Two Ways HR Can Rise Up

I spend a fair amount of time around the human resources (HR) profession. Sometimes, that’s in the form of consulting and problem-solving alongside HR leaders; other times it’s through research or teaching graduate students. I’m also an HR officer in the U.S. Navy Reserve, and I maintain my HR certifications like thousands of other HR people. 

But I’m often critical of HR. That’s not at all because I think HR people aren’t doing good work; rather, I think every profession should be critical of itself in an attempt to improve continually and stay relevant. We should all be on the lookout for how we can get better at what we do and how we do it. 

In particular for HR, I see two areas in which HR could improve substantially. This isn’t to say that HR people aren’t thinking about these things or that many HR departments are already well on their way. But it is to say that considerable room for improvement exists profession-wide. 

These two areas—or two ways in which HR can rise up—are:

• Its connection to business strategy and
• Its use of analytics.

First, HR must improve in terms of its connection to business strategy.  I hear about employee engagement committees focused primarily on planning company picnics. I hear about HR departments being the last to know about major organizational changes. I hear executives complain that HR in their own company is a gate-keeper, not a service provider. 

Being more strategic as an HR person or as an HR function implies special mindset. As Dave Ulrich writes, HR needs to understand the context of their business. They need to understand how their organization competes and wins in the marketplace. Such an understanding is critical because it informs, as Ulrich discusses, a stakeholder view of HR in which the HR function has five main groups that it serves:

1. Employees,
2. Line managers,
3. Customers,
4. Investors and the
5. Community at large. 

With such a mindset, HR people can transform their conversation from one of the tactical and administrative to one of strategy and business results. If you feel that your HR function doesn’t get the respect it deserves in your organization, having a strategic mindset and aligning what you do as a function with the business strategy is a great place to start. 

HR professionals, in particular those who lead any HR function, should know and be able to articulate how their priorities and activities align with helping the organization win. 

Second, HR must embrace analytics. The world is awash with data, and it’s increasingly becoming apparent that HR must focus on analyzing the right data in the right way to inform decision making. It’s no surprise that the Society for Industrial and Organizational Psychology named “Leveraging and Maximizing Big Data and Applying the Correct Analytics to Make Better Business Decisions” as its top workplace trend for 2016. 

From my interactions, HR can improve greatly in this area, but it’s going to require many people in HR to become comfortable with data. It’s going to require them to, in many cases, obtain new skills related to the use of data and analytics. 

If you’re wondering what on earth HR analytics is all about, here are a few references that might be helpful in getting you started:

• Josh Bersin’s article in Forbes
• “Change your company with better HR analytics,” an article on the Harvard Business Review blog 
• An overview from the Association for Talent Development 

HR people and the HR function have, in my opinion, so much to offer their organizations. They’re becoming increasingly essential for organizations as they navigate the modern world of work, but to realize that potential, they’ve got to be strategic in focus and evidence-based in their decision-making. 

And so, I encourage everyone in the world of HR to adopt a strategic posture and embrace the world of talent or HR analytics. There are many ways, of course, that HR can add value in organizations, but these are two areas in which it seems that HR can, in particular, rise up. 

Love it? Hate it? Find it thought provoking? Leave a comment, like and share!

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About Ben Baran
Ben Baran, Ph.D., is probably one of the few people in the world who is equally comfortable in a university classroom, a corporate boardroom and in full body armor carrying a U.S. government-issued M4 assault rifle. Visit: www.benbaran.com.

SPSS Syntax 102: Recoding and Computing Variables, Calculating Descriptive Statistics

Once you’ve set up your SPSS data file, created variable and value labels to aid in interpretation of future analyses, and cleaned the data as necessary, it might be time to recode and compute new variables. You may also want to calculate some basic descriptive statistics regarding key variables in your data set. All of these tasks are remarkably simple using SPSS syntax. As a reminder, this forum focuses on common analyses performed by researchers and practitioners within organizational behavior, industrial/organizational psychology, and human resource management. An in-depth explanation of everything SPSS syntax can possibly do is far beyond this forum’s scope. The focus here is on practical issues and no-nonsense knowhow to bolster your productivity.

Recoding Variables

Although you may need to recode variables for a variety of reasons, the most common reason by far that I have to conduct this procedure is to deal with “reversed” items. That’s because I deal primarily with survey data about employee’s attitudes, such as job satisfaction, supervisory support, organizational commitment, and other related variables. Reversed items are those items for which the response options mean the exact opposite of the other items in a measure.

Here’s an example. The Survey of Perceived Organizational Support (Eisenberger, Huntington, Hutchison, & Sowa, 1986) originally had 36 items designed to assess employees’ global perceptions about how the degree to which the organization values their contributions and cares about their well-being. More recently, researchers (e.g., Roch & Shanock, 2006) used the six-item version of the original measure listed below with satisfactory results. Responses range from 1 (strongly disagree) to 5 (strongly agree).

1. My work organization is willing to help me when I need a special favor. 2. My work organization shows little concern for me. 3. My work organization takes pride in my accomplishments at work. 4. My work organization values my contributions to its well-being. 5. My work organization strongly considers my goals and values. 6. My work organization really cares about my well-being.

Notice how item number 2 differs from the other items. In responding to this item, an employee who perceived a very high level of organizational support would likely respond “disagree” to item number 2 but “agree” to the other five items. That’s because item number 2 is a reversed item. To properly analyze any measure that uses a reversed item, the analyst must recode that variable. Here’s the syntax to do just that (replacing “VARNAME” with the actual variable name):

recode VARNAME (1=5) (2=4) (3=3) (4=2) (5=1) INTO VARNAME_R. exe.

Running that syntax will create a new variable named “VARNAME_R,” which has been properly recoded. Now, we need to compute a new variable that takes the mean of each respondent’s responses to the items in each measure.

Computing New Variables

To continue the example above, we need to create one variable that represents respondents’ overall perceptions of organizational support, not just their responses to individual items. The process for doing this correctly may vary depending on what you’re measuring; for example, some psychometric scales require summing the responses while others take the items’ mean.

To calculate a composite variable for the measure described above, we take the mean. The syntax to do this is as follows (replacing “VARNAME” with the actual variable name):

compute VARNAMEOVERALL = mean (VARNAME1, VARNAME_R, VARNAME3, VARNAME4, VARNAME5, VARNAME6). exe.

Note that I used VARNAME_R instead of VARNAME2, because that’s the variable created during the recoding process. Running the compute command above will result in a new variable titled “VARNAMEOVERALL,” which is the mean average of the responses given to each of the measure’s six items. Also, the compute command works for all sorts of other transformations (not just taking the mean of other variables). So you can use it in many other instances as well.

Calculating Descriptive Statistics

Finally, you may want to know some basic information about your variables such as the frequency of responses to each of the response options and the items’ maximums, minimums, means, and standard deviations.

To do this, simply run the following syntax (replacing “VARNAME” with the actual variable name):

desc VARNAME1 VARNAME2 VARNAME3. freq VARNAME1 VARNAME2 VARNAME3.

Summary: The Running List of Syntax Discussed Thus Far

So here’s a running list of basic SPSS syntax that I’ve discussed on this forum thus far. Simply replace “VARNAME” with the variable name and make other adjustments as necessary for your specific situation.

Variable and Value Labels

variable labels VARNAME 'description of item or variable' . value labels VARNAME 1 'strongly disagree' 2 'disagree' 3 'neither agree nor disagree' 4 'agree' 5 'strongly agree'.

Compute New Variable

compute VARNAMEOVERALL = mean (VARNAME1, VARNAME_2, VARNAME3, VARNAME, VARNAME5, VARNAME6). exe.

Recode New Variable

recode VARNAME (1=5) (2=4) (3=3) (4=2) (5=1) INTO VARNAME_R. exe.

Descriptive Statistics and Frequencies

desc VARNAME1 VARNAME2 VARNAME3. freq VARNAME1 VARNAME2 VARNAME3.

To tell SPSS to run syntax commands, first copy and paste it into your syntax file. Then, simply highlight it and type CTRL+R on your keyboard. You can also select “run” from the menu at the top of the syntax file or click on the right-facing triangle in the menu bar.

References

Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71, 500-507.

Roch, S., & Shanock, L. (2006). Organizational justice in an exchange framework: Clarifying organizational justice distinctions. Journal of Management, 32, 299-322.

SPSS Syntax 101: Basic Guidelines, Variable and Value Labels

Perhaps you’re now convinced that using SPSS syntax might save you some time in the long run. Maybe you even now know how to create a new syntax file. So what do you do with that file and how do you make it manage or analyze your data?

As a reminder, this forum focuses on common analyses performed by researchers and practitioners within organizational behavior, industrial/organizational psychology, and human resource management. An in-depth explanation of everything SPSS syntax can possibly do is far beyond this forum’s scope. The focus here is on practical issues and no-nonsense knowhow to bolster your productivity.

To begin, let’s cover a few guidelines regarding the use of SPSS syntax.

• Notes: You can make notes to yourself within the syntax file about what you are analyzing, why you are analyzing it, what results you found, and anything else that may help put your analysis in context for future reference. To make a note to yourself, simply precede your note with an asterisk (*) and end your sentence with a period. That will ensure that SPSS will skip over that sentence when running the analysis you specify. It is sometimes useful to use many asterisks in a row to demarcate different sections of your syntax.
• Sensitivity: Although syntax is not case-sensitive (to the best of my knowledge), syntax code is very sensitive to every other character you type (including periods and other punctuation).

Now, let’s practice. Open a data set and create a new syntax file. Click anywhere in the syntax file’s blank box. Insert a row of asterisks, a description of the file, another row of asterisks, and a period. Save your file somewhere appropriate with an appropriately distinctive file name. Your syntax should now look something like this (click on the image for a larger view).

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The next step is to insert code into your syntax file that will specify the types of analyses that the program will conduct on your data. Three general ways that I typically generate code are through (a) directly typing it in from memory, (b) copying, pasting, and editing syntax from another source, and (c) using the program’s “paste” function. I recommend starting your foray into SPSS syntax using methods (b) and (c), which may or may not naturally progress into method (a).

One common task when setting up your data for analysis is properly inputting your variable and value labels. This is helpful because when you run analyses, the SPSS output will include the labels assigned to the variables that were analyzed, assisting in your interpretation of the results. Let’s imagine that one of your variables is “overall satisfaction with life,” being measured by one item on a five-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). Forget, of course, that using a single item to measure a construct is a bad idea from a measurement perspective.

Let’s go ahead and designate a variable in your data set as having the name “lifesat.” Select the “variable view” on the bottom left corner of your data file and type “lifesat” into the upper left cell under “Name.” You can quickly assign labels to both the variable itself and to its values using the syntax below:

variable labels lifesat 'overall satisfaction with life' .

value labels lifesat 1 'strongly disagree' 2 'disagree' 3 'neither agree nor disagree' 4 'agree' 5 'strongly agree'.

To run this syntax, first copy and paste it into your syntax file. Then, simply highlight it and type CTRL+R on your keyboard. You can also select “run” from the menu at the top of the syntax file or click on the right-facing triangle in the menu bar.

An output file should appear which will simply repeat the syntax listed above. Note that you will receive an error message if there is no actual variable named “lifesat.” If you look at the “label” and “values” columns in the data set, the labels and values specified above should now appear.

This procedure is especially useful when setting up an SPSS data file from raw data, which often is in the form of an Excel spreadsheet or comma-separated values (.csv) file. For example, this is specifically how the popular online survey tool SurveyMonkey compiles data for users to download. It is especially helpful when conducting initial SPSS data file setup to list all of the variable names in one column, followed by all of the corresponding variable labels in the adjacent column, and then use the concatenate function in Excel to help create all of the syntax for you. Download this Excel 2007 spreadsheet for a quick template; pay attention to the comments in each cell regarding instructions. Note that the workbook has two spreadsheets, one for variable labels and one for value labels.

I have frequently found these basic SPSS syntax procedures to be useful in the initial-setup phase of data analysis. Future posts will discuss and explore using syntax to actually analyze your data, but these initial procedures are useful because they will aid in the interpretation of those analyses.

SPSS Basics: Getting Started with Syntax

Getting started is half the battle, especially when trying to learn a new software program or trying to accomplish a new task in a familiar one. In a previous post, I discussed some of the advantages of using syntax in SPSS instead of the software program's drop-down menus. As an astute reader commented, syntax can be very frustrating because it requires the user to follow its specifications very closely. That means that any misplaced characters or punctuation can prevent your program from running correctly, or from running at all. So is SPSS syntax worth learning for garden-variety social scientists studying organizational behavior, industrial and organizational psychology, and other related fields? Absolutely. But that's because I'm going to try to demonstrate how you can use SPSS syntax in a smart, efficient way to do the basic tasks that you may encounter on a relatively frequent basis in graduate school classes, research projects, and in providing consulting recommendations.

I'll do that through posting actual syntax code that you can copy, paste, edit, and run on your own data. That should help with tasks including computing and recoding variables, calculating descriptive statistics, and conducting analyses such as reliability analysis, factor analysis, correlations, and hierarchical regression--including mediation and moderation analyses. I'll also post some of the tips that I've learned the hard way regarding keeping your syntax and data files user-friendly.

To get started, however, I'm going to assume that you've never used syntax before. You may even be a little scared of it. You're studying or previously studied psychology, organizational behavior, or human resource management because you want to help people and organizations work and play well with one another.

No one told you'd have to be a statistician or computer scientist. But neither am I. All I'm saying is that good consulting and good science builds from an understanding of research methods; therefore, it makes good sense to figure out how to use the tools we have available in the smartest, most efficient way possible. Statisticians, computer scientists, and actuaries: You may want to search elsewhere for solutions to your specific challenges.

SPSS File Types

SPSS has three common file types associated with it. First, there's your data file. This file ends with the file extension .sav. Second, there's your output file. This file ends with the file extension .spo, and SPSS automatically creates it when your run an analysis. Third, there's your syntax file. This file ends with the file extension .sps.

Creating a New Syntax File

To create a syntax file, start by opening the SPSS program or a data set (.sav file). Then, go to the drop-down menu at the top of the screen, go to "file," then "new," and select "syntax file." A new screen should open that looks like the image below. Note that an empty data set is in the background, and the new syntax file is in the foreground.

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The syntax file in the foreground serves as your "command center," so to speak, for your data analysis. I recommend maintaining a single syntax file for each project. For example, I used the same single syntax file for all of the analysis relevant to my master's thesis and have separate ones for other projects. Basically, the syntax file is a text document that you can save, "save as," copy and paste items into and from, and so on.

Helpful hint: If you're ever on a computer that doesn't have SPSS yet need to open your syntax file for some reason, you can open it as a text file in Notepad or Wordpad. You won't be able to run the program, but you'll be able to see it, read the notes you made in it, and copy its contents.

So now you know (a) what a syntax file looks like and (b) how to create a new one. Future posts will address next steps and general tips for using the file to analyze your data. Congratulations--you got started, and that's half the battle.

SPSS Basics: Why Syntax Beats Point and Click

Consider yourself very, very lucky: It wasn't that long ago that researchers were calculating all of their statistics by hand, without the use of powerful statistical programs like SPSS, SAS, and Excel. But just because software running on powerful computers allows anyone to spit out statistics doesn't mean that you don't have to know what you're doing and to conduct your analysis in a meaningful way. Because, after all, the numbers that your software program of choice quickly hurls back at you after a few short commands is only as good as (a) the data itself and (b) what you told it to do in terms of analysis. It seems that different academic disciplines gravitate toward different software programs, and many researchers in industrial/organizational psychology, organizational behavior, and human resource management tend to use SPSS. And that's just fine for the majority of the types of analyses we like to conduct (with structural equation modeling being a notable exception). One of the big differences between SPSS and other statistical programs is that it has a variety of "point and click" options, meaning that one can tell the program to run a variety of types of analyses without having any knowledge of the program's programming language simply by using drop-down menus. While that may sound nice to beginners, it has its pitfalls. Most notably, it encourages a haphazard approach toward data analysis that in the end leaves users wondering, "What did I just do?" So I strongly encourage anyone getting started with SPSS to quickly learn to start doing all of their analyses with syntax.

What's syntax? Syntax is what we call the programming language used to conduct data analyses in SPSS without using the “point and click” method. It's useful for a multitude of reasons, including:

• It helps us conduct our analyses in a meaningful, systematic manner
• It is a lasting record of what analyses we conducted
• If something does not work, we can easily find out why
• It can perform operations that are laborious or impossible using the drop-down menus
• It is easily transferred among collaborators

Future posts on this forum will further expand upon the basics of using syntax for analyzing data in SPSS. But for now, consider abandoning the "point and click" method. You'll be glad you did.