Ass. Prof. Dr. Mogeeb Mosleh RESEARCH METHODOLOGY Ass. Prof. Dr. Mogeeb Mosleh

Data analysis and interpretation Think about analysis EARLY Start with a plan Code, enter, clean Analyze Interpret Reflect What did we learn? What conclusions can we draw? What are our recommendations? What are the limitations of our analysis?

Why do I need an analysis plan? To make sure the questions and your data collection instrument will get the information you want. To align your desired “report” with the results of analysis and interpretation. To improve reliability--consistent measures over time.

Key components of a data analysis plan Purpose of the evaluation Questions What you hope to learn from the question Analysis technique How data will be presented

Quantitative Data Analysis Ass. Prof. Dr. Mogeeb Mosleh

Analyzing and Interpreting Quantitative Data Quantitative Data is Presented in a numerical format Collected in a standardized manner e.g. surveys, closed-ended interviews, tests Analyzed using statistical techniques

True or False? Quantitative data we gather in Extension are more generalizable than qualitative data. Stating limitations weakens the evaluation

Analyzing Survey Data Do you want to report… how many people answered a, b, c, d? the average number or score? a change in score between two points in time? how people compared? how many people reached a certain level?

Common descriptive statistics Count (frequencies) Percentage Mean Mode Median Range Standard deviation Variance Ranking

Other Statistics Statistical Significance Factor Analysis Etc. Not often used in Extension program evaluation— generally require randomization, large samples, and/or control groups

Getting your data ready Assign a unique identifier Organize and keep all forms (questionnaires, interviews, testimonials) Check for completeness and accuracy Remove those that are incomplete or do not make sense

Getting the Data Ready for Analysis Data coding: assigning a number to the participants’ responses so they can be entered into a database. Data Entry: after responses have been coded, they can be entered into a database. Raw data can be entered through any software program (e.g., SPSS)

Editing Data An example of an illogical response is an outlier response. An outlier is an observation that is substantially different from the other observations. Inconsistent responses are responses that are not in harmony with other information. Illegal codes are values that are not specified in the coding instructions.

Transforming Data

Getting a Feel for the Data

Frequencies

Descriptive Statistics: Central Tendencies and Dispersions

Reliability Analysis

Qualitative Data Analysis Ass. Prof. Dr. Mogeeb Mosleh

Analyzing and Interpreting Qualitative Data Qualitative data is thick in detail and description. Data often in a narrative format Data often collected by observation, open-ended interviewing, document review Analysis often emphasizes understanding phenomena as they exist, not following pre- determined hypotheses

Qualitative Data Qualitative data: data in the form of words. Examples: interview notes, transcripts of focus groups, answers to open-ended questions, transcription of video recordings, accounts of experiences with a product on the internet, news articles, and the like.

Analysis of Qualitative Data The analysis of qualitative data is aimed at making valid inferences from the often overwhelming amount of collected data. Steps: data reduction data display drawing and verifying conclusions

Data Reduction Coding: the analytic process through which the qualitative data that you have gathered are reduced, rearranged, and integrated to form theory. Categorization: is the process of organizing, arranging, and classifying coding units.

Data Display Data display: taking your reduced data and displaying them in an organized, condensed manner. Examples: charts, matrices, diagrams, graphs, frequently mentioned phrases, and/or drawings.

Drawing Conclusions At this point where you answer your research questions by determining what identified themes stand for, by thinking about explanations for observed patterns and relationships, or by making contrasts and comparisons.

Reliability in Qualitative Research Category reliability “depends on the analyst’s ability to formulate categories and present to competent judges definitions of the categories so they will agree on which items of a certain population belong in a category and which do not.” (Kassarjian, 1977, p. 14). Interjudge reliability can be defined degree of consistency between coders processing the same data (Kassarjian 1977).

Validity in Qualitative Research Validity refers to the extent to which the qualitative research results: accurately represent the collected data (internal validity) can be generalized or transferred to other contexts or settings (external validity).