Dokumen tersebut membahas empat jenis validitas tes yaitu validitas isi, konstruk, sejalan, dan prediktif. Validitas prediktif dibuktikan dengan menghitung koefisien korelasi antara hasil tes awal dan tes berikutnya. Koefisien korelasi menunjukkan tingkat korelasi antara variabel yang diukur. Dokumen tersebut juga mendemonstrasikan perhitungan koefisien korelasi untuk membuktikan validitas prediktif suatu tes.
Dokumen tersebut menganalisis korelasi antara nilai mata pelajaran Bahasa dengan Matimatika berdasarkan data 30 siswa. Terdapat korelasi yang sangat kuat antara kedua mata pelajaran tersebut dengan nilai korelasi Pearson sebesar 0,932 dan Spearman sebesar 0,939. Hal ini menunjukkan bahwa semakin tinggi nilai Bahasa, semakin tinggi pula nilai Matimatikanya.
Tabel tersebut menampilkan statistik deskriptif dari produktivitas pupuk pada hari Sabtu, Minggu, dan Senin. Rata-rata produktivitas pupuk pada hari Sabtu adalah 23,5 satuan, Minggu 21,3 satuan, dan Senin 23,6 satuan. Uji ANOVA menunjukkan tidak ada perbedaan signifikan antara rata-rata ketiga hari tersebut pada taraf signifikansi 5%.
Dokumen tersebut membahas empat jenis validitas tes yaitu validitas isi, konstruk, sejalan, dan prediktif. Validitas prediktif dibuktikan dengan menghitung koefisien korelasi antara hasil tes awal dan tes berikutnya. Koefisien korelasi menunjukkan tingkat korelasi antara variabel yang diukur. Dokumen tersebut juga mendemonstrasikan perhitungan koefisien korelasi untuk membuktikan validitas prediktif suatu tes.
Dokumen tersebut menganalisis korelasi antara nilai mata pelajaran Bahasa dengan Matimatika berdasarkan data 30 siswa. Terdapat korelasi yang sangat kuat antara kedua mata pelajaran tersebut dengan nilai korelasi Pearson sebesar 0,932 dan Spearman sebesar 0,939. Hal ini menunjukkan bahwa semakin tinggi nilai Bahasa, semakin tinggi pula nilai Matimatikanya.
Tabel tersebut menampilkan statistik deskriptif dari produktivitas pupuk pada hari Sabtu, Minggu, dan Senin. Rata-rata produktivitas pupuk pada hari Sabtu adalah 23,5 satuan, Minggu 21,3 satuan, dan Senin 23,6 satuan. Uji ANOVA menunjukkan tidak ada perbedaan signifikan antara rata-rata ketiga hari tersebut pada taraf signifikansi 5%.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Tabel tersebut menampilkan statistik deskriptif dari produktivitas pupuk pada hari Sabtu, Minggu, dan Senin. Rata-rata produktivitas pupuk pada hari Sabtu adalah 23,5 satuan, Minggu 21,3 satuan, dan Senin 23,6 satuan. Uji ANOVA menunjukkan tidak ada perbedaan signifikan antara rata-rata ketiga hari tersebut pada taraf signifikansi 5%.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
Korelasi merupakan hubungan antara dua variabel. Terdapat tiga jenis korelasi yang dijelaskan dalam dokumen yaitu korelasi Pearson, Spearman, dan Kendall. Korelasi Pearson digunakan untuk variabel kontinu, Spearman untuk skala ordinal, dan Kendall untuk data ranking. Output analisis menunjukkan hubungan yang kuat antara nilai pretes dan postes menggunakan korelasi Pearson dan Spearman.
This document presents the results of a study that examined the effects of using comic magazines to improve reading comprehension among 8th grade students in Cilegon, Indonesia. It shows pretest and posttest scores for 30 students, with average scores increasing from 56.0667 to 62.2667 after the intervention. Statistical analysis found a strong positive correlation between pretest and posttest scores (r=0.932) and that posttest scores can reliably predict pretest scores.
This document summarizes information about Galen vein aneurysms, including:
- It describes three main clinical presentations: newborns, infants, and adolescents/adults. Newborns often present with macrocephaly and prominent frontal veins.
- Diagnostic studies include ultrasound, MRI, angiography to classify the aneurysm type and assess for an adjacent arteriovenous fistula or malformation.
- Treatment has evolved from no treatment historically to open surgery starting in the 1940s, with endovascular embolization now also used starting in the late 1980s/early 1990s. Prognostic scales help guide treatment planning and predict outcomes.
This document discusses the cost of capital and how to calculate it. It defines cost of capital as the rate of return a firm must earn on its investments to maintain its market value and attract funds. It then discusses how to calculate the costs of different sources of capital including long-term debt, preferred stock, common stock, and retained earnings. It explains how to calculate the weighted average cost of capital (WACC) and discusses weighting schemes. Finally, it discusses how to determine break points and calculate the weighted marginal cost of capital (WMCC), which can be used with the investment opportunities schedule to make financing decisions.
The document summarizes key concepts from a chapter on cost-volume-profit (CVP) analysis. It covers CVP assumptions and terminology, essential features of CVP analysis including determining the break-even point, incorporating income taxes into CVP, using CVP for decision making and sensitivity analysis, and adapting CVP for alternative cost structures. Examples are provided to illustrate calculating break-even units and revenues, conducting sensitivity analysis using spreadsheets, and evaluating different rental options for a software company using CVP analysis.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
Korelasi merupakan hubungan antara dua variabel. Terdapat tiga jenis korelasi yang dijelaskan dalam dokumen yaitu korelasi Pearson, Spearman, dan Kendall. Korelasi Pearson digunakan untuk variabel kontinu, Spearman untuk skala ordinal, dan Kendall untuk data ranking. Output analisis menunjukkan hubungan yang kuat antara nilai pretes dan postes menggunakan korelasi Pearson dan Spearman.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Tabel tersebut menampilkan statistik deskriptif dari produktivitas pupuk pada hari Sabtu, Minggu, dan Senin. Rata-rata produktivitas pupuk pada hari Sabtu adalah 23,5 satuan, Minggu 21,3 satuan, dan Senin 23,6 satuan. Uji ANOVA menunjukkan tidak ada perbedaan signifikan antara rata-rata ketiga hari tersebut pada taraf signifikansi 5%.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
Korelasi merupakan hubungan antara dua variabel. Terdapat tiga jenis korelasi yang dijelaskan dalam dokumen yaitu korelasi Pearson, Spearman, dan Kendall. Korelasi Pearson digunakan untuk variabel kontinu, Spearman untuk skala ordinal, dan Kendall untuk data ranking. Output analisis menunjukkan hubungan yang kuat antara nilai pretes dan postes menggunakan korelasi Pearson dan Spearman.
This document presents the results of a study that examined the effects of using comic magazines to improve reading comprehension among 8th grade students in Cilegon, Indonesia. It shows pretest and posttest scores for 30 students, with average scores increasing from 56.0667 to 62.2667 after the intervention. Statistical analysis found a strong positive correlation between pretest and posttest scores (r=0.932) and that posttest scores can reliably predict pretest scores.
This document summarizes information about Galen vein aneurysms, including:
- It describes three main clinical presentations: newborns, infants, and adolescents/adults. Newborns often present with macrocephaly and prominent frontal veins.
- Diagnostic studies include ultrasound, MRI, angiography to classify the aneurysm type and assess for an adjacent arteriovenous fistula or malformation.
- Treatment has evolved from no treatment historically to open surgery starting in the 1940s, with endovascular embolization now also used starting in the late 1980s/early 1990s. Prognostic scales help guide treatment planning and predict outcomes.
This document discusses the cost of capital and how to calculate it. It defines cost of capital as the rate of return a firm must earn on its investments to maintain its market value and attract funds. It then discusses how to calculate the costs of different sources of capital including long-term debt, preferred stock, common stock, and retained earnings. It explains how to calculate the weighted average cost of capital (WACC) and discusses weighting schemes. Finally, it discusses how to determine break points and calculate the weighted marginal cost of capital (WMCC), which can be used with the investment opportunities schedule to make financing decisions.
The document summarizes key concepts from a chapter on cost-volume-profit (CVP) analysis. It covers CVP assumptions and terminology, essential features of CVP analysis including determining the break-even point, incorporating income taxes into CVP, using CVP for decision making and sensitivity analysis, and adapting CVP for alternative cost structures. Examples are provided to illustrate calculating break-even units and revenues, conducting sensitivity analysis using spreadsheets, and evaluating different rental options for a software company using CVP analysis.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
The document contains data on the names of lecturers, room locations, and seating positions of 30 lecturers. It performs a factor analysis on this data, showing communalities, descriptive statistics, total variance explained by components, a component matrix, and correlations between the variables. The analysis identifies two components that explain about 49% and 45% of the variance respectively in the data.
Korelasi merupakan hubungan antara dua variabel. Terdapat tiga jenis korelasi yang dijelaskan dalam dokumen yaitu korelasi Pearson, Spearman, dan Kendall. Korelasi Pearson digunakan untuk variabel kontinu, Spearman untuk skala ordinal, dan Kendall untuk data ranking. Output analisis menunjukkan hubungan yang kuat antara nilai pretes dan postes menggunakan korelasi Pearson dan Spearman.
Dokumen tersebut melakukan analisis perbedaan nilai rata-rata rapot siswa SMP pada mata pelajaran IPA, IPS, dan MTK menggunakan uji ANOVA. Hasilnya menunjukkan tidak ada perbedaan yang signifikan antara nilai rata-rata ketiga mata pelajaran tersebut.
Analisis faktor digunakan untuk mengidentifikasi dan mengelompokkan faktor-faktor yang mewakili suatu variabel atau fenomena. Metode ini dapat menganalisis data primer dan sekunder untuk menentukan dimensi yang tidak terlihat dalam variabel dan mengelompokkan responden. Teknik interdependensi digunakan untuk menganalisis hubungan antar variabel dan menentukan kontribusi masing-masing variabel terhadap faktor.
This document summarizes the results of a factor analysis on three variables: minat, orangtua, and teman. The analysis extracted one component that explains 87.65% of the total variance. All three variables had high loadings (above 0.8) on the single extracted component. The correlation matrix showed that the variables were correlated but the matrix was not positive definite.
This document summarizes the results of a factor analysis on three variables: minat, orangtua, and teman. The analysis extracted one component that explains 87.65% of the total variance. All three variables had high loadings (above 0.8) on the single extracted component. The correlation matrix showed that the variables were correlated but the matrix was not positive definite.
This document summarizes the results of a factor analysis conducted on three variables: minat, orangtua, and teman. The analysis extracted one component that explains 87.65% of the total variance among the variables. All three variables have high communalities greater than 0.75. The component matrix shows that minat has a strong negative loading while orangtua and teman have strong positive loadings on the single extracted component.
This document summarizes the results of a factor analysis on three variables: minat, orangtua, and teman. The analysis extracted one component that explains 87.65% of the total variance. All three variables had high loadings (above 0.8) on the single extracted component. The correlation matrix showed that the variables were correlated but the matrix was not positive definite.
A factor analysis was conducted on three variables: minat, orangtua, and teman. The analysis extracted one component that explained 87.65% of the total variance. All three variables had high loadings (greater than 0.7) on the single extracted component. The correlation matrix indicated that the data was not positive definite.
This document summarizes the results of a factor analysis conducted on three variables: minat, orangtua, and teman. The analysis extracted one component that explains 87.65% of the total variance among the variables. All three variables have high communalities greater than 0.75. The component matrix shows that minat has a strong negative loading while orangtua and teman have strong positive loadings on the single extracted component.
This document summarizes the results of a factor analysis on three variables: minat, orangtua, and teman. The analysis extracted one component that explains 87.65% of the total variance. All three variables had high loadings (above 0.8) on the single extracted component. The correlation matrix showed that the variables were correlated but the matrix was not positive definite.
This document summarizes the results of a factor analysis on three variables: minat, orangtua, and teman. The analysis extracted one component that explains 87.65% of the total variance. All three variables had high loadings (above 0.8) on the single extracted component. The correlation matrix showed that the variables were correlated but the matrix was not positive definite.
Dokumen tersebut membahas tentang regresi linear tunggal dan berganda untuk menguji hubungan antara variabel tergantung dan bebas. Regresi linear tunggal menggunakan satu variabel bebas sedangkan regresi berganda menggunakan lebih dari satu variabel bebas. Dokumen ini juga menampilkan contoh penggunaan regresi untuk menguji hubungan antara nilai mata kuliah matematika dasar dan biologi umum.
Tabel tersebut menampilkan statistik deskriptif dari produktivitas pupuk pada hari Sabtu, Minggu, dan Senin. Rata-rata produktivitas pupuk pada hari Sabtu adalah 23,5 satuan, Minggu 21,3 satuan, dan Senin 23,6 satuan. Uji ANOVA menunjukkan tidak ada perbedaan signifikan antara rata-rata ketiga hari tersebut pada taraf signifikansi 5%.
1. KORELASI
Korelasi merupakan hubungan antara dua buah variabel, jika nilai suatu variabel
naik, sedangkan nilai variabel yang lain turun, maka dikatakan terdapat hubungan negatif
serta sebaliknya. Korelasi yang biasa digunakan dalam penelitian adalah:
a. Korelasi Pearson Product Moment
Korelasi ini dilakukan jika sepasang variabel kontinu, memiliki korelasi. Jumlah
pengamatan variabel X dan Y harus sama, atau kedua nilai variabel tersebut berpasangan.
Semakin besar nilai koefisien korelasinya maka akan semakin besar pula derajat
hubungan antara kedua variabel. Korelasi Pearson biasanya pada hubungan yang
berbentuk linier (keduanya meningkat atau keduanya menurun). Koefisien korelasi ini
tidak menunjukkan adanya hubungan kausal antar variabelnya.
Contoh kasus: jika terdapat hubungan korelasi antara variabel citra merek dengan
kepuasan konsumen motor merek Honda.
b. Korelasi Spearman
Jika pengamatan dari 2 variabel X dan Y adalah dalam bentuk skala ordinal, maka
derajat korelasi dicari dengan koefisien korelasi spearman. Prosedurnya terdiri atas:
1. Atur Pengamatan dari kedua variabel dalam bentuk ranking.
2. Cari beda dari masing-masing pengamatan yang sudah berpasangan.
C.Korelasi Rank Kendall
Analisis korelasi rank Kendall digunakan untuk mencari hubungan dan menguji
hipotesis antara dua variabel atau lebih, bila datanya berbentuk ordinal atau ranking.
Kelebihan metode ini bila digunakan untuk menganalisis sampel berukuran lebih dari 10
dan dapat dikembangkan untuk mencari koefisien korelasi parsial.Metode yang
digunakan pada analisis koefisien korelasi rank Kendall yang diberi notasi τ adalah
sebagai berikut: 1. Beri ranking data observasi pada variabel X dan variabel Y.
2. Susun n objek sehingga ranking X untuk subjek itu dalam urutan wajar, yaitu 1, 2, 3,
…, n. Apabila terdapat ranking yang sama maka ranking-nya adalah rata-ratanya.
3. Amati ranking Y dalam urutan yang bersesuaian dengan ranking X yang ada dalam
urutan wajar kemudian tentukan jumlah angka pasangan concordant (Nc) dan jumlah
angka pasangan discordant (Nd).
Koefisien korelasi non-parametrik
Koefisien korelasi Pearson merupakan statistik parametrik, dan ia kurang begitu
menggambarkan korelasi bila asumsi dasar normalitas suatu data dilanggar. Metode
korelasi non-parametrik seperti ρ Spearman and τ Kendall berguna ketika distribusi
tidak normal. Koefisien korelasi non-parametrik masih kurang kuat bila dibandingkan
dengan metode parametrik jika asumsi normalitas data terpenuhi, namun cenderung
memberikan hasil distrosi ketika asumsi tersebut tak terpenuhi.
3. Correlations
Descriptive Statistics
Mean Std. Deviation N
pretest 56.0667 6.04542 30
posttest 62.2667 6.92289 30
Correlations
pretest posttest
pretest Pearson Correlation 1 .932**
Sig. (2-tailed) .000
N 30 30
posttest Pearson Correlation .932** 1
Sig. (2-tailed) .000
N 30 30
**. Correlation is significant at the 0.01 level
(2-tailed).
ANALISIS OUTPUT
Dari output di peroleh informasi sebagai berikut :
Mean dari pretest = 56.0667
Mean dari posttest = 62.2667
Standard deviasi pretest = 6.04542
Standar deviasi posttest = 6.92289
Banyaknya data yang di analisis = 30
Dengan menggunakan korelasi pearson di peroleh:r =0.932
Itu berarti hubungan antara pretest dan pretest sangat kuat .dari
koefisien korelasi yang bertanda + di peroleh arti adanya
hubungan yang searah.artinya,jika posttest meningkat hasil
pretest semakin tinggi.begitu pula sebaliknya.
4. Nonparametric Correlations
Correlations
pretest posttest
Spearman's rho pretest Correlation Coefficient 1.000 .939**
Sig. (2-tailed) . .000
N 30 30
posttest Correlation Coefficient .939** 1.000
Sig. (2-tailed) .000 .
N 30 30
**. Correlation is significant at the 0.01 level (2-tailed).
ANALISIS OUTPUT
Dengan menggunakan nilai koefisien korelasi spearman’s di
peroleh :
Nilai korelasinya = 0.939 ;artinya, asosiasi antara nilai pretest
dan posttest searah.