Introduction

This report aims to provide a detailed analysis of frequency and percentage tables, which were constructed based on survey data. These tables reflect participant distribution by home state, major, and self-reported computer skills, both before and after an educational intervention.

The following sections will describe the methods used, findings based on the provided data tables, and the implications of these findings.

Methods

The data analyzed in this report was drawn from a survey of 20 respondents. The survey collected demographic information such as home state and academic major, as well as self- reported levels of computer skills both before and after an educational intervention. Frequency counts and percentages were calculated for each variable to assess the distribution of responses across categories.

Findings

The data is summarized in four tables, each representing different aspects of the survey. The first objective I identified was the state in which the participants came from. The home state frequency and percentage table represent the distribution of respondents by their home state. Of

the 20 respondents, the majority (85%) were from Alabama, while smaller proportions were from Georgia (10%) and Tennessee (5%) (see Table 1). The dominance of Alabama in this survey

sample indicates that the survey likely targeted individuals with a strong regional bias, possibly reflecting the focus of the study.

Table 1

Home State Frequency and Percentage

Home State	f	%
TN	1	5
GA	2	10
AL	17	85
Total	20	100

The next objective I identified with the survey dealt with the majors of each participant.

Participants were asked to report on their academic major. The largest group of respondents (30%) were enrolled in Agricultural Leadership, Education, and Communications (ALEC), followed by Agricultural Economics (AGECON) (25%). Environmental science and wildlife majors each accounted for (15%), while smaller numbers studied animal science (5%), forestry (5%), and wood science (5%). The diverse range of majors suggests a broad interest in

agricultural-related fields, with a notable preference for ALEC and AGECON majors (see Table 2).

Table 2

Major Frequency and Percentage

Major	f	%
AGECON	5	25
ALEC	6	30
Animal Science	1	5
Environmental	3	15
Forestry	1	5
Wildlife	3	15
Wood Science	1	5
Total	20	100

The third objective assessed respondent’s rating their computer skills before undergoing an educational intervention. Most participants (60%) rated their skills as “average,’’ while (20%) rated themselves as “above average”. Only (5%) considered themselves “beginners” or

“experts”. This distribution shows that most respondents felt they had a reasonable grasp of computer skills before the intervention, with few extreme ratings (see Table 3).

Table 3

Computer Skills Before 1 Frequency and Percentage

Computer Skills Before 1	f	%
Above Average	4	20
Average	12	60
Beginner	1	5
Below Average	2	10
Expert	1	5
Total	20	100

The fourth objective identified from the survey was computer skills after an educational intervention. Post-intervention, the distribution of computer skill levels shifted slightly. More respondents rated their skills as “above average” (25%), but there was a significant increase in those who rated themselves “below average” (20%). The number of “average” ratings declined slightly to (55%). The increase in “below average” self-ratings may suggest a heightened awareness of the skills gap, possibly indicating that the intervention made participants more aware of their limitations (see Table 4).

Table 4

Computer Skills After 1 Frequency and Percentage

Computer Skills After 1	f	%
Above Average	5	25
Average	11	55
Below Average	4	20
Total	20	100

Implications

The findings from these tables offer valuable insights into the demographic composition of the survey sample and shifts in perceived computer skills following an intervention. The heavy representation from Alabama suggests that the findings may be regionally specific,

limiting the generalizability of the results. However, the diversity of majors indicates that agricultural education is drawing students from a variety of academic backgrounds, which bodes well for the interdisciplinary nature of the field.

The analysis of computer skills before and after the educational intervention is particularly interesting. While more participants rated their skills as “above average” post-

intervention, the increase in “below average” ratings could indicate that the intervention exposed areas where participants needed more development. This suggests the intervention was effective in raising awareness about the necessity of improving digital competencies.

Conclusion and Discussion

The survey data, analyzed in the frequency and percentage tables, provides a clear overview of participants’ demographic characteristics and shifts in their self-perceived computer skills. The implications of these findings highlight the need for continued focus on improving digital literacy in agricultural education programs, as well as the importance of understanding regional biases in survey samples. Further research could expand the sample size and include respondents from a wider range of regions to validate these findings on a broader scale.