Observational protocol and qualitative documentations

As a researcher, you could be a non-participant to a full-on participant when observing your subjects in a study.  Thus, the observed/empathized behavioral and activities of individuals in the study are jotted down in field notes (Creswell, 2013).  Most researchers use an observational protocol to jotting down these notes as they observe their subjects.  According to Creswell (2013), this protocol could consist of: “separate descriptive notes (portraits of the participants, a reconstruction of dialogue, a description of the physical setting, accounts of particular events, or activities) [to] reflective notes (the researcher’s personal thoughts, such as “speculation, feelings, problems, ideas, hunches, impressions, and prejudices), … this form might [have] demographic information about the time, place, and date of the field setting where the observation takes place.”

Whereas, observational work can be combined with in-depth interviewing, and sometimes the observational work (which can be an everyday activity) can help prepare the researcher for the interviews (Rubin, 2012).  Doing so can increase the quality of the interviews because the interviewers know what the researcher has seen or read and can provide more information on those materials.  This can also allow the researcher to master the terminology before entering the interview. Finally, Rubin (2012) also states that cultural norms become more visible through observation rather than just a pure in-depth interview.

In Creswell (2013), Qualitative Documents are information contained within documents that could help a researcher out in their study that could be either public (newspapers, meeting minutes, official reports) and/or private (personal journals/diaries, letters, emails, internal manuals, written procedures, etc.) documents.  This can also include pictures, videos, educational materials, books, files. Whereas, Artifact Analysis is the analysis of the written text, usually are charts, flow sheets, intake forms, reports, etc.

The main analysis approach of this document would be to read the document to gain a subject matter understanding.  Document analysis would aid in quickly grouping, sorting and resort the data obtained for a study.  This manual will not be included in the coded dataset, but will help provide appropriate codes/categories for the interview analysis, in other words give me suggestions about what might be related to what.   Finally, one way to interpret this document would be for triangulation of data (data from multiple sources that are highly correlated) between the observation, interviews and this document.   


Organizational research & Participant Observer

For organizational research, some of their major goals for research are to examine their formation, recruitment of talent, adaption to constraints, types and causes, factors for growth, change and demise, which all fall under ethnographic studies (Lofland, 2005).  Ethnographic studies lend themselves much more nicely to participant-observers.

Participant observer is where the researcher/observer is not just only watching their subjects, but also actively participates (joins in) with their subject. The level of participation of the observer might impact what is observed (the more participation the harder it is to observe and take notes), thus low-key role participation is preferred.  Participating before the interviews will allow the observer to be sensitive to important issues otherwise missed. It is a more in-depth version of interviewing building on a regular conversation.  Participation may occur after watching for a while, focusing on a specific topic/question. (Rubin, 2012)


Data Analysis of Qualitative data

Each of the methods has at its core a thematic analysis of data, which is methodically and categorically linking data, phrases, sentences, paragraphs, etc. into a particular theme.  Coring up these themes by their thematic properties helps in understanding the data and developing meaningful themes aiding in building a conclusion to the central question.

Ethnographic Content Analysis (Herron, 2015):  Thick descriptions (collection of field notes that describe and recorded learning and a collection of perceptions of the researcher) help in the creation of cultural themes (themes related to behaviors on an underlying action) from which information was interpreted.

Phenomenological data analysis (Kerns, 2014): Connections among different classes of data through a thematic analysis were used for which results could be derived from.

Case study analysis (Hartsock, 2014): Through the organization of data within a specific case design and treating each distinct data set as a case study, one could derive some general themes within each individual case.  Once, all these general themes are identified, we should look for some cross-case themes.

Grounded Theory Data Analysis (Falciani-White, 2013): Code data through comparing incidents/data to a category (by breaking down, analyzing, comparing, labeling and categorizing data into meaningful units of data), and integrating categories by their properties, in order to help you identify a few themes in order to drive a theory in a systematic manner.


Interviewing strategy and qualitative sampling

As an interviewing strategy, open-ended questions leave the responses open to participant experience and categories and don’t close down the discussion or allow the participant to answer the question in one word (Snow et al, 2005).  Though in the past it was rejected because it did not involve a precise measurement, sometimes data that may not be easily measurable or counted, have value because of its intrinsic complexity and showcase of the “conditional nature of reality” (Rubin, 2012).  A whole field of text-analytics is aiming to prove that this data, considered as unstructured data, is an important part of knowledge discovery and knowledge sharing. Thus, Rubin (2012) says that open-ended questions grant the participant the chance to respond to the question in any way they choose, as elaborated on a response, allow participants to raise issues that are important to them, or even raise new issues not thought of by the interviewer.  Creswell (2013), further states that the more open the questions the better because it will allow the interviewer to listen to what people say and how they say, which can allow the participants to share their own views.  Usually, there are a few open-ended questions.  Finally, open-ended questions are used primarily in qualitative studies, but a mixture of both close-ended and open-ended questions could be asked in mixed methods studies.

One thing is to have the right questions as part of your interviewing strategies, it is another thing to have the right qualitative sampling plan.

Sampling Plans {purposeful/judgmental sampling, maximum variation sampling, sampling extreme or deviant cases, theoretical sampling, snowball/chain-referral sampling, cluster sampling, single-stage sampling, random sampling} (Creswell, 2013, Rubin, 2012, & Lofland et al, 2005). Here are just three of the many sampling plans listed in the sampling plan space.

  • Purposeful/judgmental sampling: In order to learn about a selective character, group, or category or their variations, you group the population into different characters, groups, or categories to collect data from with the participants now representing those divisions. (Creswell, 2013 & Lofland et al, 2005)
  • Maximum variation sampling: Allows for an analysis of error and bias in a phenomenon, through sampling and discovering the widest range of diversity in the phenomena of interest. (Lofland et al, 2005)
  • Snowball/chain-referral sampling: Asking your initial set of contacts with characteristics X, if they can refer to you their network that has the same characteristics X that you are studying. This is a means to enlarge your sample size and break down barriers to the entrance of your future participant. (Lofland et al, 2005). Depending on the characteristic X, like domestic violence, sexual assault, etc., this technique may run into IRB issues (Rubin, 2012).  Rubin (2012), stated that the way to avoid IRB issues if you have the current participants contact the future participants on your behalf to participate in the interview process, but this can drastically reduce the number maximum number of participants you could have gotten.


Sample SQL sets: Querying Tables

Below is an ERD of a database supporting the basic revenue business cycle.


Important: Use only the information that is given in the request in creating your queries.

Queries Examples:

  1. Provide a list of the descriptions and list prices of all the products the company
Select ProdDesc Description, ListPrice Prices
     From Product
  1. What products does the company have at least 10 items in stock?
Select ProdDesc Description, QuantityOnHand AS 'Quantity Available'
     From Product
          Where QuantityOnHand >= 10
  1. Sort all of the product descriptions in alphabetical order.
Select ProdDesc Description
     From Product
          Order by ProdDesc
  1. What are the names of the customers who live in Oklahoma or Arizona?
Select LName + ', ' + FName AS Name, State
     From Customer
          Where State = 'OK'
          or State = 'AZ'
  1. How many items on hand does the company have for the following product ids: (1, 4, 9, 10, 13, 22)?
Select ProductID, ProdDesc Description, QuantityOnHand AS 'Quantity Available'
     From Product
          Where ProductID IN (1,4,9,10,13,22)
  1. Which customers made orders on August 23, 2008 (no redundancy in results)?
Select Distinct CustomerID, OrderDate
     From Orders
          Where OrderDate='August 23, 2008'
  1. Which customers have ordered printers (no redundancy in results)?
Select Distinct o.CustomerID, p.ProdDesc
     From Orderline ol, Product p, orders o
          Where o.OrderID = ol.OrderID
          and ol.ProductID = p.ProductID
          and ProdDesc='printer'

8. Table Creation

Create a table called BackOrder. BackOrder should have the following columns: BO_ID, DelayDesc, Date, and ProductID.

When you create BackOrder, you must specify a PK constraint for BO_ID and an FK constraint for ProductID. DelayDesc should be a VarChar data type and can be null. The date should be a date data type and cannot be null.

Create Table BackOrder
       BO_ID Numeric(4),
       DelayDesc Varchar(15),
       Date Datetime Not Null,
       ProductID Numeric(18) Not Null,
       Constraint BackOrder_hern5717_BO_ID_pk Primary Key (BO_ID),
       Constraint Product_productID_fk Foreign Key (ProductID) References product(ProductID)

Insert a few lines into the table, then delete the lines.

Insert into BackOrder (BO_ID, DelayDesc, Date, ProductID)
   values (1,'Oklahoma Constitution', '1/14/2014', 1),
    (2,'Utah Constitution', '12/23/2013', 1),
    (3,'New Mexico Constitution', '12/21/2013', 1)

Delete from BackOrder
       Where BO_ID = 2


Quant: Compelling topics

Most Compelling Topics

Field (2013) states that both quantitative and qualitative methods are complimentary at best, none competing approaches to solving the world’s problems. Although these methods are quite different from each other. Simply put, quantitative methods are utilized when the research contains variables that are numerical, and qualitative methods are utilized when the research contains variables that are based on language (Field, 2013).  Thus, central to quantitative research and methods is to understand the numerical, ordinal, or categorical dataset and what the data represents. This can be done through either descriptive statistics, where the researcher uses statistics to help describe a data set, or it can be done through inferential statistics, where conclusions can be drawn about the data set (Miller, n.d.).

Field (2013) and Schumacker (2014), defined central tendency as an all-encompassing term to help describe the “center of a frequency distribution” through the commonly used measures mean, median, and mode.  Outliers, missing values, and multiplication of a constant, and adding a constant are factors that affect the central tendency (Schumacker, 2014).  Besides just looking at one central tendency measure, researchers can also analyze the mean and median together to understand how skewed the data is and in which direction.  Heavily skewed distributions would heavily increase the distance between these two values, and if the mean less than the median the distribution is skewed negatively (Field, 2013).  To understand the distribution, better other measures like variance and standard deviations could be used.

Variance and standard deviations are considered as measures of dispersion, where the variance is considered as measures of average dispersion (Field, 2013; Schumacker, 2014).  Variance is a numerical value that describes how the observed data values are spread across the data distribution and how they differ from the mean on average (Huck, 2011; Field, 2013; Schumacker, 2014).  The smaller the variance indicates that the observed data values are close to the mean and vice versa (Field, 2013).

Rarely is every member of the population studied, and instead a sample from that population is randomly taken to represent that population for analysis in quantitative research (Gall, Gall, & Borg 2006). At the end of the day, the insights gained from this type of research should be impersonal, objective, and generalizable.  To generalize the results of the research the insights gained from a sample of data needs to use the correct mathematical procedures for using probabilities and information, statistical inference (Gall et al., 2006).  Gall et al. (2006), stated that statistical inference is what dictates the order of procedures, for instance, a hypothesis and a null hypothesis must be defined before a statistical significance level, which also has to be defined before calculating a z or t statistic value.  Essentially, a statistical inference allows for quantitative researchers to make inferences about a population.  A population, where researchers must remember where that data was generated and collected from during quantitative research process.

Most flaws in research methodology exist because the validity and reliability weren’t established (Gall et al., 2006). Thus, it is important to ensure a valid and reliable assessment instrument.  So, in using any existing survey as an assessment instrument, one should report the instrument’s: development, items, scales, reports on reliability, and reports on validity through past uses (Creswell, 2014; Joyner, 2012).  Permission must be secured for using any instrument and placed in the appendix (Joyner, 2012).  The validity of the assessment instrument is key to drawing meaningful and useful statistical inferences (Creswell, 2014).

Through sampling of a population and using a valid and reliable survey instrument for assessment, attitudes and opinions about a population could be correctly inferred from the sample (Creswell, 2014).  Sometimes, a survey instrument doesn’t fit those in the target group. Thus it would not produce valid nor reliable inferences for the targeted population. One must select a targeted population and determine the size of that stratified population (Creswell, 2014).

Parametric statistics, are inferential and based on random sampling from a distinct population, and that the sample data is making strict inferences about the population’s parameters, thus tests like t-tests, chi-square, f-tests (ANOVA) can be used (Huck, 2011; Schumacker, 2014).  Nonparametric statistics, “assumption-free tests”, is used for tests that are using ranked data like Mann-Whitney U-test, Wilcoxon Signed-Rank test, Kruskal-Wallis H-test, and chi-square (Field, 2013; Huck, 2011).

First, there is a need to define the types of data.  Continuous data is interval/ratio data, and categorical data is nominal/ordinal data.  Modified from Schumacker (2014) with data added from Huck (2011):

Statistic Dependent Variable Independent Variable
Analysis of Variance (ANOVA)
     One way Continuous Categorical
     Single Sample Continuous
     Independent groups Continuous Categorical
     Dependent (paired groups) Continuous Categorical
Chi-square Categorical Categorical
Mann-Whitney U-test Ordinal Ordinal
Wilcoxon Ordinal Ordinal
Kruskal-Wallis H-test Ordinal Ordinal

So, meaningful results get reported and their statistical significance, confidence intervals and effect sizes (Creswell, 2014). If the results from a statistical test have a low probability of occurring by chance (5% or 1% or less) then the statistical test is considered significant (Creswell, 2014; Field, 2014; Huck, 2011Statistical significance test can have the same effect yet result in different values (Field, 2014).  Statistical significance on large samples sizes can be affected by small differences and can show up as significant, while in smaller samples large differences may be deemed insignificant (Field, 2014).  Statistically significant results allow the researcher to reject a null hypothesis but do not test the importance of the observations made (Huck, 2011).  Huck (2011) stated two main factors that could influence whether or not a result is statistically significant is the quality of the research question and research design.

Huck (2011) suggested that after statistical significance is calculated and the research can either reject or fail to reject a null hypothesis, effect size analysis should be conducted.  The effect size allows researchers to measure objectively the magnitude or practical significance of the research findings through looking at the differential impact of the variables (Huck, 2011; Field, 2014).  Field (2014), defines one way of measuring the effect size is through Cohen’s d: d = (Avg(x1) – Avg(x2))/(standard deviation).  If d = 0.2 there is a small effect, d = 0.5 there is a moderate effect, and d = 0.8 or more there is a large effect (Field, 2014; Huck, 2011). Thus, this could be the reason why a statistical test could yield a statistically significant value, but further analysis with effect size could show that those statistically significant results do not explain much of what is happening in the total relationship.

In regression analysis, it should be possible to predict the dependent variable based on the independent variables, depending on two factors: (1) that the productivity assessment tool is valid and reliable (Creswell, 2014) and (2) we have a large enough sample size to conduct our analysis and be able to draw statistical inference of the population based on the sample data which has been collected (Huck, 2011). Assuming these two conditions are met, then regression analysis could be made on the data to create a prediction formula. Regression formulas are useful for summarizing the relationship between the variables in question (Huck, 2011).

When modeling predict the dependent variable based upon the independent variable the regression model with the strongest correlation will be used as it is that regression formula that explains the variance between the variables the best.   However, just because the regression formula can predict some or most of the variance between the variables, it will never imply causation (Field, 2013).  Correlations help define the strength of the regression formula in defining the relationships between the variables, and can vary in value from -1 to +1.  The closer the correlation coefficient is to -1 or +1; it informs the researcher that the regression formula is a good predictor of the variance between the variables.  The closer the correlation coefficient is to zero, indicates that there is hardly any relationship between the variable (Field, 2013; Huck, 2011; Schumacker, 2014).  It should never be forgotten that correlation doesn’t imply causation, but can help determine the percentage of the variances between the variables by the regression formula result, when the correlation value is squared (r2) (Field, 2013).



  • Creswell, J. W. (2014) Research design: Qualitative, quantitative and mixed method approaches (4th ed.). California, SAGE Publications, Inc. VitalBook file.
  • Field, A. (2013) Discovering Statistics Using IBM SPSS Statistics (4th ed.). UK: Sage Publications Ltd. VitalBook file.
  • Gall, M. D., Gall, J., & Borg W. (2006). Educational research: An introduction (8th ed.). Pearson Learning Solutions. VitalBook file.
  • Huck, S. W. (2011) Reading Statistics and Research (6th ed.). Pearson Learning Solutions. VitalBook file.
  • Joyner, R. L. (2012) Writing the Winning Thesis or Dissertation: A Step-by-Step Guide (3rd ed.). Corwin. VitalBook file.
  • Miller, R. (n.d.). Week 1: Central tendency [Video file]. Retrieved from http://breeze.careeredonline.com/p9fynztexn6/?launcher=false&fcsContent=true&pbMode=normal
  • Schumacker, R. E. (2014) Learning statistics using R. California, SAGE Publications, Inc, VitalBook file.

Quant: In-depth Analysis in SPSS


This short analysis attempts to understand the marital happiness level on combined income.  It was found that marital happiness levels are depended on a couples’ combined income, but for the happiest couples, they were happy regardless how much money they had.  This, quantitative analysis on the sample data, has shown that when the happiness levels are low, there is a higher chance of lower levels of combined income.


Mulligan (1973), was one of the first that stated arguments about money was one of the top reasons for divorce between couples.  Factors for financial arguments could stem from: Goals and savings; record keeping; delaying tactics; apparel cost-cutting strategies; controlling expenditures; financial statements; do-it-yourself techniques; and cost cutting techniques (Lawrence, Thomasson, Wozniak, & Prawitz, 1993). Lawrence et al. (1993) exerts that financial arguments are common between families.  However, when does money no longer become an issue?  Does the increase in combined family income affect the marital happiness levels?  This analysis attempts to answer these questions.


Crosstabulation was conducted to get a descriptive exploration of the data.  Graphical images of box-plots helped show the spread and distribution of combined income per marital happiness.  In this analysis of the data the two alternative hypothesis will be tested:

  • There is a difference between the mean values of combined income per marital happiness levels.
  • There is a dependence between the combined income and marital happiness level

This would lead to finally analyzing the hypothesis introduced in the previous section, one-way analysis of variance and two-way chi-square test was conducted respectively.


Table 1: Case processing summary for analyzing happiness level versus family income.

u6db1f7Table 2: Crosstabulation for analyzing happiness level versus family income (<$21,250).

u6db1f3Table 3: Crosstabulation for analyzing happiness level versus family income for (>$21,250).

Table 4: Chi-square test for analyzing happiness level versus family income.


Table 5: Analysis of Variance for analyzing happiness level versus family income.



Figure 1: Boxplot diagram per happiness level of a marriage versus the family incomes.


Figure 2: Line diagram per happiness level of a marriage versus the mean of the family incomes.

Discussions and Conclusions

There are 1419 participants, and only 38.5% had responded to both their happiness of marriage and family income (Table 1).  What may have contributed to this huge unresponsive rate is that there could have been people who were not married, and thus making the happiness of marriage question not applicable to the participants.  Thus, it is suggested that in the future, there should be an N/A classification in this survey instrument, to see if we can have a higher response rate.  Given that there are still 547 responses, there is other information to be gained from analyzing this data.

As a family unit gains more income, their happiness level increases (Table 2-3).  This can be seen as the dollar value increases, the % within the family income and ranges recorded to midpoint for the very happy category increases as well from the 50% to the 75% level.    The unhappiest couples seem to be earning a combined medium amount of $7500-9000 and at $27500-45000.  Though for marriages that are pretty happy, it’s about stable at 30-40% of respondents at $13750 or more.

The mean values of family income to happiness (Figure 2), shows that on average, happier couples make more money together, but at a closer examination using boxplots (Figure 1), the happiest couples, seem to be happy regardless of how much money they make as the tails of the box plot extend really far from the median.  One interesting feature is that the spread of family combined income is shrinks as happiness decreases (Figure 1).  This could possibly suggest that though money is not a major factor for those couples that are happy, if the couple is unhappy it could be driven by lower combined incomes.

The two-tailed chi-squared test, shows statistical significance between family combined income and marital happiness allowing us to reject the null hypothesis #2, which stated that these two variables were independent of each other (Table 4).  Whereas the analysis of variance doesn’t allow for a rejection of the null hypothesis #1, which states the means are different between the groups of marital happiness level (Table 5).

There could be many reasons for this analysis, thus future work could include analyzing other variables that could help define other factors for marital happiness.  A possible multi-variate analysis may be necessary to see the impact on marital happiness as the dependent variable and combined income as one of many independent variables.



  FILE=’C:\Users\mkher\Desktop\SAV files\gss.sav’.



  /TABLES=hapmar BY incomdol





ONEWAY rincome BY hapmar


* Chart Builder.





  SOURCE: s=userSource(id(“graphdataset”))

  DATA: hapmar=col(source(s), name(“hapmar”), unit.category())

  DATA: incomdol=col(source(s), name(“incomdol”))

  DATA: id=col(source(s), name(“$CASENUM”), unit.category())

  GUIDE: axis(dim(1), label(“HAPPINESS OF MARRIAGE”))

  GUIDE: axis(dim(2), label(“Family income; ranges recoded to midpoints”))

  SCALE: cat(dim(1), include(“1”, “2”, “3”))

  SCALE: linear(dim(2), include(0))

  ELEMENT: schema(position(bin.quantile.letter(hapmar*incomdol)), label(id))


* Chart Builder.


  /GRAPHDATASET NAME=”graphdataset” VARIABLES=hapmar MEAN(incomdol)[name=”MEAN_incomdol”]




  SOURCE: s=userSource(id(“graphdataset”))

  DATA: hapmar=col(source(s), name(“hapmar”), unit.category())

  DATA: MEAN_incomdol=col(source(s), name(“MEAN_incomdol”))

  GUIDE: axis(dim(1), label(“HAPPINESS OF MARRIAGE”))

  GUIDE: axis(dim(2), label(“Mean Family income; ranges recoded to midpoints”))

  SCALE: cat(dim(1), include(“1”, “2”, “3”))

  SCALE: linear(dim(2), include(0))

  ELEMENT: line(position(hapmar*MEAN_incomdol), missing.wings())



Appreciative Inquiry

Creswell (2014), stated that inquiry procedures come in three flavors: quantitative, qualitative, mix.  Mostly under the inquiry procedures of quantitative methodologies, action research is a style of participatory research (Creswell, 2014).  It is the action research methodology that Appreciative Inquiry began (Holmber & Reed, 2010).  Appreciative Inquiry, usually asks what went well, or what was done well rather than what went wrong (Hammond, 2006).  Appreciative Inquiring works on a psychological concept of “Framing”.  Especially, since words not only have definitions but can also have an emotional connotation (Hammond, 2006).  Words with these emotional connotations can influence people.  Therefore, people’s perceptions and preferences change based on how a question or statement is framed (Prentice, 2007). According to appreciative inquiry, it is easier to sell an idea where the focus is on the positive aspect of that idea.  It is usually better to say that a bag of dried plantain chips is 95% fat-free, rather than 5% fat (Hammond, 2006; Prentice, 2007). How statements or questions are worded, can change how people view/frame the issue.  But, the goal of appreciative inquiry is not just a matter of framing, but also finding out what has been done that works well and doing more of that (Hammond, 2006).

Even though Appreciative Inquiry is a popular model for organizational change, its lack of rigor in its assessment approaches can upset those that are more quantitative in nature.  Especially since quantitative methodologist use the quantitative measure to deductively reach a conclusion (Cresswell, 2014).  However, Appreciative Inquiry gives researchers a different perspective than what they are accustom to and more researchers are becoming inspired by this model (Holmber & Reed, 2006).  For instance, if questions in an assessment instrument were to remove words with negative connotations such as “dysfunction”, “co-dependent”, “stress”, “addition”, “depress”, etc., to words that are more neutral or positive connotations, it can reframe the issue (Brooksher & Brylow, 2014). Using words with negative connotations can trigger a person’s need for loss aversion.  Loss aversion suggests that people perceive pain at least two times more than pleasure, and always aim to mitigate the pain (Prentice, 2007).  Thus, wording assessment instruments and the results they generate should be heavily considered (Brookshear & Brylow, 2014).  This is especially the case when the goal of quantitative research is to remain impersonal and objective in their studies (Creswell, 2014).

Another idea from Appreciative Inquiry that quantitative methodologist could use is the fact that it tries to examine an idea from a different angle.  For instance, sometimes conducting a Fermi decomposition of an idea, which is when a researcher is trying to solve a big idea by breaking it up into smaller more tangible and quantitatively measurable set of solutions, is one way of viewing the idea from a different angle (Hubbard, 2010).   Also, asking “Why?” iteratively five times, as suggested in lean six sigma DMAIC (define, measure, analyze, implement, and control) process is another way to understand an idea better (iSixSigma, n.d.).  Thus, looking at ideas from different angles can help find the cause of an idea or an opportunity for improvement.


  • Brookshear, G. & Brylow, D. (2014). Computer Science: An Overview (12th ed.). Pearson Learning Solution. VitalBook file.
  • Creswell, J. W. (2014) Research design: Qualitative, quantitative and mixed method approaches (4th ed.). California, SAGE Publications, Inc. VitalBook file.
  • Hammond, S. (2006). The Thin Book of Appreciative Inquiry. Thin Book Publishing.
  • Holmber, L. & Reed, J. (2010). AI research ntoes. AI Practitioners, 12(4), 55-57. Retrieved from https://www.academia.edu/362704/A_quantitative_approach_to_AI-research
  • Hubbard, D. W. (2010). How to measure anything: Finding the values of “intangibles” in business. (2nd e.d.) New Jersey, John Wiley & Sons, Inc.
  • iSixSigma (n.d.).  Determine the root cause: 5 whys. Retrieved from https://www.isixsigma.com/tools-templates/cause-effect/determine-root-cause-5-whys/
  • Prentice, R. A. (2007). Ethical decision making: More needed than good intentions. Financial Analysis Journal, 63(6), 17–30.

Differences between Quantitative and Qualitative Intros and Lit Reviews

Simply put, quantitative methods are utilized when the research contains variables that are numerical, and qualitative methods are utilized when the research contains variables that are based on language (Field, 2013).  Thus, each methods goals and procedures are quite different. This difference in goals and procedures drives differences in how a research paper’s introduction and literature review are written.

Introductions in a research paper allow the researcher to announce the problem and why it is important enough to be explored through a study.  Given that qualitative research may not have any known variables or theories, the introductions tend to vary tremendously (Creswell, 2014; Edmondson & MacManus, 2007).  Creswell (2014), suggested that qualitative methods introductions can begin with a quote from one the participants; stating the researchers’ personal story from a first person or third person viewpoint, or can be written in an inductive style.  There is less variation in quantitative methods introductions because the best way to introduce the problem is to introduce the variables, from an impersonal viewpoint (Creswell, 2014).  It is through gaining further understanding of these variables’ influence on a particular outcome is what’s driving the study in the first place.

The purpose of the literature review is for the researcher to share the results of other studies tangential to theirs to show how their study relates to the bigger picture and what gaps in the knowledge they are trying to solve (Creswell, 2014).  Edmondson and MacManus (2007) stated that when the nature of the field of research is nascent, the study becomes exploratory and qualitative in nature.  Given their exploratory nature, in qualitative methods, the researchers write their literature review in the form that is exploratory and in an inductive manner (Creswell, 2014).  Edmondson and MacManus (2007) stated that when the nature of the research is mature, there are plenty of related and existing research studies on the topic, a more quantitative approach is more appropriate.  Given that there is a huge body of knowledge to draw from when it comes to quantitative methods, the researchers tend to have substantially large amounts of literature at the beginning and structure it in a deductive fashion (Creswell, 2014).  Framing the literature review in a deductive manner allows the researcher at the end of the literature review to state clearly and measurably their research question(s) and hypotheses (Creswell, 2014; Miller, n.d.).

To conclude, understanding which methodological approach best fits a research study can help drive how the introduction and literature review sections are crafted and written.


  • Creswell, J. W. (2014) Research design: Qualitative, quantitative and mixed method approaches (4th ed.). California, SAGE Publications, Inc. VitalBook file.
  • Edmondson, A. C., & McManus, S. E. (2007). Methodological fit in management field research. Academy of Management Review, 32(4), 1155–1179. http://doi.org/10.5465/AMR.2007.26586086
  • Field, A. (2013) Discovering Statistics Using IBM SPSS Statistics (4th ed.). UK: Sage Publications Ltd. VitalBook file.

Quant: Chi-Square Test in SPSS

Introduction The aim of this analysis is to determine the association strength for the variables agecat and degree as well the major contributing cells through a chi-square analysis. Through the use of standardized residuals, it should aid in determining the cell contributions. Hypothesis Null: There is no basis of difference between the agecat and degree … Continue reading “Quant: Chi-Square Test in SPSS”


The aim of this analysis is to determine the association strength for the variables agecat and degree as well the major contributing cells through a chi-square analysis. Through the use of standardized residuals, it should aid in determining the cell contributions.


  • Null: There is no basis of difference between the agecat and degree
  • Alternative: There is are real differences between the agecat and degree


For this project, the gss.sav file is loaded into SPSS (GSS, n.d.).  The goal is to look at the relationships between the following variables: agecat (Age category) and degree (Respondent’s highest degree).

To conduct a chi-square analysis, navigate through Analyze > Descriptive Statistics > Crosstabs.

The variable degree was placed in the “Row(s)” box and agecat was placed under “Column(s)” box.  Select “Statistics” button and select “Chi-square” and under the “Nominal” section select “Lambda”. Select the “Cells” button and select “Standardized” under the “Residuals” section. The procedures for this analysis are provided in video tutorial form by Miller (n.d.).  The following output were observed in the next four tables.


Table 1: Case processing summary.

Valid Missing Total
N Percent N Percent N Percent
Degree * Age category 1411 99.4% 8 0.6% 1419 100.0%

From the total sample size of 1419 participants, 8 cases are reported to be missing, yielding a 99.4% response rate (Table 1).   Examining the cross tabulation, for the age groups 30-39, 40-49, 50-59, and 60-89 the standardized residual is far less than -1.96 or far greater than +1.96 respectively.  Thus, the frequencies between these two differ significantly.  Finally, for the 60-89 age group the standardized residual is less than -1.96, making these two frequencies differ significantly.  Thus, for all these frequencies, SPSS identified that the observed frequencies are far apart from the expected frequencies (Miller, n.d.).  For those significant standardized residuals that are negative is pointing out that the SPSS model is over predicting people of that age group with that respective diploma (or lack thereof).  For those significant standardized residuals that are positive is point out that the SPSS model is under-predicting people of that age group with a lack of a diploma.

Table 2: Degree by Age category crosstabulation.

Age category Total
18-29 30-39 40-49 50-59 60-89
Degree Less than high school Count 42 33 36 20 112 243
Standardized Residual -.1 -2.8 -2.3 -2.7 7.1
High school Count 138 162 154 113 158 725
Standardized Residual .9 .2 -.2 .4 -1.2
Junior college or more Count 68 115 114 78 68 443
Standardized Residual -1.1 1.8 1.9 1.4 -3.7
Total Count 248 310 304 211 338 1411

Deriving the degrees of freedom from Table 2, df = (5-1)*(3-1) is 8.  However, none of the expected counts were less than five because the minimum expected count is 36.3 (Table 3) which is desirable.  The chi-squared value is 96.364 and is significance at the 0.05 level. Thus, the null hypothesis is rejected, and there is a statistically significant association between a person’s age category and diploma level.  This test doesn’t tell us anything about the directionality of the relationship.

Table 3: Chi-Square Tests

Value df Asymptotic Significance (2-sided)
Pearson Chi-Square 96.364a 8 .000
Likelihood Ratio 90.580 8 .000
Linear-by-Linear Association 23.082 1 .000
N of Valid Cases 1411
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 36.34.

Table 4: Directional Measures

Value Asymptotic Standard Errora Approximate Tb Approximate Significance
Nominal by Nominal Lambda Symmetric .029 .013 2.278 .023
Degree Dependent .000 .000 .c .c
Age category Dependent .048 .020 2.278 .023
Goodman and Kruskal tau Degree Dependent .024 .005 .000d
Age category Dependent .019 .004 .000d
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Cannot be computed because the asymptotic standard error equals zero.
d. Based on chi-square approximation

Since there is a statistically significant association between a person’s age category and diploma level, the chi-square test doesn’t show how much these variables are related to each other. The lambda value (when we reject the null hypothesis) is 0.029; there is a 2.9% relationship between the two variables. Thus the relationship has a very weak effect (Table 4). Thus, 2.9% of the variance is accounted for, and there is nothing going on in here.


There is a statistically significant association between a person’s age category and diploma level.  According to the crosstabulation, the SPSS model is significantly over-predicting the number of people with less education than a high school diploma for the age groups of 20-59 as well as those with a college degree for the 60-89 age group.  This difference in the standard residual helped drive a large and statistically significant chi-square value. With a lambda of 0.029, it shows that 2.9% of the variance is accounted for, and there is nothing going on in here.



  /TABLES=ndegree BY agecat