Thesis Results
Sir Darryl
2022-11-28
New names:
• `` -> `...1`
• `PRETEST` -> `PRETEST...4`
• `POST TEST` -> `POST TEST...5`
• `PRETEST` -> `PRETEST...7`
• `POST TEST` -> `POST TEST...8`
• `PRETEST` -> `PRETEST...10`
• `POST TEST` -> `POST TEST...11`
• `PRETEST` -> `PRETEST...13`
• `POST TEST` -> `POST TEST...14`# A tibble: 48 × 17
...1 Students Type PRETE…¹ POST …² Rdiff PRETE…³ POST …⁴ InterR1 PRETE…⁵
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. ABARA, … GBA 2 2 0 3 4 1 4
2 2. AMPORIN… GBA 2 4 2 4 4 0 3
3 3. ANDAG, … GBA 3 3 0 4 5 1 3
4 4. ASPACIO… GBA 3 4 1 4 5 1 3
5 5. BAANG, … GBA 3 4 1 4 4 0 2
6 6. BACALSO… GBA 2 3 1 3 4 1 3
7 7. BALA, J… GBA 3 3 0 3 4 1 2
8 8. BALABAG… GBA 2 3 1 4 5 1 3
9 9. BALAGAS… GBA 2 2 0 3 4 1 2
10 10. BILLONE… GBA 2 2 0 3 4 1 4
# … with 38 more rows, 7 more variables: `POST TEST...11` <dbl>, InterR2 <dbl>,
# PRETEST...13 <dbl>, `POST TEST...14` <dbl>, InterR3 <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, and abbreviated variable names
# ¹PRETEST...4, ²`POST TEST...5`, ³PRETEST...7, ⁴`POST TEST...8`,
# ⁵PRETEST...10
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesNew names:
• `` -> `...1`
• `PRETEST` -> `PRETEST...4`
• `POST TEST` -> `POST TEST...5`
• `PRETEST` -> `PRETEST...7`
• `POST TEST` -> `POST TEST...8`
• `PRETEST` -> `PRETEST...10`
• `POST TEST` -> `POST TEST...11`
• `PRETEST` -> `PRETEST...13`
• `POST TEST` -> `POST TEST...14`# A tibble: 50 × 17
...1 Students Type PRETE…¹ POST …² Rdiff PRETE…³ POST …⁴ InterR1 PRETE…⁵
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. ATIENZA… PBA 2 2 0 3 4 1 4
2 2. BACONGU… PBA 1 3 2 3 4 1 3
3 3. COMAYAS… PBA NA NA 0 NA NA 0 NA
4 4. GONZALE… PBA 3 3 0 3 4 1 4
5 5. LAGROSA… PBA 3 3 0 4 5 1 5
6 6. LAUS, E… PBA 2 2 0 3 4 1 2
7 7. LIBERTA… PBA NA NA 0 NA NA 0 NA
8 8. LITANG,… PBA 2 2 0 3 4 1 3
9 9. LIZARDO… PBA 1 2 1 3 5 2 3
10 10. LLANITA… PBA 2 2 0 3 4 1 3
# … with 40 more rows, 7 more variables: `POST TEST...11` <dbl>, InterR2 <dbl>,
# PRETEST...13 <dbl>, `POST TEST...14` <dbl>, InterR3 <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, and abbreviated variable names
# ¹PRETEST...4, ²`POST TEST...5`, ³PRETEST...7, ⁴`POST TEST...8`,
# ⁵PRETEST...10
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names# A tibble: 98 × 17
...1 Students Type PRETE…¹ POST …² Rdiff PRETE…³ POST …⁴ InterR1 PRETE…⁵
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. ABARA, … GBA 2 2 0 3 4 1 4
2 2. AMPORIN… GBA 2 4 2 4 4 0 3
3 3. ANDAG, … GBA 3 3 0 4 5 1 3
4 4. ASPACIO… GBA 3 4 1 4 5 1 3
5 5. BAANG, … GBA 3 4 1 4 4 0 2
6 6. BACALSO… GBA 2 3 1 3 4 1 3
7 7. BALA, J… GBA 3 3 0 3 4 1 2
8 8. BALABAG… GBA 2 3 1 4 5 1 3
9 9. BALAGAS… GBA 2 2 0 3 4 1 2
10 10. BILLONE… GBA 2 2 0 3 4 1 4
# … with 88 more rows, 7 more variables: `POST TEST...11` <dbl>, InterR2 <dbl>,
# PRETEST...13 <dbl>, `POST TEST...14` <dbl>, InterR3 <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, and abbreviated variable names
# ¹PRETEST...4, ²`POST TEST...5`, ³PRETEST...7, ⁴`POST TEST...8`,
# ⁵PRETEST...10
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names# A tibble: 87 × 17
...1 Students Type PRETE…¹ POST …² Rdiff PRETE…³ POST …⁴ InterR1 PRETE…⁵
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. ABARA, … GBA 2 2 0 3 4 1 4
2 2. AMPORIN… GBA 2 4 2 4 4 0 3
3 3. ANDAG, … GBA 3 3 0 4 5 1 3
4 4. ASPACIO… GBA 3 4 1 4 5 1 3
5 5. BAANG, … GBA 3 4 1 4 4 0 2
6 6. BACALSO… GBA 2 3 1 3 4 1 3
7 7. BALA, J… GBA 3 3 0 3 4 1 2
8 8. BALABAG… GBA 2 3 1 4 5 1 3
9 9. BALAGAS… GBA 2 2 0 3 4 1 2
10 10. BILLONE… GBA 2 2 0 3 4 1 4
# … with 77 more rows, 7 more variables: `POST TEST...11` <dbl>, InterR2 <dbl>,
# PRETEST...13 <dbl>, `POST TEST...14` <dbl>, InterR3 <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, and abbreviated variable names
# ¹PRETEST...4, ²`POST TEST...5`, ³PRETEST...7, ⁴`POST TEST...8`,
# ⁵PRETEST...10
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesPretest Average Score
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union# A tibble: 2 × 4
Type count mean sd
<chr> <int> <dbl> <dbl>
1 GBA 46 2.86 0.600
2 PBA 41 2.99 0.541Posttest Average Score
# A tibble: 2 × 4
Type count mean sd
<chr> <int> <dbl> <dbl>
1 GBA 46 3.65 0.490
2 PBA 41 3.74 0.555# A tibble: 46 × 17
...1 Students Type PRETE…¹ POST …² Rdiff PRETE…³ POST …⁴ InterR1 PRETE…⁵
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. ABARA, … GBA 2 2 0 3 4 1 4
2 2. AMPORIN… GBA 2 4 2 4 4 0 3
3 3. ANDAG, … GBA 3 3 0 4 5 1 3
4 4. ASPACIO… GBA 3 4 1 4 5 1 3
5 5. BAANG, … GBA 3 4 1 4 4 0 2
6 6. BACALSO… GBA 2 3 1 3 4 1 3
7 7. BALA, J… GBA 3 3 0 3 4 1 2
8 8. BALABAG… GBA 2 3 1 4 5 1 3
9 9. BALAGAS… GBA 2 2 0 3 4 1 2
10 10. BILLONE… GBA 2 2 0 3 4 1 4
# … with 36 more rows, 7 more variables: `POST TEST...11` <dbl>, InterR2 <dbl>,
# PRETEST...13 <dbl>, `POST TEST...14` <dbl>, InterR3 <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, and abbreviated variable names
# ¹PRETEST...4, ²`POST TEST...5`, ³PRETEST...7, ⁴`POST TEST...8`,
# ⁵PRETEST...10
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.8 ✔ stringr 1.4.0
✔ tidyr 1.2.0 ✔ forcats 0.5.2
✔ readr 2.1.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
Attaching package: 'rstatix'
The following object is masked from 'package:stats':
filter# A tibble: 184 × 15
...1 Stude…¹ Type PRETE…² POST …³ PRETE…⁴ POST …⁵ PRETE…⁶ POST …⁷ PRETE…⁸
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. … ABARA,… GBA 2 2 3 4 4 5 2
2 2. … AMPORI… GBA 2 4 4 4 3 4 2
3 3. … ANDAG,… GBA 3 3 4 5 3 5 3
4 4. … ASPACI… GBA 3 4 4 5 3 4 3
5 5. … BAANG,… GBA 3 4 4 4 2 5 3
6 6. … BACALS… GBA 2 3 3 4 3 5 2
7 7. … BALA, … GBA 3 3 3 4 2 4 2
8 8. … BALABA… GBA 2 3 4 5 3 5 5
9 9. … BALAGA… GBA 2 2 3 4 2 3 3
10 10. BILLON… GBA 2 2 3 4 4 4 3
# … with 174 more rows, 5 more variables: `POST TEST...14` <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, Rating <fct>, Score <dbl>, and
# abbreviated variable names ¹Students, ²PRETEST...4, ³`POST TEST...5`,
# ⁴PRETEST...7, ⁵`POST TEST...8`, ⁶PRETEST...10, ⁷`POST TEST...11`,
# ⁸PRETEST...13
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesAverage increase of scores using GBA of all raters
# A tibble: 4 × 6
Type Rating variable n mean sd
<chr> <fct> <chr> <dbl> <dbl> <dbl>
1 GBA InterR1 Score 46 0.804 0.582
2 GBA InterR2 Score 46 1.04 1.23
3 GBA InterR3 Score 46 0.391 0.649
4 GBA Rdiff Score 46 0.913 0.812
# Test of Difference among Raters in terms of GBA
Loading required package: carData
Attaching package: 'car'The following object is masked from 'package:purrr':
someThe following object is masked from 'package:dplyr':
recode Df Sum Sq Mean Sq F value Pr(>F)
Rating 3 10.97 3.658 4.997 0.00237 **
Residuals 180 131.76 0.732
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1In the above results, it shows that there is a significant difference of rating among raters since the p-value (0.00237) is less than the 0.05 level of significance.
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Score ~ Rating, data = totalGBAdiff)
$Rating
diff lwr upr p adj
InterR2-InterR1 0.2391304 -0.22349315 0.70175402 0.5384772
InterR3-InterR1 -0.4130435 -0.87566706 0.04958011 0.0983541
Rdiff-InterR1 0.1086957 -0.35392793 0.57131924 0.9290277
InterR3-InterR2 -0.6521739 -1.11479750 -0.18955033 0.0018954
Rdiff-InterR2 -0.1304348 -0.59305837 0.33218880 0.8844826
Rdiff-InterR3 0.5217391 0.05911555 0.98436271 0.0201601As shown above, rater 3 contributes the significant difference of rating among the raters.
Withour Rater 3 in GBA
Excluding rater 3, it shows in the result below that p-value = 0.456 which means that the rating now among the remaining raters do not differ statistically.
# A tibble: 138 × 16
...1 Stude…¹ Type PRETE…² POST …³ PRETE…⁴ POST …⁵ PRETE…⁶ POST …⁷ PRETE…⁸
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. … ABARA,… GBA 2 2 3 4 4 5 2
2 2. … AMPORI… GBA 2 4 4 4 3 4 2
3 3. … ANDAG,… GBA 3 3 4 5 3 5 3
4 4. … ASPACI… GBA 3 4 4 5 3 4 3
5 5. … BAANG,… GBA 3 4 4 4 2 5 3
6 6. … BACALS… GBA 2 3 3 4 3 5 2
7 7. … BALA, … GBA 3 3 3 4 2 4 2
8 8. … BALABA… GBA 2 3 4 5 3 5 5
9 9. … BALAGA… GBA 2 2 3 4 2 3 3
10 10. BILLON… GBA 2 2 3 4 4 4 3
# … with 128 more rows, 6 more variables: `POST TEST...14` <dbl>,
# InterR3 <dbl>, PretestAve <dbl>, PosttestAve <dbl>, Rating <fct>,
# Score <dbl>, and abbreviated variable names ¹Students, ²PRETEST...4,
# ³`POST TEST...5`, ⁴PRETEST...7, ⁵`POST TEST...8`, ⁶PRETEST...10,
# ⁷`POST TEST...11`, ⁸PRETEST...13
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesAverage increase of scores using GBA excluding rater 3
# A tibble: 3 × 6
Type Rating variable n mean sd
<chr> <fct> <chr> <dbl> <dbl> <dbl>
1 GBA InterR1 Score 46 0.804 0.582
2 GBA InterR2 Score 46 1.04 1.23
3 GBA Rdiff Score 46 0.913 0.812
# Test of Difference among Raters in terms of GBA
Df Sum Sq Mean Sq F value Pr(>F)
Rating 2 1.32 0.6594 0.789 0.456
Residuals 135 112.80 0.8356 Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Score ~ Rating, data = totalGBAdiff)
$Rating
diff lwr upr p adj
InterR2-InterR1 0.2391304 -0.2125718 0.6908326 0.4233890
Rdiff-InterR1 0.1086957 -0.3430066 0.5603979 0.8361606
Rdiff-InterR2 -0.1304348 -0.5821370 0.3212674 0.7730295Test of Difference if Using GBA significantly improve students performance
One Sample t-test
data: totalGBAdiff$Score
t = 11.845, df = 137, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
0.7666554 1.0739243
sample estimates:
mean of x
0.9202899 In the result above using T-test for paired observations, it shows that using GBA significantly improve students’ performance with a p-value result of 0.00000000000000022.
For PBA
# A tibble: 41 × 17
...1 Students Type PRETE…¹ POST …² Rdiff PRETE…³ POST …⁴ InterR1 PRETE…⁵
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. ATIENZA… PBA 2 2 0 3 4 1 4
2 2. BACONGU… PBA 1 3 2 3 4 1 3
3 4. GONZALE… PBA 3 3 0 3 4 1 4
4 5. LAGROSA… PBA 3 3 0 4 5 1 5
5 6. LAUS, E… PBA 2 2 0 3 4 1 2
6 8. LITANG,… PBA 2 2 0 3 4 1 3
7 9. LIZARDO… PBA 1 2 1 3 5 2 3
8 10. LLANITA… PBA 2 2 0 3 4 1 3
9 11. LUMAPAC… PBA 2 2 0 3 5 2 3
10 12. LUZANA,… PBA 2 2 0 3 3 0 4
# … with 31 more rows, 7 more variables: `POST TEST...11` <dbl>, InterR2 <dbl>,
# PRETEST...13 <dbl>, `POST TEST...14` <dbl>, InterR3 <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, and abbreviated variable names
# ¹PRETEST...4, ²`POST TEST...5`, ³PRETEST...7, ⁴`POST TEST...8`,
# ⁵PRETEST...10
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names# A tibble: 164 × 15
...1 Stude…¹ Type PRETE…² POST …³ PRETE…⁴ POST …⁵ PRETE…⁶ POST …⁷ PRETE…⁸
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1. … ATIENZ… PBA 2 2 3 4 4 4 3
2 2. … BACONG… PBA 1 3 3 4 3 5 3
3 4. … GONZAL… PBA 3 3 3 4 4 5 5
4 5. … LAGROS… PBA 3 3 4 5 5 4 4
5 6. … LAUS, … PBA 2 2 3 4 2 3 3
6 8. … LITANG… PBA 2 2 3 4 3 3 3
7 9. … LIZARD… PBA 1 2 3 5 3 3 3
8 10. LLANIT… PBA 2 2 3 4 3 3 3
9 11. LUMAPA… PBA 2 2 3 5 3 5 3
10 12. LUZANA… PBA 2 2 3 3 4 4 3
# … with 154 more rows, 5 more variables: `POST TEST...14` <dbl>,
# PretestAve <dbl>, PosttestAve <dbl>, Rating <fct>, Score <dbl>, and
# abbreviated variable names ¹Students, ²PRETEST...4, ³`POST TEST...5`,
# ⁴PRETEST...7, ⁵`POST TEST...8`, ⁶PRETEST...10, ⁷`POST TEST...11`,
# ⁸PRETEST...13
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names# A tibble: 4 × 6
Type Rating variable n mean sd
<chr> <fct> <chr> <dbl> <dbl> <dbl>
1 PBA InterR1 Score 41 0.78 0.822
2 PBA InterR2 Score 41 0.78 1.15
3 PBA InterR3 Score 41 0.707 0.955
4 PBA Rdiff Score 41 0.732 0.708
# Test of Difference among Raters in terms of GBA
Df Sum Sq Mean Sq F value Pr(>F)
Rating 3 0.16 0.0549 0.064 0.979
Residuals 160 136.59 0.8537 It shows that the rating among the four (4) raters do not differ significantly with a p-value result of 0.979.
Test of Difference if Using PBA significantly improve students performance
One Sample t-test
data: totalGBAdiff$Score
t = 10.486, df = 163, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
0.6087682 0.8912318
sample estimates:
mean of x
0.75 In the result above using T-test for paired observations, it shows that using PBA significantly improve students’ performance with a p-value result of 0.00000000000000022.