1 Stem and leaf and box plot for years in the United States in a sample of college students.

Source: The output for this figure was generated using SAS/STAT software, Version 9.2 of the SAS System for Windows. Copyright © 2007 SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA.

You can also see that the numbers seem to cluster in the 14- to 28-year range and that there are more at the lower end of the stem (fewer years in the United States) than at the higher end. Why this imbalance? Knowledge of the sample explains it. This is a diverse group of college students including European American, African American, Hispanic American, and Afro-Caribbean students. A portion (not yet determined in this analysis) of the latter two groups was not born in the United States but came to the United States at various ages. This explains the imbalance to the lower end of the range of years in the United States. This is an important characteristic of this sample of college students.

Box Plots

A box plot is illustrated in Figure 19-1 to the right of the stem and leaf. The box plot also suggests an imbalance toward the lower end of the range. The horizontal line across the box indicates the median (middle value) or 50th percentile of this distribution. The top of the box is the 75th percentile (75% of the cases fall at or below this line), and the bottom is the 25th percentile (25% of the cases fall at or below this line). The dashed vertical line above and below indicate values that are not outliers. The zeros above this line from 44 to 56 years in the United States indicate outliers that lie outside (above or below) most of the distribution. The full length of the box represents the interquartile range (IQR), from the 25th to the 75th percentile, which constitutes the middle half of the distribution (Duffy & Jacobsen, 2005). A series of box plots (not shown) may be done to show differences in distribution among several groups; for example, they could be used to show the differences in years in the United States by ethnic group membership.

Bar Charts and Pie Charts

These charts are especially helpful in visualizing differences between several groups within a sample. The simplest bar and pie charts show the frequency (total number) within each group. For example, Figure 19-2 is a very simple pie chart that illustrates the proportion of the college student sample that is European American (26.75%), African American (17.20%), Hispanic American (28.03%), and Afro-Caribbean (28.03%). It is quickly apparent that the African American group is a little smaller than the other three groups, and there are a few less European Americans than there are Hispanic American and Afro-Caribbean students.

Basic Quantitative Data Analysis and Research

Figure 19-2 Pie chart showing proportion of each ethnic group within the sample.

Source: The output for this figure was generated using SAS/STAT software, Version 9.2 of the SAS System for Windows. Copyright © 2007 SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA.

The bar chart in Figure 19-3 illustrates a slightly more complex relationship between nominal data (ethnic group) and interval data (depression scores): the mean scores on the CES-D depression scale (Radloff, 1977). In this instance, you can see that the Hispanic American students have higher CES-D scores than the others…

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