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By Michael Harvey
In the September-October 1994 issue of the Communique, I posted a technical writing employment survey. Of the 310 Communique recipients, 67 responded by the November deadline. I continued to get responses as late as March, but didn’t include their data.
The survey’s purpose was to discover:
The Durham Tech program produces around ten graduates a year. When they get jobs, are they paid less than other tech writers? For that matter, how many tech writers studied tech writing in college or graduate school?
I used the SAS system to statistically test each of the questions posed in item 4. I performed a series of Chi-Square tests, which measure how well hypotheses fit observations and which apply to data where observations fall into discrete categories. I also ran analyses of variance, which test the effect of independent variables on dependent variables. Chi-Square tests showed that of all the variables tested, only experience and education significantly affected salary. An analysis of variance testing a model including years of education and experience, whether an employee was permanent or contractor, gender, and age showed a significant effect on salary.
Closer examination of the model revealed that only experience and salary contributed to the effect, experience more strongly than education. Put simply, the longer you’re a technical writer, the more money you make.
You’re likely to earn more the more educated you are, but only if experienced. Salary differences between males and females, permanent employees and contractors, and older and younger writers are not significant.
Not surprisingly, an experienced technical writer is more likely to have a title such as “Senior Technical Writer” or “Technical Documentation Specialist” than someone less experienced. But job title by itself doesn’t predict salary. Here’s one of the most interesting facts to emerge from the data; it’s a graphical representation of the distribution of salary across survey respondents:
Not responding = 3
As you can see, this graph peaks at $35-40K, dips, and peaks again at salaries over $50K. The distribution strongly suggests distinct groups within the pool of respondents. Considering the results of my statistical tests, I conclude that there’s one less experienced group making between $25K and $45K and another more experienced group making $40K and up. For a future employment survey, we will need to extend the upper salary bound. With the currently available data, there’s no telling where the salary curve peaks above $50K.
The descriptive statistics that emerged from examining job satisfaction, professional growth, tools used, and so on, proved fascinating. I discovered, consistent with common sense, that someone who reported a high level of professional growth also reported high job satisfaction. The correlation between the two was significant. The correlation between working overtime and reported job satisfaction, which I thought would be significantly negative, was insignificant. Someone making a high salary reported that technical writing met their salary expectations and reported higher job satisfaction, but the correlation between the variables was insignificant. All other correlations were insignificant. That is, reported opportunities for training, availability of resources, how often someone had to work overtime, and how much new writing a writer got to do, all had no effect on salary or job satisfaction. This was touted as an employment survey, but it only shows what employees think about employment opportunities.
It’s important to understand how workers perceive their market, but in the future, we should survey employers as well as employees to truly identify what kinds of jobs will be offered.
You can read the complete results of Michael’s employment survey analysis at http://stc.org/region2/ncc/www/Salary_Survey.html.
In the September-October 1994 issue of the Communique, I posted a technical writing employment survey. Of the 310 Communique recipients, 67 responded by the November deadline. I continued to get responses as late as March, but didn’t include their data.
The survey’s purpose was to discover:
- Who are the members of the Carolina STC? What is the age range of our membership? The gender breakdown? Highest educational degree earned? How much experience? Salary distribution? Job satisfaction?
- What do we do? What is our breakdown of “permanent” versus contractor? Do we write for end-users, programmers, or system administrators? What type of work environment do we have? Do we work on new or revised material?
- Where do we work? For how long? Where did we work last, and for how long? How many writers in our organization? Why did we leave our last job? Do we think we get adequate training?
- Does a writer’s education predict salary? Does experience predict salary? Do contractors make more than permanent employees? Do males make more than females? Do older writers make more than younger writers? Are there too many technical writers in the RTP market? Are we overworked? Are there fewer permanent positions opening up than before?
The Durham Tech program produces around ten graduates a year. When they get jobs, are they paid less than other tech writers? For that matter, how many tech writers studied tech writing in college or graduate school?
I used the SAS system to statistically test each of the questions posed in item 4. I performed a series of Chi-Square tests, which measure how well hypotheses fit observations and which apply to data where observations fall into discrete categories. I also ran analyses of variance, which test the effect of independent variables on dependent variables. Chi-Square tests showed that of all the variables tested, only experience and education significantly affected salary. An analysis of variance testing a model including years of education and experience, whether an employee was permanent or contractor, gender, and age showed a significant effect on salary.
Closer examination of the model revealed that only experience and salary contributed to the effect, experience more strongly than education. Put simply, the longer you’re a technical writer, the more money you make.
You’re likely to earn more the more educated you are, but only if experienced. Salary differences between males and females, permanent employees and contractors, and older and younger writers are not significant.
Not surprisingly, an experienced technical writer is more likely to have a title such as “Senior Technical Writer” or “Technical Documentation Specialist” than someone less experienced. But job title by itself doesn’t predict salary. Here’s one of the most interesting facts to emerge from the data; it’s a graphical representation of the distribution of salary across survey respondents:
| Range ($K) | Freq | Cum. Freq | % | Cum. % | |
| 25 - 30 | -+ ********* +- | 9 | 9 1 | 4.06 | 14.06 |
| 30 - 35 | -+*********** +- | 11 | 20 | 17.19 | 31.25 |
| 35 - 40 | -+*************** +- | 15 | 35 | 23.44 | 54.69 |
| 40 -45 | -+********* +- | 9 | 44 | 14.06 | 68.75 |
| 45 - 50 | -+** +- | 2 | 46 | 3.13 | 71.88 |
| Over 50 | -+****************** +- | 18 | 64 | 28.13 | 100.00 |
| -----+----+----+--- 5 10 15 Frequency | |||||
Not responding = 3
As you can see, this graph peaks at $35-40K, dips, and peaks again at salaries over $50K. The distribution strongly suggests distinct groups within the pool of respondents. Considering the results of my statistical tests, I conclude that there’s one less experienced group making between $25K and $45K and another more experienced group making $40K and up. For a future employment survey, we will need to extend the upper salary bound. With the currently available data, there’s no telling where the salary curve peaks above $50K.
The descriptive statistics that emerged from examining job satisfaction, professional growth, tools used, and so on, proved fascinating. I discovered, consistent with common sense, that someone who reported a high level of professional growth also reported high job satisfaction. The correlation between the two was significant. The correlation between working overtime and reported job satisfaction, which I thought would be significantly negative, was insignificant. Someone making a high salary reported that technical writing met their salary expectations and reported higher job satisfaction, but the correlation between the variables was insignificant. All other correlations were insignificant. That is, reported opportunities for training, availability of resources, how often someone had to work overtime, and how much new writing a writer got to do, all had no effect on salary or job satisfaction. This was touted as an employment survey, but it only shows what employees think about employment opportunities.
It’s important to understand how workers perceive their market, but in the future, we should survey employers as well as employees to truly identify what kinds of jobs will be offered.
You can read the complete results of Michael’s employment survey analysis at http://stc.org/region2/ncc/www/Salary_Survey.html.
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