Results chapter conducting, interpreting, and writing

Quantitative and Qualitative Results Chapter 4:

Conducting, Interpreting, and Writing

by Dr. James Lani

Results Chapter and Threading a Needle

Threading a Needle Conducting a Results Chapter

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Today’s Webinar• Quantitative Results • Quantitative Results Strategy• Data cleaning and Screening • Descriptive statistics• Conducting common analyses and

reporting findings• Qualitative Results• Qualitative Analysis Overview • Grounded theory: Open, Axial,

Selective coding• Phenomenological: Lived experiences

• Q & A


Quantitative Results

Quantitative Results Strategy


• Select the correct analysis (RQ and level of measurement)

• Clean your data• Describe variables• Conduct the analyses/assess

assumptions • Present the findings• Summarize the findings

Data Cleaning and Preparation


• Assess data for outliers (±3.29);• Multiple imputation for missing

data;• Create composite score (with

reverse coding if necessary);• Conduct Cronbach’s alpha (α)

Descriptive Statistics


Means and Standard Deviations

Frequency and Percentages

Variables n %

Location

Urban 72 48.0Rural 78 52.0

Ethnicity

White 36 24.0 Hispanic 43 28.7

Other 71 47.3

Table 1Frequencies and Percentages for Nominal Variables

Conducting Frequently Used Statistical

Analyses


o Chi-square: Goodness of fit and Test of independence

o Pearson correlationo Independent samples t-testo One way ANOVAo Dependent samples t-testo Repeated-measures ANOVAo Linear regressiono Multiple, Logistic, Ordinal, Multinominal

regressiono Mediation and Moderation

Chi-square: Goodness of Fit and

Test of Independence


• Chi-square analysis answers what research question?

• Assumptions of analysis:o Each cell has count of 1; o 80% of cells have an expected

value of 5.• Conducting analysis;• Presenting findings;

o Write-up in narrative;o Tables and figures.

Chi-Square: Goodness of Fitand Test of Independence


Republican

Democrat Green Independent

Libertarian

x x x x x

Republican

Democrat

Green Indep. Libert.

Male x x x x x

Female

x x x x x

Pearson Correlation


• Correlation analysis answers what research question?

• Assumptions of analysisLinearity, homoscedacity,

normality • Conducting analysis (e.g., IQ and

Creativity)• Presenting findings

Write-up in narrativeTables and figures

Pearson Correlation Assumptions


Linearity Non-linear



HomoscedasticityMet

Hetroscedasticity



Normal Non-Normal

Independent Samples t-test


Independent t-test analysis answers what research question?

Assumptions of analysis (IQ by gender)

Homogeneity of varianceNormality

Conducting analysis

Presenting findingsWrite-up in narrativeTables and figures

Males Females

Part 1=1 Part 4=2

Part 2=2 Part 5=3

Part 3=3 Part 6=4

X=2 X=3

Males Females

Part 1=1.9 Part 4=2.9

Part 2=2.0 Part 5=3.0

Part 3=2.1 Part 6=3.1

X=2 X=3

One-Way ANOVA


ANOVA answers what research question?

Assumptions of analysisHomogeneity of varianceNormality

Conducting analysis


Repub Dem Indep

Part 1=1.9

Part 4=2.9

Part 2=2.0

Part 5=3.0

Part 3=2.1

Part 6=3.1

X=2 X=3 X=

Dependent Samples t-test


Dependent sample t-test analysis answers what research question?

Assumptions of analysisNormality

Conducting analysis


Science Pretest

Science Posttest

Part 1=1 Part 1=2

Part 2=2 Part 2=3

Part 3=3 Part 3=4

X=2 X=3

Science Pretest

Science Posttest

Part 1=1.9 Part 1=2.9

Part 2=2.0 Part 2=3.0

Part 3=2.1 Part 3=3.1

X=2 X=3

Repeated-Measures ANOVA


Repeated-measures ANOVA answers what research question?

Conducting analysisSphericity

(homogeneity of variance)


Science Pretest

Science Posttest

Science Follow-

up

Part 1=1.9

Part 1=2.9

Part 1=

Part 2=2.0

Part 2=3.0

Part 2=

Part 3=2.1

Part 3=3.1

Part 3=

X=2 X=3 X=

Linear Regression


Linear regression analysis answers what research question? (e.g., Does IQ predict Creativity)

Assumptions of analysisNormality, Multicollineality,

Homoscedasticity(residual vs. predicted values)

Conducting analysis


DVIV

Regressions: Multiple, Logistic,


Ordinal, Multinominal

Its all about the level of measurement of the DV

Mediation Analysis


Mediation analyses answers what kind of research questions?

Assumptions of analysisAssumptions of regression

Conducting analysis


Mediation Analysis Example


IQ (IV) Creativity (DV)

Education (M)

3 Regression Equations

IVM; must be significantIVDV; must be significantM, IV DV; IV is no longer significant

Moderation Analysis


Moderation analyses answers what kind of research questions?

Assumptions of analysisAssumptions of regression

Conducting analysis


Moderation Analysis


IQ (IV)

CreativityAge (Mod)IQ x Age

InteractionRegression with 2 blocks

Step 1: IQ and Age enteredStep 2: Interaction term entered

Moderation is supported if interaction is significant.

Note. To avoid multicollinearity, center IV/Mod (subtract mean), then create the interaction term.

Qualitative Results

Qualitative Analysis Overview


• Sample size: no definitive requirement

• Grounded theory: Open, Axial, Selective coding

(~20-30 participants)

• Phenomenological: Lived experiences

• (~5-25 participants)

• Inter-rater reliability: Kappa coefficient

Data Preparation


• Transcribe interviews

• Line number transcripts

• If thematizing without software, organize

interviews by interview question rather than

interviewee;

• If you are using NVIVO software,

• Sources: Separate document by each

interviewee;

• Node: Separated for each interview

question, themes, and subthemes.

Grounded Theory Example:Effectiveness of Block Scheduling


Research Question 1: During the first year of block scheduling, how did students perceive the effectiveness of the new class schedule?

Interview Question 1. “During the first year of block scheduling, how did you feel about your class load?”

Open coding revealed two sets of theme related information based on the level of difficulty and amount of information taught. (1) The majority of participants cited the level of difficulty associated with block scheduling. Level of difficulty was related to overall course load and class schedules. (2) The amount of information taught in the classes was also mentioned by the students.

Qualitative Analysis: Grounded Theory

Part. Them

e

Phrases/descriptive words

Level of

difficulty

2Initially it was very frustrating juggling so many classes, but eventually it got easier

3

In the beginning the classes were overwhelming, and there were problems, but eventually I was able to manage

4

I think I had about the same amount of homework as before, so my classes are pretty easy

5

It was easy and having a staggered schedule helped

6

The tests were a lot harder because we were able to cover more information in a shorter amount of time, and we were tested on all of it

8

In the beginning it was hard, but for the second half of the year I had figured out how to manipulate my schedule so I had an even work load most of the week

10

It was fine, pretty easy transition

Table 1: Effectiveness Interview Question 1 Open Coding Responses



Axial coding for feelings about the class load revealed the following thematic relationships (in order from strongest to weakest associations):

Four of the ten (40.0%) specified more information was taught/covered (e.g., covered chapters a lot faster, not used to learning this much, more information in a shorter amount of time).

Three of the ten (30.0%) specified the year started out difficult but got easier (e.g., frustrating but got easier, overwhelming but eventually I could manage, and hard but eventually I figured it out).

Three of the ten (30%) specified the class load was pretty easy (e.g., classes are easy, easy transition, and having a staggered schedule made it easier).



Selective coding of interview question 1, feelings about the class load, generated a composite response of block scheduling was easy once an understanding was gained (e.g., frustrating but got easier, overwhelming but eventually I could manage, fine, pretty easy transition, and hard but eventually I figured it out), followed by the theme of more information was taught (e.g., covered chapters a lot faster, not used to learning this much, more information in a shorter amount of time).

The researcher selected one participant’s statement that reflected the axial coding composite with the most congruence. Participant 3 stated, “In the beginning the classes were overwhelming, and there were problems, but eventually I was able to manage. Having the block schedule allows me to have a study period, so even though the tests cover more information now I can be prepared because I have more time to study.”

Qualitative Analysis: Phenomenological


• Read entire set of transcripts to get a

feel for what is being said;

• Read across each interview question

for all participants, pulling themes

out of passages and then quoting

(excerpts)

Qualitative Analysis: Kappa Coefficient


Rater 1

Rater 2 Present Absent Subtotal

Present A B A+B

Absent C D C+D

Subtotal A+C B+D A+B+C+D

Observed agreement = (A + D)

Expected Agreement = (((A + B) * (A + C)) + ((C + D) * (B + D))) / (A + B + C + D)

Kappa = ((observed agreement) – (expected agreement)) / ((A + B + C + D) – (expected agreement)) Note: A, B, C, and D are the frequencies in which a theme is identified in same excerpt between rater 1 and rater 2.

For Theme

1

Qualitative Analysis References


Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage Publications.

Glaser, B. G. & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Piscataway, New Jersey: Transaction.

Morse, J. M. (1994). Designing funded qualitative research. In Denizin, N. K. & Lincoln, Y. S., Handbook of qualitative research (2nd Ed). Thousand Oaks, CA: Sage.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd Ed.). Thousand Oaks, CA: Sage.

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