Researchware, Inc.

Even at a young age James Kratky had a love of mathematics. As he grew older, so too did that love of mathematics, and when it came time to select an undergraduate major while attending Ferris State University in the central region of Michigan, James followed his strong passion for mathematics and his desire to serve others. He knew that teaching mathematics would be a great career choice.

Upon entering the classroom as a teacher, James quickly realized that what he knew wasn’t nearly enough. He knew that he wasn’t even sure what questions he needed to ask to best improve his teaching practice. People would tell James that he was a good teacher who challenged his students to learn mathematics. However, he believed there had to be more available (both in teaching methods and technology) that could be used together to help students to learn mathematics better. It was because of this that he decided to go to graduate school to expand his understanding of mathematics education.

While obtaining his doctoral degree in Mathematics Education, James conducted a research study surrounding "Instrumental Orchestration"- a construct referring to the teacher’s configuration and exploitation of the classroom and tools while guiding and shaping students’ use of mathematical technologies to enhance their learning.

As the participant in his research study, James selected a college instructor who used both digital statistical tools (e.g. graphing calculators) and specific research-informed teaching practices. The instructor focused on eliciting student thinking to help guide the lesson toward her intended learning goals for the students. Rather than rely on direct lecture, she led her classroom community through group investigations that encouraged conceptual development as the students engaged in each other’s thinking and even challenged the fidelity of the tools they were using! Along the way, she implemented several specific teacher moves to support her pedagogical goals, and James’ research explored and illuminated these characteristics of her instrumental orchestrations.

A goal of James’s research was to see how the teacher was supporting technology in the classroom. He witnessed extensive student exploration. The instructor introduced the mathematical tools to the students and then let them generate whatever statistical representations they thought were appropriate for the given context, while at the same time sharing and building off each other along the way.

James chose to conduct his research and gather his data by placing three video cameras throughout the classroom which captured both classroom interactions and the technology, while at the same time using a wireless mic on the teacher (fed to 1 camera) plus 5 audio recorders for additional audio capture. In a previous study, he had identified tensions between analyzing just transcript data or videos. He sought to alleviate some of that tension by combining data sources. James used transcripts as highly accurate records of what was spoken during class time. However, when the teacher asked questions, she often used wait time (a nonverbal pause lasting for a moment). By allowing for a few seconds of silence, the teacher invited her students to participate and contribute to the statistical conversation. Using a simple transcript would not have highlighted this wait time and other nonverbal subtleties, and therefore James chose to use both transcript data and video data to truly capture a sense of what was taking place in the classroom.

James analyzed 18 hours of video and transcript data with Researchware’s qualitative analysis software, HyperRESEARCH. Having previously used HyperRESEARCH, he found it to have a very easy learning curve, and knowing its capabilities, realized that he could manipulate HyperRESEARCH by writing simple scripts on his MAC that enabled him to synchronize both the multiple videos and transcript data together and code the data for the types of teacher moves made during instruction. In addition to ease of use, and the affordable cost to purchase HR, James also found the sorting and annotation features of HyperRESEARCH to be very helpful. Overall HyperRESEARCH proved to be a very effective tool in helping James analyze his data. “To make the most of all that HR has to offer, researchers still have to reflect on what we do and be willing to put in the effort to make the tool work to the best of its ability” says James. “HyperRESEARCH, like any software, won’t do everything for us. We must keep asking questions and make decisions to help us answer those questions in our research. However, James adds, HyperRESEARCH has a lot more to offer if we are prepared to do the work to get there – it’s part of the process of doing worthwhile research.”

We love to hear from customers who tell us how HyperRESEARCH has helped them do their research.

I was especially delighted when I discovered this video from Pepperdine University's Graduate School of Education and Psychology. It's a presentation organized by Dr. Kay Davis, titled "HyperResearch Creates HyperHappy Doctoral Students." 

The Education Division of the GSEP provides licensed copies of HyperRESEARCH to doctoral students engaging in qualitative research. This video presentation serves as an introduction to the software, discusses why Pepperdine University chose HyperRESEARCH, how they use it, and explores student feedback on the software. 

This presentation was originally offered to Pepperdine University students and faculty in the Graduate School of Education and Psychology (GSEP) in February, 2014. The video replay is available on Pepperdine's "Community" website, and also on YouTube.  

Here's the full video (1 hour - see below for links to key points within the video):

Of course, policies and practices for dissertation research vary among universities and instutitions. So not all of this information will be pertinent to every doctoral student or graduate program!

Here are some links to specific portions of Pepperdine University's video for those interested (will open in a new window and autoplay beginning at the appropriate start time):

Why do we need qualitative analysis software? (timecode 8:28-9:45) "...software provides us with a faster and more efficient way to code our data...much more flexibility... ability to search and query... easy access to multiple users"

Why HyperRESEARCH and HyperTRANSCRIBE software? (timecode 9:45-10:45) "...cross-platform... supports coding, theming, and theory building... highly intuitive, it's user friendly as an interface..."

Why Paula Thompson is a fan of HyperRESEARCH (timecode 15:00-16:40) "... for students at the dissertation phase, it's often the right-sized tool for the volume of data that they're working with and the complexity of analysis that they're doing...."

HyperTRANSCRIBE (timecode 18:28-19:10)

Various features of HyperRESEARCH (timecode 20:07-32:50) (see also Researchware's webinar replays)

Student comments regarding their experience with HyperRESEARCH (timecode 38:52 - 41:07) "...easy to use. made reporting and tracking codes much easier...."

Student comments on their experiences with HyperTRANSCRIBE (timecode 41:08 - 42:52) "...this was a gem! Hyper Transcribe allowed me to easily create transcripts of the interviews for my research...."

Student advice for other students (timecode 42:55 - 45:46) "...begin transcribing your data as soon as possible...", "...learn tool early!"

Are you a doctoral student looking for uncomplicated software to help you organize your qualitative data for your dissertation? Learn more about HyperRESEARCH here: Powerful Tools. No Complications.

Are you a professor looking for the right software tools for teaching qualitative research methods? Let us show you how we can help.

Are you a HyperRESEARCH or HyperTRANSCRIBE user with a special research topic, or a unique approach? Tell us about your research!

Background

Many researchers and research teams develop a code list prior to the coding of any documents. Initial thought as to potential and anticipated major and minor themes expected in the data can help focus and guide coding efforts. HyperRESEARCH allows the import and export of code lists to its Code Book or list of master codes.

The information below:

• explains the options available for importing code lists to HyperRESEARCH, and 
• provides downloadable Microsoft Excel templates you can use for the style of code list, ranging from basic to full featured, that you wish to create and import.

A Basic Code List

The simplest code list that can be imported is a simple list of code names, one per line. In the first of our Microsoft Excel templates, or any text editor, you can simply create a list of code names (see side bar for name restrictions).

Once you have developed your code list in the Excel template, you can prepare it for import with the following steps:

If you are working in Excel:

1. Remove (Delete) the header line from the template
2. Select Save As from the File menu/tab to save the template
3. Select Text (Tab Delimited) (*.txt) from the Save as type: pop up menu in the Save As dialog.
4. Click Save to save your code list. Excel will warn you about saving in the text format. Just click Yes.

If you are working in HyperRESEARCH:

1. From the Edit Code pop up menu in the Code Book, select Import List... to import the code list you created.
2. HyperRESEARCH will remind you that the list will be appended or added to the current Code Book. Click OK to add the codes.

The Code Book shown to the left will be produced upon importing the list shown in the template above.

A Basic Code List with Code Descriptions

Code descriptions in HyperRESEARCH's Code Book can be used to keep notes about anything the researcher(s) like. Most often, code descriptions are used to keep guidance on how the code is intended to be applied or to clarify the difference between similar codes used in a study.

To create an importable list of codes with code descriptions, simply add a second column in Excel (or a second column separated by a tab in a text editor) for the Code Description. The Code Description column can contain any characters except a line break (carriage return). Code Descriptions can be blank or empty.

Following the same steps as above to save the edited template for importing as a code list:

1. Remove (Delete) the header line from the template
2. Select Save As from the File menu/tab to save the template
3. Select Text (Tab Delimited) (*.txt) from the Save as type: pop up menu in the Save As dialog.
4. Click Save to save your code list. Excel will warn you about saving in the text format. Just click Yes.

Importing the same template shown above for a code list with code descriptions produced the resulting code list shown to the left.

A Code List with Code Groups

To create a code list with code groups, two additional columns need to be added. They must be in order after the Code (or group) Name column. These are the Type (code or group) and the Parent Group. The row order does not matter, but it is a good convention for easier reading to list Groups before you use them. The Type column cannot be blank and must contain only the word code or group depending on whether the name is for a code or a code group. The Parent Group column must contain the name of the parent group for every code or group. The Top level group or default group or root of the tree is called All Codes. The Parent Group can not be blank.

Repeat the same steps as previously described to remove the header line and save the template as a tab delimited file and then import the code list into HyperRESEARCH.

The template above produces the code list shown to the left when imported. Notice that A Sample Code and Another Sample Code have been placed in the group called Sample Group.

A Full Code List (with everything)

A template for a full code list, combining everything from the examples above, consists of four columns. The Name, Type, Parent, and Description columns. The columns must be in that order. Descriptions can be optional (blank). You can assign a Code to more than one group by separating the Parent group names by a vertical bar "|" as illustrated for the code called A Last Sample Code shown in the template below. In this example, the code is assigned to both the Sample Group and the Last Codes Group.

Repeat the steps previously described to save the code list and import it to HyperRESEARCH.

The Code Book shown to the left results from importing the template above. Note that A Last Sample Code has been placed in two groups, the Sample Group and the Last Codes Group and the description for the Sample Group.

Confidentiality of the sensitive information provided by participants in a research study has always been an area of concern, especially for qualitative researchers. How do I use information from my participants to tell the narrative of my research, while still ensuring appropriate confidentiality is preserved?

A recent paper entitled "No silver bullet: De-identification still doesn't work" by Arvind Narayanan and Edward W. Felten of Princeton University, thoughtfully reminds us that in this era of "Big Data" and access to large public data sets, it's easier than ever to re-identify participants based on "anonymous" data. Location, details of personal history, and other data that is seemingly not specific to any one person can be collated with public sources to narrow down and identify an individual.

One small way to keep pace is with tools that help ensure confidentiality. At Researchware, we've begun this process by including data masking in HyperRESEARCH's Report Builder.

Clicking "Mask Confidential Data" displays the masking feature, allowing the researcher to automatically hide names or other identifying information in their source material, replacing the information with either pseudonyms or an explicit indication that this is confidential data.

As the Narayanan & Felton article reminds us, "Data privacy is a hard problem." As we continue improving HyperRESEARCH, we'll be looking at data privacy improvements, such as automatically searching your data for information that could potentially be used to breach confidentiality and giving you the option of hiding it. Attentiveness to the new dangers to privacy – on the part of researchers and software developers – will help us all ensure research participants that their personal information will stay private.