Blog Index

2016, Current to 11/30

Topic	Dates of Posts
Dissertation, general	4/8, 4/11, 5/11, 6/20, 8/29, 9/2
Dissertation calculator	4/15
Selecting a Topic	9/7
Committee Members	8/10, 9/19
Mentor Interviews	9/21, 9/23, 9/26
URR
Center for Research Quality
Overview of Process
Premise
Prospectus	2/3
Proposal	2/5
Research questions	4/13
Research design
Theory	2/1, 4/27, 5/2, 5/4
C. 1	3/23, 10/1, 10/3, 10/5, 10/7, 10/10, 10/12
C. 2 (literature related)	1/4, 1/6, 1/8, 1/11, 1/13, 1/15, 1/20, 1/22,1/25, 2/10, 3/7, 3/9, 7/11, 7/13, 8/22, 10/14, 10/17, 10/19, 10/21, 10/24
C. 3	3/21, 3/25, 10/26, 10/28, 11/7, 11/9, 11/11, 11/14, 11/16, 11/18, 11/21, 11/25
Defense
IRB	3/28, 4/4
Data Collection	6/3, 6/6, 6/8, 6/10, 6/13, 6/15, 6/17
Quantitative	11/9, 11/11, 11/18, 11/25
Qualitative	5/16, 5/18, 5/20, 5/23, 8/1, 11/2, 11/4, 11/16
Mixed Methods	11/2, 11/4, 11/18, 11/25
C. 4	9/16
C. 5
Appendixes
Final Defense
Career	9/9
Goal Form
Motivation	5/9, 7/1, 7/4, 7/6, 7/8, 7/15, 7/20, 7/22, 7/25, 8/8, 8/15
Organizing	2/22, 2/24, 2/26, 3/4
Secondary Data
Support, Getting	8/17
Resilience	5/25, 5/27
Writing	3/11, 3/14, 3/16, 6/24, 7/18, 8/3, 8/5, 8/12, 8/26
Other	1/1, 1/17, 1/29, 2/12, 2/15, 2/17, 3/2, 4/1, 4/6, 4/18, 4/20, 4/22, 4/25, 5/6, 5/13, 6/1, 6/22, 6/27, 7/27, 8/17, 8/19, 8/24, 9/12, 9/14, 9/19, 9/28, 11/23, 11/28

Previous Years

Topic	Dates of Posts - 2013	Dates of Posts - 2014	Dates of Posts - 2015
Dissertation, general	7/5, 8/16, 8/19, 9/27, 10/2	6/25, 12/5	1/7, 3/6, 11/20, 12/16
Dissertation calculator		9/5	3/9
Selecting a Topic	4/23, 7/8, 7/10	4/28, 5/9, 8/25
Committee Members	4/17, 5/3, 6/10, 7/19, 8/21	5/7, 10/8, 12/22	9/14, 10/14, 12/18
URR	5/8, 5/27
Center for Research Quality	12/9
Overview of Process	4/19, 9/18, 12/13
Premise (no longer used)	4/17, 9/6
Prospectus		4/4, 4/7, 4/9, 4/11, 4/14, 4/18, 4/21, 4/23, 4/25, 4/28, 5/2, 5/5, 8/27
Proposal	4/22, 9/9	9/8, 11/3, 11/5
Research questions	10/9	4/18	6/19
Research Design			6/15, 6/22, 6/26
Theory			6/15, 6/17
C. 1	5/6, 10/21, 10/23, 10/25, 10/28, 11/1	11/7	3/11, 3/13, 3/16, 3/18, 3/20, 9/18, 9/21
C. 2 (literature related)	4/26, 5/29, 6/3, 6/12, 6/17, 6/28, 9/16, 10/11, 11/4, 11/6, 11/9, 11/15	6/9, 6/11, 6/16, 9/10, 9/15, 9/17, 9/19, 9/26, 9/29, 11/10, 12/26	1/9, 3/23, 3/25, 3/27, 4/3, 4/8, 9/23, 10/21
C. 3	5/1, 10/16, 10/28, 11/18, 11/20, 11/22, 11/25, 12/2, 12/4, 12/6, 12/11, 12/16, 12/18, 12/20, 12/23, 12/27	1/3, 1/6, 1/13, 11/12	4/10, 4/13, 4/15, 4/17, 4/20, 4/22, 4/24, 4/27, 4/29, 5/4, 5/6, 5/8, 5/11, 5/13, 5/15, 5/18, 9/25
Defense	4/23, 5/8		9/28
IRB	5/10, 10/14	1/10, 1/15, 1/17, 1/20, 1/22, 1/24, 1/27, 1/29, 2/3, 2/5, 2/7, 2/10, 2/12, 2/17, 2/19, 2/21, 2/24, 10/13, 10/15, 10/17, 10/20, 10/22, 10/24, 10/27	6/24 10/5
Data Collection	5/13, 5/15, 10/16		6/24, 11/23, 11/27, 12/4, 12/7, 12/9
Quantitative	5/17, 7/24, 7/26, 7/29, 7/31, 8/2, 8/5, 10/4, 10/7, 11/20, 12/2, 12/4, 12/6, 12/18, 12/23, 12/27	1/3, 2/26, 3/12, 7/9, 7/14, 7/16, 7/18, 7/21, 7/23, 7/25, 7/28	5/4, 5/8, 5/11, 5/13, 5/15, 7/1, 7/6, 7/8, 7/13, 7/15, 7/17, 7/20, 7/22, 7/24
Qualitative	5/20, 11/20, 11/22, 11/25, 12/11, 12/16	1/6, 3/14, 10/29, 12/12, 12/15, 12/17, 12/19	1/5, 1/12, 10/19
Mixed Methods	5/22, 11/18, 11/20, 11/22, 11/25, 12/11, 12/20, 12/23, 12/27	1/3, 1/6	4/10, 5/6, 5/8, 5/11, 5/13, 5/15
C. 4	5/17, 5/20, 5/22, 7/17	3/5, 3/10, 3/12, 3/14, 3/17, 3/19, 3/21, 11/14	5/20, 5/22, 5/25, 5/27, 6/3, 6/5, 10/7
C. 5	5/24, 9/20, 10/11	3/21, 3/24, 3/26, 3/28, 11/19	6/5, 6/8, 6/10, 6/12, 10/9
Final Defense	4/23, 5/27/ 9/11		10/12
Career	7/12	8/18, 8/20
Goal Form	8/12	5/23, 5/26, 6/2, 8/15, 11/24
Motivation	6/5, 6/26, 7/1, 8/16, 8/23, 9/2, 9/18, 10/18, 11/8, 11/27	1/1, 6/4, 6/6, 7/4, 7/11, 9/22, 10/10, 12/3	1/21, 3/2, 7/3, 7/10, 9/2, 9/4, 9/7, 9/9 9/11
Organizing	4/22, 10/2	8/1, 8/4, 8/6, 8/8, 8/11, 8/13, 12/8	7/27, 7/29, 8/3, 8/5, 8/10
Secondary Data	5/31	2/24
Support, Getting	4/26, 6/5, 6/24, 8/16	5/21	1/26
Resilience			2/6, 2/16, 2/18, 2/20, 2/23, 2/25, 3/4
Writing	4/26, 4/29, 6/12, 6/21, 7/3, 8/9, 8/14, 9/4, 9/23,9/25	5/16, 5/19, 6/16, 6/18, 6/20, 7/2, 7/7, 8/27, 12/10	1/14, 1/23, 2/9, 12/11, 12/14
Other	4/18, 6/7, 6/14, 6/19, 6/24, 6/26, 7/1, 7/8, 7/15, 7/19, 7/22, 8/7, 8/16, 8/19, 8/26, 8/28, 8/30, 9/2, 9/13, 9/18, 10/18, 11/27, 12/13, 12/25	2/14, 3/3, 3/7, 4/16, 5/12, 5/14, 5/28, 6/2, 6/13, 6/23, 6/27, 8/22, 9/1, 9/3, 9/12, 10/3, 10/6, 11/17, 11/26, 12/1, 12/24, 12/29, 12/31	1/16, 1/19, 1/28, 1/30, 2/4, 2/11, 2/13, 4/1, 4/6, 6/1, 8/12, 8/14, 8/19, 8/21, 8/24, 8/26, 8/31, 9/16, 10/2, 10/16, 10/23, 10/26, 10/28, 11/2, 11/4, 11/6, 11/9, 11/11, 11/13, 11/16, 11/18, 11/25, 12/2, 12/21, 12/23, 12/25, 12/28

Next time we will examine threats to validity. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Welcome to winter quarter

Welcome to winter quarter! During this quarter, it is very important to consider a realistic goal for what you can achieve. It is a good idea to lay out week by week what you can accomplish, taking into account holidays and vacations.

If you have not already done so, begin to establish some good dissertation habits, such as carrying articles to read during down and waiting times, and planning some special dissertation time to work. Many students find it helps to go to a library or quiet coffee shop where you won’t be disturbed. Make your dissertation a priority; otherwise you are just wasting your money. 

Next time I will post an updated blog index. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Chapter 3: Threats to Validity: external validity – quantitative and mixed methods studies

The threats to validity section in the quantitative and mixed methods checklist is one of the trickiest to understand. Let's see if I can make some sense of it for you. It begins with threats to external validity. External validity is the extent to which the researcher can conclude that results apply to a larger population (generalizability). Here are some common threats to external validity.

•Reactive or interaction effect of testing--a pretest might increase or decrease a subject's sensitivity or responsiveness to the experimental variable. Therefore, giving a pretest changes your participants, they will respond differently later because they took the pretest.

•Interaction effects of selection biases and the experimental variable. You may unintentionally choose people that have particular biases. For example if you are doing an online survey about use of the internet- you will only have people participate who are already comfortable enough with the computer and internet to choose to participate in an online survey. You will be missing people who are not comfortable with computers.

•Reactive effects of experimental arrangements--it is difficult to generalize to non-experimental settings if the effect was attributable to the experimental arrangement of the research. So, if you are doing some type of experiment in a controlled setting (picture a quiet psychology lab room), there is no way to know what will happen when a similar occasion occurs in the real world.

•Multiple treatment interference--as multiple treatments are given to the same subjects, it is difficult to control for the effects of prior treatments. People cannot "unlearn" something, so whatever has happened to them previously will affect future learning/ experiences.

In this section of chapter 3, you need to think through what are the threats to external validity in your study. Keep in mind that no study is perfect, it is ok, in fact, it is expected that there will be issues. The important thing is that you recognize them.

Next time, we will look at threats to validity- internal validity quant and mixed methods. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Giving Thanks

Tomorrow is Thanksgiving, let's take a moment to think about how that relates to your dissertation. As a dissertation student, you have come so far in your education! The doctorate is considered the terminal degree, meaning, there are none higher than that. Just making it to the point of working on the dissertation is a great achievement- take a moment and realize how far you have come!

Only 1% of the population has a doctoral degree, you are very privileged, you are bright and capable, or you would not have gotten this far. Think of all of the faculty who have given you their time, effort, and wisdom to help you get here… they stretch out far beyond graduate school… all the way to high school, elementary school, and kindergarten. Send them blessings.

Think about all of your classmates and friends who have helped you reach this point. All of the people who have shared your frustrations and your joys, who have been there for support. Send them thanks.

And finally, think about your family members who have given up so much for you to achieve this goal. They have given you time to work, held you in crisis, and celebrated with you in your accomplishments. They may be your cheerleader, or your silent supporter, but you know they are behind you. Take a moment to let them know you appreciate them.

I hope you have a wonderful holiday!

Next time we will continue our review - Chapter 3: Threats to Validity. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Chapter 3: Instrumentation in mixed methods studies

For mixed methods, the instrument section has to accommodate both qual and quant instruments. For the qualitative components, identify each instrument (observation sheet, interview protocol, focus group protocol, videotape, audiotape, artifacts, archival data, and other kinds of data collection instruments). State the source of each item and provide the permission for it in your appendix.

For published instruments, identify who developed it, where and with what populations it has been used. State why you think it is appropriate for your study, and any cultural or context issues that might be present with your population.

If you are designing qualitative instruments, explain how you developed them – what was the basis for them? How will you establish content validity?

Similarly, for the quantitative components, explain the background of each instrument. Discuss validity and reliability in previous studies and where it has been used before.

The next section is how you will recruit participants for each component (qual and quant). Go into detail on how and where the data will be collected for each component.

Finally, you need to lay out your data analysis plan for each component. For the quantitative aspects, indicate your hypotheses and what statistical tests will be used for each. How will you interpret the results? For the qualitative portion, indicate how you will code the transcripts and how you will handle discrepant cases. Then you need to integrate the two types of data and compare their results. How will you do this?

Next time we will give thanks. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Chapter 3: Instrumentation in quantitative studies

Previously, we looked at instrumentation in qualitative studies, this time we move to quantitative studies. Generally, students will be relying on previously published survey instruments for quantitative studies. I do not recommend developing new surveys for a doctoral study. I will explain more on this below.

You will begin this section by discussing each instrument you will be using, who developed it and when. Then indicate why you have chosen it, and why it is appropriate for your study. You will need permission to use the instrument from the developer- include it in your appendix. Then go into detail about the published validity and reliability values that are relevant for your study (those using similar populations). Finally, you will discuss when and with what populations it has been used and how validity and reliability were established for each study.

If you are developing your own survey instrument, first describe the basis for its development. Did it come through items indicated in the literature? Did you or plan to do a pilot study to refine the questions? You will need to provide evidence for its reliability and validity, this typically requires extensive testing (100s of participants). Finally, show how the instrument will answer your research questions. If you are developing your own instrument, it will require quite a bit of testing and additional work; again, I do not recommend this for a dissertation.

If you are doing an intervention involving manipulation of an independent variable, there are issues that will come up with the IRB, address them early! As far as c. 3, identify any materials that will be used in the intervention. Indicate who developed the materials and where they have been used in the past (you also may need permission from the developer to use them). If you developed them, indicate how that was done. Provide evidence that another agency will sponsor the intervention.

Next, you need to operationalize each variable. So for example, if you are interested in resilience, define it and how you will be measuring it. Then talk about how each variable or score is calculated and what the scores represent. Give an example item from each scale/ subscale.

The final portion of this section is your data analysis plan. Mention what software you will use, how you will clean the data (how you will handle missing data, and make sure there are not any extreme outliers). Restate your research questions and hypotheses from c 1. Then, for each hypothesis describe the statistical tests that will be used. If you are doing many statistical tests, you need to account for that by using a correction statistic (Bonferroni's is common- it reduces your p value, based on the number of tests). If you are using covariates and/or have confounding variables, you need to discuss it. Finally, how will you interpret the results (key parameter estimates, confidence intervals and/or probability values, odds ratios, etc.).

Next time we will continue our review - Chapter 3: Instrumentation in mixed methods studies. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu