Month: December 2023

Manual Qualitative Data Analysis Tutorial Using Creswell & Poth’s Data Analysis Spiral

Posted on by Omega Graduate School

Manual Qualitative Data Analysis Tutorial Using Creswell & Poth’s Data Analysis Spiral

Hello, I’m Dr. Sara Reichard, MLitt Tutor at Omega Graduate School, and this is a brief video tutorial on manual qualitative data analysis using Microsoft Word. In this tutorial, we will explore a very easy, manual approach to analyzing qualitative data, specifically focusing on using text color highlighting in Microsoft Word to identify codes and organize them under themes. We will also follow Creswell and Poth’s Data Analysis Spiral, a comprehensive framework for qualitative data analysis. Let’s get started!

Introduction

 Qualitative data analysis involves extracting meaning and insights from qualitative research. While various software programs are available for data analysis, using Microsoft Word can be cost-effective, especially for researchers who prefer a manual approach. This tutorial will use Microsoft Word’s features to analyze qualitative data efficiently.  Creswell and Poth’s Data Analysis Spiral is a simple five-step process for qualitative data analysis: Step One: Managing and organizing the data (data preparation), Step Two: Reading and memoing emergent ideas, Step Three: Describing and classifying codes into themes, Step Four: Developing and assessing interpretations, Step Five: Representing and visualizing the data.

Step One – Managing and Organizing Data

 The first step in the data analysis is managing and organizing the data. Create a new document in Microsoft Word and copy and paste your qualitative data, such as interview transcripts or field notes, into the document. It’s important to ensure that each participant’s data is clearly labeled and organized to facilitate analysis. You can create headings or subheadings for each participant or group of participants, making navigating the document easier.

Step Two – Reading and Memoing Emergent Ideas

 Once the data is organized, proceed to read through the data carefully. As you read, make notes or memos about emergent ideas, patterns, or insights you observe. This process helps you capture initial impressions and thoughts that may guide further analysis. You can add annotated comments in Microsoft Word to record these memos alongside the relevant data. Remember to remain open-minded during this stage, as inductive and abductive coding can emerge from these observations.

Step Three – Describing and Classifying Codes into Themes

We start the coding process in the third step of the Data Analysis Spiral. Select a color for each code you want to identify. For example, you can assign one color representing a specific theme or concept. Using Microsoft Word’s text color highlighting feature, apply the designated color to the relevant sections of text that represent each code. This visual representation allows you to see patterns and connections across the data.

Next, create headings to organize these codes into themes. For instance, you can create a separate section for each theme and cut and paste the relevant text excerpts under their respective themes. Microsoft Word’s cut-and-paste functionality makes it easy to reorganize the data as you refine your themes and sub-themes. Remember, this process is iterative, and you may need to revisit and revise your codes and themes as you gain deeper insights into the data.

Step Four – Developing and Assessing Interpretations

In the fourth step, it’s time to develop interpretations based on emerging themes and patterns. Take the time to analyze the data within each theme, examining the relationships and meanings embedded in the text. Ask yourself questions such as “What does this pattern or theme signify?” or “How does it relate to the research objectives?” This interpretive process lets you explore the data and answer your research questions.

To assess the validity and reliability of your interpretations, it’s important to engage in member checking or seeking feedback from participants to ensure your transcripts accurately reflect what participants said during the interview.

Step Five – Representing and Visualizing the Data

The final step in the Data Analysis Spiral involves representing and visualizing the data. Microsoft Word offers various tools for creating tables, charts, or diagrams to enhance data presentation. You can create tables to summarize key findings or use charts and diagrams to visualize the relationships between themes or sub-themes. These visual representations provide a comprehensive overview of the qualitative data and support communicating your research findings.

Sample Interview Data

Now, let’s take a look at some sample qualitative interview data. This study is on how healthcare providers perceive the role of spirituality in a small Midwestern hospital. Here are some sample interview transcripts with inductive codes and themes already identified according to the research question.

In analyzing the interview transcripts, several key codes and themes emerged about healthcare professionals’ perceptions regarding spirituality’s role in patient care. Let’s explore how these codes and themes were identified.

Inductive Codes

First, the inductive coding process involved carefully reading and reviewing the interview transcripts to identify recurring ideas, concepts, and perspectives. Through this iterative process, the following inductive codes were identified:

  1. Patient Care – This code represents the overall focus on providing comprehensive care to patients, encompassing their physical needs and emotional, psychological, and spiritual well-being.
  2. Holistic Wellbeing – This code emphasizes the significance of addressing patients’ holistic well-being, recognizing that spiritual care is integral in promoting overall health and healing.
  3. Incorporating Spirituality in Practice – This code reflects the healthcare providers’ belief in integrating spirituality into their practice. It encompasses their efforts to acknowledge and address the spiritual needs of patients as an essential component of care.
  4. Patient Comfort – This code highlights the healthcare providers’ commitment to creating a comforting and supportive environment for patients, recognizing that spiritual beliefs and practices can bring solace and peace during challenging times.
  5. Respect for Diverse Beliefs – This code underscores the healthcare providers’ understanding of the importance of respecting and honoring patients’ diverse religious and spiritual beliefs. It involves fostering an inclusive environment that allows patients to express and practice their

Application and Reporting

Once the codes were identified, the next step was to cluster them into meaningful themes. Two overarching themes emerged from the data:

Theme 1 – Healthcare providers believe they should incorporate spirituality into their practice to address aspects of patient wellbeing holistically. This theme encompasses the codes of Holistic Wellbeing and Incorporating Spirituality in Practice. It recognizes that spirituality is essential to patient care, contributing to individuals’ overall wellbeing and healing.

Theme 2 – Healthcare providers believe respecting diverse beliefs can foster patient comfort and enhance patient care. This theme combines the codes of Respect for Diverse Beliefs, Patient Care, and Patient Comfort. It emphasizes the importance of creating an inclusive and respectful environment that acknowledges and supports patients’ diverse religious and spiritual beliefs, ultimately enhancing their comfort and the quality of care they receive.

The codes and themes illustrate healthcare professionals’ perspectives regarding the role of spirituality in patient care. These insights can inform the development of strategies and interventions promoting holistic care, respecting diversity, and enhancing the overall patient experience.

Illustrating Findings

To illustrate your findings, generate an APA-style table with an overview of the themes, codes, and some rich, thick descriptions based on participant responses.

Conclusion

That concludes our tutorial on analyzing qualitative data using Microsoft Word. We have covered the key steps of Creswell and Poth’s Data Analysis Spiral, including managing and organizing the data, reading and memoing emergent ideas, describing and classifying codes into themes, developing and assessing interpretations, and representing and visualizing the data. Remember, qualitative data analysis is an iterative process that requires time, reflection, and ongoing refinement. Microsoft Word can be a valuable tool to facilitate this process effectively.

Earn Your PhD Degree in Social Research

Privacy Policy

Do you have more questions?  Our admissions team is standing by to answer any questions you may have.

 

  Questions?  Contact Admissions!

 

When to Use Parametric versus Nonparametric Procedures in Statistics for Social Research

Posted on by Omega Graduate School

When to Use Parametric versus Nonparametric Procedures in Statistics for Social Research

Using parametric procedures in statistics means you can generalize your findings from the sample to the population. You can use parametric procedures when you have a large sample size or when your sample meets certain assumptions about normality. If you have a small sample size or your sample cannot meet assumptions about normaltiy, you can still test your hypotheses with a nonparametric procedure. Using a nonparametric procedure means your findings only apply to the sample, not the population.

Parametric Procedures

  • Assumptions: Require data to follow a specific distribution, typically normal distribution.
  • Examples: t-Test, Pearson correlation.
  • Use When: You have a sufficiently large sample size, and your data meets the assumptions of normality, homogeneity of variances, and linearity.

Nonparametric Procedures

  • Assumptions: Do not require data to follow a specific distribution.
  • Examples: Wilcoxon test, Mann–Whitney U test, Spearman correlation.
  • Use When: Data does not meet the assumptions for parametric tests, with ordinal data, or when dealing with small sample sizes.

t-Test vs Wilcoxon Test vs Mann–Whitney U Test (Quasi-Experimental Hypotheses)

  • t-Test:
    • Use When: Comparing means of two groups.
    • Conditions: Normal distribution, similar variances, interval or ratio scale data.
    • Ideal For: Larger sample sizes.
       
  • Wilcoxon Test:
    • Use When: Non-normal or ordinal data; paired samples.
    • Conditions: Useful for smaller sample sizes; for matched or paired samples.
    • Ideal For: Situations where t-test assumptions are not met; paired data.
        
  • Mann–Whitney U Test:
    • Use When: Comparing two independent samples.
    • Conditions: Non-normal data or different sample sizes.
    • Ideal For: Situations similar to the t-test but without the normality assumption; unpaired data.

Pearson vs Spearman Correlation (Correlational Hypotheses)

  • Pearson Correlation:
    • Use When: Evaluating the linear relationship between two continuous, normally distributed variables.
    • Conditions: Variables on interval or ratio scale.
    • Ideal For: Expected linear relationships.
  • Spearman Correlation:
    • Use When: Data is ordinal or not normally distributed.
    • Conditions: Assessing monotonic relationships.
    • Ideal For: Non-linear relationships or data not meeting Pearson’s criteria.

Summary

  • Use parametric procedures (t-Test, Pearson) when your data adheres to normal distribution criteria and you have a large sample size.
  • Opt for nonparametric procedures (Wilcoxon for paired samples, Mann–Whitney U for independent samples, Spearman) in small sample sizes, ordinal data, or when parametric assumptions are not satisfied.
  • How to Conduct Normality Tests in PSPP in Statistics for Social Research

Earn a PhD Degree in Social Research

Privacy Policy

Reach Out to OGS Admissions!

 

  Questions?  Contact Admissions!

 

How to Conduct Normality Tests Using PSPP in Statistics for Social Research

Posted on by Omega Graduate School

 

How to Conduct Normality Assumptions Tests Using PSPP in Statistics for Social Research

In PSPP, when conducting normality testing on continuous variables, focus on two key outputs: skewness and kurtosis. Skewness measures the asymmetry of your data distribution. A skewness value close to zero indicates a symmetrical distribution. Positive skewness shows a tail on the right side, while negative skewness indicates a tail on the left.

On the other hand, Kurtosis reflects the ‘tailedness’ of the distribution. A kurtosis value close to zero suggests a distribution similar to the normal distribution in terms of tail thickness. High positive kurtosis indicates heavier tails than a normal distribution, implying more outliers. Negative kurtosis shows thinner tails, suggesting fewer outliers.

To assess normality, compare these values against the standard normal distribution benchmarks. Significant deviations from zero in either skewness or kurtosis can indicate a departure from normality. This assessment helps decide the appropriateness of parametric tests, which assume normal data distribution.

You can use certain numerical thresholds to determine if your data are within the bounds of normality based on skewness and kurtosis values. However, it’s important to note that these thresholds are not strict rules but general guidelines, and interpretations may vary slightly depending on the field of study or the specific statistical approach.

Skewness

  1. A skewness value between -1 and +1 is generally acceptable for normal distribution.
  2. Values between -1 and -0.5 or between +0.5 and +1 might be moderately skewed.
  3. Values less than -1 or greater than +1 are typically regarded as highly skewed.

Kurtosis

  1. Kurtosis values are often compared to 3, which is the kurtosis of a normal distribution (sometimes, this is adjusted to 0, depending on whether the software uses excess kurtosis, which subtracts 3 from the raw kurtosis value).
  2. A kurtosis value between 2 to 4 (or -1 to +1 for excess kurtosis) is generally acceptable.

Values outside this range might indicate that the data have tails that are too heavy or too light compared to a normal distribution.

These values are more like rough guidelines rather than definitive limits. Statistical decisions should not be based solely on these criteria but should also consider the context of the data, the sample size, and other statistical considerations. Additionally, visual methods such as histograms or Q-Q plots are often used in conjunction with these numerical methods for a more comprehensive assessment of normality.

Step-by-Step Procedures in PSPP

  1. Open the dataset in PSPP
  2. Click on “Analyze”–>”Descriptive Statistics”–>”Descriptives…”
  3. Choose your continuous variables (at the ordinal, interval, or ratio levels) and use the ” >” button to move them to the “Variables” box on the right.
  4. Deselect all options under statistics except for “Kurtosis” and “Skewness.”
  5. Click “OK.”

In the output window of PSPP, you will see a table similar to this:

╭────────────────────────────┬──┬────────┬─────────┬────────┬─────────╮
│                            │ N│Kurtosis│S.E. Kurt│Skewness│S.E. Skew│
├────────────────────────────┼──┼────────┼─────────┼────────┼─────────┤
│Religiosity_Level           │25│   -1.42│      .90│     .38│      .46│
│Social_Justice_Attitudes_1_5│25│    -.71│      .90│    -.31│      .46│
│Social_Cohesion_Score_1_10  │25│   -1.02│      .90│    -.02│      .46│
│Valid N (listwise)          │25│        │         │        │         │
│Missing N (listwise)        │ 0│        │         │        │         │
╰────────────────────────────┴──┴────────┴─────────┴────────┴─────────╯

To analyze the output for normality based on the kurtosis and skewness values, let’s examine each variable in the provided data:

Religiosity_Level

  • Kurtosis: -1.42 (With a standard error of 0.90)
  • Skewness: 0.38 (With a standard error of 0.46)

The kurtosis value is slightly below the acceptable range, suggesting a distribution with lighter tails than a normal distribution. The skewness is within the acceptable range (-1 to +1), indicating a fairly symmetrical distribution. However, the standard errors are relatively high, which might affect the reliability of these measures due to the small sample size (N=25).

Social_Justice_Attitudes_1_5

  • Kurtosis: -0.71 (With a standard error of 0.90)
  • Skewness: -0.31 (With a standard error of 0.46)

Both kurtosis and skewness values are within the acceptable range for a normal distribution. The distribution appears to have slightly lighter tails and is fairly symmetrical.

Social_Cohesion_Score_1_10

  • Kurtosis: -1.02 (With a standard error of 0.90)
  • Skewness: -0.02 (With a standard error of 0.46)

The kurtosis value is slightly outside the preferred range, indicating lighter tails than a normal distribution. The skewness is almost zero, suggesting a very symmetrical distribution.

In this case, the skewness values for all variables are within the acceptable range, indicating symmetry in the distributions. The kurtosis values for all variables are slightly lower than the normal range, suggesting distributions with lighter tails than a normal distribution. However, the standard errors are quite large compared to the kurtosis and skewness values, which is a common issue in smaller samples and can affect the precision of these estimates.

Therefore, while the data do not show extreme deviations from normality, the reliability of these results might be limited due to the small sample size. Additionally, visual assessments and other statistical tests should be used to confirm these findings.

Example Visualizations

This is what the histograms (distributions) of each variable might look like:

Kurtosis and Skewness Histograms - PSPP
Kurtosis and Skewness Histograms – PSPP

Religiosity Level

  • This graph shows a slight positive skew and light tails (platykurtic). The distribution is fairly symmetrical but slightly skewed to the right.

Social Justice Attitudes

  • The distribution here is slightly negatively skewed with light tails. It is mostly symmetrical but with a slight skew to the left.

Social Cohesion Score

  • This graph presents a distribution with almost no skew and light tails. It’s very symmetrical, closely resembling a normal distribution, but with the tails being lighter than a standard normal distribution.

What Does It Mean?

In this case, we need to look at the Skewness and Kurtosis of each variable to determine if we can use parametric or nonparametric statistical procedures. The results of parametric procedures can be generalized to the population, and nonparametric procedures can only be applied to the sample.

How to proceed is a judgment call by the researcher. In this case, each variable is mostly within the acceptable ranges with slight skews. Using parametric procedures on any statistical tests involving these variables would likely be safe. Still, because the sample sizes are small, it might be necessary to expand the sample size. 

Earn a PhD Degree in Social Research

Privacy Policy

Reach Out to OGS Admissions!

 

  Questions?  Contact Admissions!

 

DPhil vs PhD: Breaking Down the Pros and Cons of Each Advanced Degree

Posted on by Omega Graduate School

DPhil vs PhD: Breaking Down the Pros and Cons of Each Advanced Degree

In this article we will explore DPhil vs PhD, comparing and contrasting the two advanced degrees. Explore the OGS Doctor of Philosophy Degree in Social Research (Religion and Society Studies)

As an adult learner seeking an advanced degree, you already know the power of research in driving meaningful social transformation. This post will explore how research shapes social change and discuss the skills and expertise required to conduct impactful research and the differences between a PhD and DPhil nomenclature for a Doctor of Philosophy degree.

Research is the foundation upon which social change advances. It allows us to understand the root causes of social issues, identify effective solutions, and measure the impact of our efforts. Without research, our actions may be misguided or ineffective, and we risk wasting valuable time and resources.

One of the key skills required for conducting impactful research is problem identification. As researchers, we must have a deep understanding of the social issues we aim to address and be able to define the research problem clearly. This involves conducting a comprehensive literature review to identify knowledge and gaps in the field.
Once the problem is identified, developing a research design is next. This involves selecting appropriate research methods and tools that will enable us to collect relevant and reliable data. It’s important to consider ethical considerations and ensure our research methods are valid and reliable.

Data analysis is another skill in conducting research, which involves organizing, interpreting, and making sense of the data collected. Researchers can uncover patterns and trends using appropriate statistical techniques and data visualization tools and draw meaningful conclusions from their findings.
Effective communication is also required for research on social change. As researchers, we must convey our findings clearly and compellingly to stakeholders, including policymakers, community leaders, and the general public. Communication requires strong writing and presentation skills and the ability to adapt our communication style to different audiences.

Lastly, collaboration and stakeholder engagement are essential for impactful research in leading social change. By involving community members, organizations, and individuals directly affected by the social issue, researchers can ensure that their research is relevant, meaningful, and has a greater chance of driving change. Collaboration allows diverse perspectives and expertise, leading to more comprehensive and effective solutions.

What is the difference? DPhil vs PhD

When pursuing advanced degrees, the terms DPhil and PhD often come up. While both are doctoral degrees, there are some differences between them. Let’s explore what sets apart DPhil vs PhD.

The naming convention is the most significant distinction between a DPhil and a PhD. DPhil stands for Doctor of Philosophy, and it is the degree awarded primarily in the United Kingdom and some other countries, including Australia New Zealand. On the other hand, PhD is short for Doctor of Philosophy and is awarded in many countries, including the United States.

Despite the difference in nomenclature, DPhil and PhD are generally equivalent in terms of academic rigor and the level of expertise required. Both degrees typically involve several years of intensive research and completing a thesis or dissertation based on original research.

One key difference DPhil vs PhD is the examination process. In a DPhil program, the candidate usually undergoes an oral examination, often called a viva voce or an oral defense. This examination is conducted by an expert panel, including internal and external examiners, who assess the candidate’s understanding of their research topic and the broader field of study. This oral examination can be rigorous and involves defending the research findings and methodology.

In contrast, the PhD examination process varies across different countries and institutions. It can include a written thesis defense, an oral examination, or a combination. The specific format and requirements are built. It provides the evidence to identify societal issues, understand their root causes, and develop effective solutions. You can contribute to the knowledge base that informs policies and practices, leading to constructive social outcomes.

Certain skills and expertise are required to conduct research that drives social change. These include critical thinking, problem-solving, data analysis, and effective communication. As an adult learner pursuing an advanced degree, you have likely honed these skills through your education and professional experience. However, further developing and refining these skills is essential to conducting credible and impactful research.

Critical thinking is analyzing information objectively, evaluating arguments, and identifying biases. In the context of research, critical thinking helps you ask the right questions, consider alternative perspectives, and draw valid conclusions. You can ensure your research is rigorous and credible by applying critical thinking skills.

Is a DPhil a higher degree than a PhD?

Regarding advanced degrees, multiple terms can often be used interchangeably, leading to confusion among individuals seeking clarity. One common question that arises in the realm of academic qualifications is whether a DPhil is a higher degree than a PhD.

In short, a DPhil and a PhD are typically considered equivalent degrees. The term “DPhil” is primarily used in countries such as the United Kingdom and some European countries, while “PhD” is more commonly used in the United States and other parts of the world. Both degrees signify the highest level of academic achievement in a particular field of study.

The main difference between a DPhil and a PhD lies in the terminology and the historical context. The term “DPhil” is derived from the Latin phrase “Doctor Philosophiae,” which translates to “Doctor of Philosophy.” On the other hand, “PhD” stands for “Doctor of Philosophy” directly in English. Therefore, it can be said that a DPhil is essentially a regional variation of the PhD title.

In terms of the requirements and rigor of the degrees, they are typically identical. Both degrees require candidates to undertake original research, contribute new knowledge to their field, and produce a substantial thesis or dissertation. Earning either a DPhil or a PhD involves conducting extensive research, engaging in critical analysis, and demonstrating high academic expertise.

Are there any advantages to pursuing a DPhil over a PhD?

DPhil vs PhD: Weighing the Advantages

The decision between a DPhil and a PhD can be significant when pursuing an advanced degree. Both degrees are highly regarded and can lead to exciting opportunities for those seeking to make a positive impact through research. However, there are certain advantages that the DPhil offers over the traditional PhD. This blog post will explore some of these advantages and why they might appeal to adult learners seeking advanced degrees to lead social change through research.

1. In certain countries, such as the United Kingdom, the DPhil degree is equivalent to a PhD and is offered by prestigious universities like the University of Oxford. By pursuing a DPhil, you align yourself with a long and illustrious tradition of excellence in academia. This can increase recognition and respect within your field, opening doors to collaborations, publications, and career advancements.

2. The DPhil often allows more flexibility in defining your research focus than a PhD program. While a PhD program typically requires a narrower research question and a more specialized approach, a DPhil allows for a broader topic exploration. This can be particularly advantageous for those who wish to tackle complex, interdisciplinary research questions that require a more holistic perspective.

3. DPhil programs generally have a longer duration than PhD programs, typically four to seven years.

Are course requirements for a DPhil and a PhD the same?

When considering pursuing an advanced degree, DPhil vs PhD, it is essential to understand the differences in course requirements. While both degrees represent the highest level of academic achievement and require extensive research, some distinctions should be considered.

Generally, a DPhil (Doctor of Philosophy) and a PhD (Doctor of Philosophy) are similar in their academic rigor and commitment to original research. However, the specific course requirements can vary between institutions and even within different departments or disciplines. Thoroughly research and compare the requirements of each program you are considering. The good news is, the OGS Doctor of Philosophy can be interchangeably represented as a DPhil or PhD.

One key factor to consider is the structure of the program. A DPhil program tends to be more structured and often includes a set of taught courses that students must complete alongside their research. These courses provide a solid foundation in research methodologies and subject-specific knowledge. In contrast, a PhD program typically focuses more on independent research and original contributions to the field. While some PhD programs may also include coursework, the emphasis is generally on the dissertation or thesis.

Another aspect to consider is the duration of the program. DPhil programs often have a fixed duration, typically around 3-4 years, during which students are expected to complete their coursework, research, and submit their thesis. On the other hand, PhD programs can have a more flexible timeline, with students having the opportunity to complete their research and thesis at their own pace, within a given time frame.

Frequently Asked Questions (FAQ)

Here are some commonly asked questions about DPhil and PhD degrees and brief answers to help you understand the key differences and considerations.

What is the difference between a DPhil and a PhD?

A DPhil and a PhD are advanced degrees requiring extensive research, but the main difference lies in their regional recognition and focus. DPhil is primarily used in the United Kingdom, while PhD is more widely recognized internationally.

Why Does OGS use DPhil/PhD Interchangeably?

Because the teaching method at OGS is based on the British tutorial system, it has used the DPhil nomenclature since its founding. However, because the degree is authorized in Tennessee, USA, it is a “Doctor of Philosophy” degree and PhD/DPhil can be used interchangeably. It is entirely up to the graduate as to how they prefer to list their postnominal (“after their name”) degree.

What are the pros of pursuing a DPhil?

Pros of pursuing a DPhil include a more specialized focus, longer research duration allowing for in-depth exploration, and the prestige associated with this qualification.

What are the pros of pursuing a PhD?

Pros of pursuing a PhD include its international recognition, diverse research opportunities, and a broader range of career options available upon completion.

Are there any cons to pursuing a DPhil?

Cons of pursuing a DPhil include limited recognition outside of the UK, longer duration that may delay entry into the job market, and potentially limited funding opportunities.

Are there any cons to pursuing a PhD?

Cons of pursuing a PhD include potentially lacking the same level of specialization as a DPhil, shorter research duration, and the need for additional effort to establish international recognition.

Which degree should I choose: DPhil vs PhD?

Choosing between a DPhil and a PhD depends on your career goals, preferred research focus, and desired international recognition. It is important to carefully consider these factors and consult with academic advisors to make an informed decision.

Request Admissions Information

Privacy Policy

Do you have more questions?  Our admissions team is standing by to answer any questions you may have.

 

  Questions?  Contact Admissions!