ProQOL.org

How The ProQOL is Used in Research

The ProQOL is the world's most commonly used measure of the positive and negative aspects of helping others who have experienced great sorrow or traumatic stress. There are hundreds of published documents about research and use with the ProQOL. You may wish to consult PubMed (free access), the U.S. National Library of Medicine of the Institutes of Health, the PILOTS database (free access) of the VA National Center for PTSD, Google Scholar (free access) and PsychInfo (access fee may apply) of the American Psychological Association. A bibliography of over 1,000 papers through 2010 can be found on this site.

If you want to use the ProQOL for research you are free to do so under the terms and conditions below. If you need a specific written permissions can be accessed at the Request Use Permission page.

The ProQOL measure may be freely copied and used as long as (a) author is credited, (b) no changes are made other than those authorized below, and (c) it is not sold. You may substitute the appropriate target group for / [helper] / if that is not the best term. For example, if you are working with teachers, replace / [helper] /with teacher. Word changes may be made to any word in italicized square brackets to make the measure read more smoothly for a particular target group.Additionally you are granted permission to convert the ProQOL into other formats such as a computerized or taped version for the visually impaired.

Should I use the raw scores or grouping scores (high, normal or low risk)?

It may be best to use the categorical scores if you are interested in how people group across the scores, for example, in ANOVA where you have categorical comparisons. You may choose to create your own groups based on the raw scores. For example, you may wish to create groups based on 10 percentile ranges rather than on the three used for the self-score method.

The most important thing to do is to understand your question of interest. This is probably not your hypothesis. The Question of Interest is

You can compare your group to itself. If you go this route you use the means and standard deviations of your own group. You may wish to compare the scores of your group to the means and standard deviations reported in the manual. If you want to do this you use your raw scores and compare them to the raw scores in the manual. This is often done using a one sample t-test comparing the means and standard deviations of your group to the norms reported in the manual. The center of the sample data is represented by the mean (X bar) and the norms are represented by

You can also do an ANOVA or a variant of that by making one group the raw scores from the manual and the second group your scores. You can decide how you wish to compare your scores. In general people use the t-test noted above. Another alternative, although less statistically satisfying and one that introduces error into the analysis, is to simply assign the mean and standard deviation of the manual score as a hypothetical group. Because there will be no variance in this "group" you make from the manual scores, you will have to introduce some variance into your hypothetical sample. This can be done in multiple ways, including setting the means and standard deviations by the SEM or using the variance. You will have to vary the scores but your final mean, standard deviation and SEM should average to the manual numbers. There are some computer programs that will do this type of calculation for you or you can do it using a program like Excel or by hand.

If you are comparing your sample's raw scores to the published raw scores and there are differences, you probably have a group that is different from the larger sample. This can be good or baffling. For example, if you have a sample of nurses and their scores differ from the general samples reported in the manual, you probably have found a true group difference.

Sometimes there are differences but they do not make theoretical sense. In those cases it is useful to check your analyses types or the computer code. It is also important to make sure that each of your data points are aligned with the right subject and so forth. It is very easy to get data misaligned. You certainly will not be the first researcher that this has happened to! In an early data collection on the ProQOL, the average number of children for the subjects was 42.3 children. A quick look at the data showed that the column for number of children was mixed up age of participant.

My alpha reliabilities are different than what is published in the manual, what does that mean?

If your alpha reliability is different than the ones reported in the manual, the ProQOL works differently in your specific sample than the aggregate sample from the manual. If your alpha reliabilities are substantially lower than what is reported in the manual the measure may be a poor one for your sample and care should be used in interpreting your results. If the alpha reliability is higher, the ProQOL is a particularly useful and reliable measure for your sample. You may find that some scales are better for your sample than others. You must decide based on your reliability scores, if you are comfortable with using a particular scale with your sample.

If your factor analytic structure is different from the manual, consider three things. The first is the simplest. You should check to see that you used the same type of factor analysis. For example, principal componants analysis will yield different structures than a common factor analysis. You can read more about this below in the factor analysis section.

Second, you may be observing the recognized overlap between the Depression and STS scales that can lead to a two factor solution or a three factor solution with fewer items. If you observe that you can measure these scales with fewer items, you are correct. The items that could be deleted are included for user friendliness and the items are statistically neutral, that is, they do not change the statistical characteristics of the scale. More information about this issue is in the next section which discusses the correlation between the Burnout and STS scales.

The third, and probably the most important thing is to examine the similarity of your sample with the data used to create the ProQOL. Are the two comparable? It may be that your factor structure is based on your sample and may or may not apply to the population used for the ProQOL development. For example, if your sample is of teachers in high-risk scools the factor structure describes your sample of teachers in high-risk schools, not the universe of people who could take the ProQOL. The data bank includes people from many fields, males and females and multiple countries.

Clinical treatment and previous research has identified a strong relationship between depression and Traumatic Stress; depression and trauma are frequent co-travelers. The correlation between the two scales acknowledges this overlap assigning as best a possible the unique variance of each. However, since theoretically items about depression and trauma can belong to both scales the two scales share variance.

Items that load highly on both scales are also retained for practical reasons. Based on the information we received from focus groups, it is easier for people to understand three subscales with 10 items each than one subscale with 10 items, one with 5 and one with 7. Another practical reason is writing computer code for statistical analysis. When you have the same number of items on each subscale you do not have to adjust the denominator across comparative analyses.

The items that are included to balance each scale to 10 items are statistically neutral to the item to scale and factor structures. That is, they make no statistical difference if they are or are not included. They are in effect place holder items.

Below is a quick summary of Factor analyses and Principal Componants Analysis. Readers may be interested in the following two papers: Principal Componants Analysis and Factor Analysis. There is no relationship between the authors of these papers and the ProQOL.

Depending on your question of interest, It may be useful to conduct a factor analysis. The data reported in the manual are analyzed using Common factor analysis (CFA) and multigroup factorial analysis (MFA), not a Principal Components Analysis (PCA). All of these analyses can be useful but they have different goals and mathematical procedures so it is important to match your analysis to your question of interest. Each are discussed below.

In PCA, the goal is to extract the

A PCA may produce 5, 10, 15 or more componants. At what point is it useful to stop selecting components? How many does it take to truly represent the data collected? How many componants (factors) account for the variance? For this test an egin value analysis or scree plot may be useful in determining when it is appropriate to stop including componants. For example, if the first componant (factor) accounts for 65% of the variance and the second 23% followed by a few componants that together account for the remaining 22%, is it better to use the first two or should you use more componants? The first two componants (factors) tell you what happens with 88% of the variance and adding other factors may not be useful and might even introduce error variance into your equations.

Because the goal of the test is to extract the first componant (factor) based on its accounting for the most variance and the remaining variance the principal component with the most variance accounted for, rotating the components (factors) is not only unhelpful it literally alters the goal of PCA.

Common Factor Analysis

In common factor analysis (CFA) the synthetic variables (factors) are extracted simultaneously, as opposed to PCA where they are extracted iteratively based on the size of the variables' contributions. In CFA the factors are computed based on similarity of variance within any one variable. That is, a single variable is parsed out to the different factors based on how it contributes to the factors. For example, the variance for gender may be split into three factors in varying proportions. The correlation of gender to Factor 1 could be .36; to Factor 2, it could be .19; and to Factor 3 it could be .07. The decision of how many factors to use for the variance in depends on your theoretical expectation. In general, most people would use the .36 but not the .07 as it is a very small, probably inconsequential contribution. You can examine this further by leaving that variable out and seeing if the structure of the factors are similar or different. In theory because the variable loaded highly on Factor 1 it should change. Factor 2 might change and Factor 3 probably would not since there was such a tiny contribution made by the variable (.07).

Because CFA extracts the factors simultaneously, there are an infinitely likely number of potentially equally likely solutions. Rotation is useful here in that it presents you with an outcome that is most likely the most commonly occurring. A scree plot may be very useful in identifying factor structures.

In this analysis I used SPSS with GLS and ML extractions, varimax rotation with three models considered with set two, three and four factor solutions. A three-factor model was selected because it fit with the theory and because there was unique contribution for all three factors. Burnout and STS might be collapsed given that they are both statistically and theoretically correlated based on depression-type symptoms, but they retain unique values that separate the two, primarily the element of fear associated with STS.

The data are also analyzed using a multigroup factorial analysis. In MFA data are compared based on their CFA structure across groups. For example, Factors 1 and 2 may be analyzed across gender and profession. If the factors are different for gender and not different for professions, it is important to treat gender as one grouping variable with two subgroups, males and females. Since there are no differences across professions, these can be conglomerate into a single group.

An example of how to use this in an analysis can be seen in a multigroup analysis of variance or a nested ANOVA. Scores on the ProQOL are the dependent variables (scale scores) and your Independent (grouping) variables would be gender and professions. You would need to have subgroups (male and female) for gender but not for professions. For example, if your professions group included nurses and teachers you might find that male helpers might be different than female helpers. In this analysis data were analyzed across groups expected to be different; e.g., gender and professions as well as year of data collection and across randomly drawn samples from the larger whole.

You can participate in the continued development of the ProQOL We invite you to consider donating a copy of your raw data to the ProQOL databank. We depend on these donations to build the psychometric properties and norm the ProQOL. Please know that your donated data will never be published so that it can be identified with your research. For example, we have collected data from multiple studies of nurses. The data are merged as nursing data, not specific project data.To find out more about contributing to the ProQOL effort, go to the Donate Data page.

The ProQol is a volunteer effort. As our time allows, we consult with student and professional colleagues in regard to their research on professional quality of life, compassion satisfaction, burnout and compassion fatigue. Many of our consultations are ones where cross-cultural issues and issues of indigenous people are paramount. While we cannot always donate our time, as time and resources permit, we do work pro bono.

At times, students ask us to serve on their theses and dissertation committees or to act as content experts for their advanced degrees. We are happy to discuss these options with you and participate as time and resources permit.

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