tag:blogger.com,1999:blog-5622002286975943862024-03-19T02:21:34.624-07:00Belajar Bareng Data Science"An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem." -- John TukeyWildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.comBlogger25125tag:blogger.com,1999:blog-562200228697594386.post-84530729591052536052023-07-11T00:48:00.002-07:002023-09-12T21:51:00.932-07:00Educational Data Mining<p> ...</p><h3 style="text-align: left;">Referensi</h3><p></p><ol style="text-align: left;"><li>Data mining applications in university information management system development, <a href="https://www.degruyter.com/document/doi/10.1515/jisys-2022-0006/html" target="_blank">https://www.degruyter.com/document/doi/10.1515/jisys-2022-0006/html</a></li><li>Educational data mining: prediction of students' academic performance using machine learning algorithms, <a href="https://slejournal.springeropen.com/articles/10.1186/s40561-022-00192-z#Tab2" target="_blank">https://slejournal.springeropen.com/articles/10.1186/s40561-022-00192-z#Tab2</a></li><li>Data Mining on the Prediction of Student’s Performance at the High School National Examination, <a href="https://www.scitepress.org/Papers/2021/104080/104080.pdf" target="_blank">https://www.scitepress.org/Papers/2021/104080/104080.pdf</a></li>National standardized tests database implemented as a research methodology in mathematics education. The case of algebraic powers, <a href="https://hal.science/hal-02430515/document" target="_blank">https://hal.science/hal-02430515/document</a><li>Student Performance in R, <a href="https://github.com/nicolettejohnson/student-performance-r" target="_blank">https://github.com/nicolettejohnson/student-performance-r</a></li><li></li><li>Use Data Warehouse and Data Mining to Predict Student Academic Performace in Schools,, <a href="https://www.slideshare.net/Ranjithgowda93/data-mining-to-predict-academ" target="_blank">https://www.slideshare.net/Ranjithgowda93/data-mining-to-predict-academ</a></li><br /></ol><p></p>Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-15669493697070092732023-03-21T03:57:00.003-07:002023-03-21T03:57:17.715-07:00Applied Multivariate Analysis with R in dairy farming<br /><br />Applied multivariate analysis with R can be useful in dairy farming for a variety of purposes, such as:<br /><br /><h3 style="text-align: left;">1. Cluster analysis </h3><div><br /></div><div>Cluster analysis can be used to group cows based on their milk production, milk composition, and other characteristics. This can help farmers identify subgroups of cows that require specific management practices, such as different feed or medication regimes.<div><br /></div><div>Here's an example of how to perform cluster analysis using multivariate analysis with R in dairy farming:</div><div><br /></div><div><div></div><blockquote><div># Load the necessary packages</div><div>library("cluster")</div><div>library("factoextra")</div><div><br /></div><div># Load the dataset (replace "data.csv" with the name of your file)</div><div>data <- read.csv("data.csv")</div><div><br /></div><div># Select the variables you want to use for clustering (replace "var1", "var2", etc. with the names of your variables)</div><div>vars <- data[,c("var1", "var2", "var3", "var4")]</div><div><br /></div><div># Perform hierarchical clustering</div><div>hc <- hclust(dist(vars))</div><div><br /></div><div># Determine the optimal number of clusters using the elbow method</div><div>fviz_nbclust(vars, hcut, method = "wss") # "wss" stands for "within sum of squares"</div><div><br /></div><div># Based on the elbow plot, let's say we choose 4 clusters</div><div>k <- 4</div><div><br /></div><div># Perform k-means clustering</div><div>km <- kmeans(vars, k)</div><div><br /></div><div># Visualize the clusters</div><div>fviz_cluster(km, data = vars, stand = FALSE, geom = "point")</div><div></div></blockquote><br />In this example, we first load the necessary packages (cluster and factoextra) and then load the dataset. We then select the variables we want to use for clustering and perform hierarchical clustering using the hclust() function. We then use the fviz_nbclust() function from the factoextra package to determine the optimal number of clusters using the elbow method. Based on the elbow plot, we choose 4 clusters and then perform k-means clustering using the kmeans() function. Finally, we visualize the clusters using the fviz_cluster() function from the factoextra package.<br /><br />Note that you will need to modify this code to fit your specific dataset and research question.<br /><br /><h3 style="text-align: left;">2. Principal Component Analysis (PCA)</h3></div><div><br /></div><div>PCA can be used to identify patterns and relationships among variables that contribute to milk production. This can help farmers identify key factors that impact milk production and develop strategies to optimize these factors.<br /><br /><div>Here's an example of how to perform Principal Component Analysis (PCA) using multivariate analysis with R in dairy farming:</div><div><br /></div><div></div><blockquote><div># Load the necessary packages</div><div>library("FactoMineR")</div><div>library("factoextra")</div><div><br /></div><div># Load the dataset (replace "data.csv" with the name of your file)</div><div>data <- read.csv("data.csv")</div><div><br /></div><div># Select the variables you want to use for PCA (replace "var1", "var2", etc. with the names of your variables)</div><div>vars <- data[,c("var1", "var2", "var3", "var4")]</div><div><br /></div><div># Perform PCA</div><div>pca <- PCA(vars, graph = FALSE)</div><div><br /></div><div># Visualize the results</div><div>fviz_pca_var(pca) # plot of variables</div><div>fviz_pca_biplot(pca) # biplot of variables and observations</div></blockquote><div></div><div><br /></div><div><br /></div><div>In this example, we first load the necessary packages (FactoMineR and factoextra) and then load the dataset. We then select the variables we want to use for PCA and perform PCA using the PCA() function from the FactoMineR package. We set graph = FALSE to prevent the function from automatically plotting the results. Finally, we visualize the results using the fviz_pca_var() and fviz_pca_biplot() functions from the factoextra package.</div><div><br /></div><div>Note that you will need to modify this code to fit your specific dataset and research question. Additionally, you may want to explore other options for visualizing the results of PCA, such as scree plots, heatmaps, or 3D scatterplots.</div><br /><h3 style="text-align: left;">3. Discriminant Analysis</h3></div><div><br /></div><div>Discriminant analysis can be used to classify cows based on their milk production, milk composition, or other characteristics. This can help farmers identify which cows are the most productive and which may need additional attention.</div><div><br /></div><div><div>Here's an example of how to perform Discriminant Analysis using multivariate analysis with R in dairy farming:</div><div><br /></div><div></div><blockquote><div># Load the necessary packages</div><div>library("MASS")</div><div>library("caret")</div><div><br /></div><div># Load the dataset (replace "data.csv" with the name of your file)</div><div>data <- read.csv("data.csv")</div><div><br /></div><div># Split the dataset into training and testing sets (replace "0.8" with the proportion of data you want to use for training)</div><div>index <- createDataPartition(data$Class, p = 0.8, list = FALSE)</div><div>train <- data[index,]</div><div>test <- data[-index,]</div><div><br /></div><div># Select the variables you want to use for discriminant analysis (replace "var1", "var2", etc. with the names of your variables)</div><div>vars <- train[,c("var1", "var2", "var3", "var4")]</div><div><br /></div><div># Perform linear discriminant analysis</div><div>lda <- lda(Class ~ ., data = train[,c("Class", vars)])</div><div><br /></div><div># Predict the classes of the testing set</div><div>predictions <- predict(lda, test[,c("var1", "var2", "var3", "var4")])</div><div><br /></div><div># Evaluate the accuracy of the predictions</div><div>confusionMatrix(predictions$class, test$Class)</div></blockquote><div></div><div><br /></div><div>In this example, we first load the necessary packages (MASS and caret) and then load the dataset. We then split the dataset into training and testing sets using the createDataPartition() function from the caret package. We select the variables we want to use for discriminant analysis and perform linear discriminant analysis using the lda() function from the MASS package. We then predict the classes of the testing set using the predict() function and evaluate the accuracy of the predictions using the confusionMatrix() function from the caret package.</div><div><br /></div><div>Note that you will need to modify this code to fit your specific dataset and research question. Additionally, you may want to explore other options for performing discriminant analysis, such as quadratic discriminant analysis or regularized discriminant analysis.</div><br /><br /><h3 style="text-align: left;">4. Regression Analysis</h3></div><div><br /></div><div>Regression analysis can be used to model the relationship between milk production and various predictors, such as age, breed, diet, and management practices. This can help farmers identify the factors that contribute to milk production and develop strategies to optimize these factors.</div><div><br /></div><div><div>Here's an example of how to perform Regression Analysis using multivariate analysis with R in dairy farming:</div><div><br /></div><div></div><blockquote><div># Load the necessary packages</div><div>library("car")</div><div>library("tidyverse")</div><div><br /></div><div># Load the dataset (replace "data.csv" with the name of your file)</div><div>data <- read.csv("data.csv")</div><div><br /></div><div># Select the variables you want to use for regression analysis (replace "var1", "var2", etc. with the names of your variables)</div><div>vars <- data[,c("var1", "var2", "var3", "var4")]</div><div><br /></div><div># Fit a multiple linear regression model</div><div>model <- lm(outcome ~ var1 + var2 + var3 + var4, data = data)</div><div><br /></div><div># Check the assumptions of the model</div><div>plot(model) # plot of residuals vs. fitted values</div><div>qqPlot(model) # normal probability plot of residuals</div><div><br /></div><div># Evaluate the performance of the model</div><div>summary(model) # summary of model coefficients and significance</div><div>confint(model) # confidence intervals of model coefficients</div><div>anova(model) # analysis of variance table</div><div><br /></div><div># Make predictions using the model</div><div>new_data <- data.frame(var1 = c(1, 2, 3), var2 = c(4, 5, 6), var3 = c(7, 8, 9), var4 = c(10, 11, 12))</div><div>predictions <- predict(model, newdata = new_data)</div></blockquote><div></div><div><br /></div><div><br /></div><div>In this example, we first load the necessary packages (car and tidyverse) and then load the dataset. We then select the variables we want to use for regression analysis and fit a multiple linear regression model using the lm() function from the stats package. We check the assumptions of the model using the plot() and qqPlot() functions from the car package. We evaluate the performance of the model using the summary(), confint(), and anova() functions. Finally, we make predictions using the model by creating a new dataset with the predictor variables and using the predict() function.</div><div><br /></div><div>Note that you will need to modify this code to fit your specific dataset and research question. Additionally, you may want to explore other options for performing regression analysis, such as non-linear regression, mixed-effects models, or generalized linear models.</div><br /><br /><h3 style="text-align: left;">5. Time Series Analysis</h3></div><div><br /></div><div>Time series analysis can be used to forecast future milk production based on historical data. This can help farmers plan for future milk production and make informed decisions about pricing, marketing, and other business decisions.<br /><br /><div>Here's an example of how to perform Time Series Analysis using multivariate analysis with R in dairy farming:</div><div><br /></div><div></div><blockquote><div># Load the necessary packages</div><div>library("zoo")</div><div>library("ggplot2")</div><div><br /></div><div># Load the dataset (replace "data.csv" with the name of your file)</div><div>data <- read.csv("data.csv")</div><div><br /></div><div># Convert the data to a time series object</div><div>ts_data <- zoo(data[,c("var1", "var2", "var3", "var4")], order.by = data$Date)</div><div><br /></div><div># Plot the time series</div><div>autoplot(ts_data, facets = TRUE) + theme_minimal()</div><div><br /></div><div># Decompose the time series</div><div>decomp <- decompose(ts_data)</div><div>autoplot(decomp)</div><div><br /></div><div># Fit a time series model</div><div>model <- auto.arima(ts_data$var1)</div><div><br /></div><div># Make predictions using the model</div><div>predictions <- forecast(model, h = 10)</div><div><br /></div><div># Plot the predictions</div><div>autoplot(predictions) + theme_minimal()</div><div></div></blockquote><div><br /></div><div><br /></div><div>In this example, we first load the necessary packages (zoo and ggplot2) and then load the dataset. We convert the data to a time series object using the zoo() function from the zoo package and plot the time series using the autoplot() function from the ggplot2 package. We then decompose the time series using the decompose() function and plot the components using autoplot(). We fit a time series model using the auto.arima() function from the forecast package and make predictions using the forecast() function. Finally, we plot the predictions using autoplot().</div><div><br /></div><div>Note that you will need to modify this code to fit your specific dataset and research question. Additionally, you may want to explore other options for performing time series analysis, such as seasonal ARIMA models, exponential smoothing models, or dynamic regression models.</div><div><br /></div>In summary, multivariate analysis with R can be a powerful tool for dairy farmers to optimize their production practices and improve their profitability.<div><br /></div><div><br /></div><h3 style="text-align: left;">References</h3></div></div><div>1. ChatGPT. (2023, March 21). Applied Multivariate Analysis with R in dairy farming [Online forum post]. Retrieved from https://www.gpt.com</div>Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-27502854529900350562022-06-14T02:17:00.005-07:002022-07-14T07:13:49.405-07:00Factor Analysis<h3 style="text-align: left;">SPSS</h3><p>Analyze --> Dimension Reduction --> Factor </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEilyr3ls6xz62xyeBTZ4RggS9JZdAgPAJ2OR9ExtSnhPeCevd6MrCRJCtcBx4y-qOiKXvYz5oQRl2wYSgitglQ3rh1RtJdfGr0WlhohJnr6U8WMulLvJdi0TYpMaZOLucO7tGdWcJzULr6SrgO3WHTPc9VCLuKrcBvd2bmCVdlSVprXOEAfQyAKjywe/s964/Screen%20Shot%202022-07-14%20at%2021.04.33.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="238" data-original-width="964" height="79" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEilyr3ls6xz62xyeBTZ4RggS9JZdAgPAJ2OR9ExtSnhPeCevd6MrCRJCtcBx4y-qOiKXvYz5oQRl2wYSgitglQ3rh1RtJdfGr0WlhohJnr6U8WMulLvJdi0TYpMaZOLucO7tGdWcJzULr6SrgO3WHTPc9VCLuKrcBvd2bmCVdlSVprXOEAfQyAKjywe/s320/Screen%20Shot%202022-07-14%20at%2021.04.33.png" width="320" /></a></div><br /><p>Extraction --> Method (Maximum likelihood)</p><h3 style="text-align: left;"> Referensi</h3><ol style="text-align: left;"><li>SPSS Factor Analysis – Beginners Tutorial, <a href="https://www.spss-tutorials.com/spss-factor-analysis-tutorial/" target="_blank">https://www.spss-tutorials.com/spss-factor-analysis-tutorial/</a><br /></li></ol>Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-43065036874459587472020-10-07T15:24:00.009-07:002022-08-26T09:30:53.431-07:00Assessing ranks<p><b>Pertanyaan </b><br /><br />Misalkan, jika kita memiliki nilai Fisika dari seluruh sekolah yang berada di sebuah Desa, bagaimana cara merangking sekolah berdasarkan capaian kemampuan Fisikanya, jika populasi siswa setiap sekolah sangat berbeda, misal ada sekolah yang mengambil ujian Fisika hanya 5 orang dan ada sekolah lain yang sampai 100 orang. Karena kalau merangking dari nilai rata-rata saja, tidak begitu valid, terutama jika populasinya kecil. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEggZgkLQcagqTWLX6VWcLpE0hP25Q2aNSl1JQ3bOr-1gB7NhQlEf7MZgLomhZPKj_J-eZUCZOOHdXdDQ5T7SZuf7Fvou3rB3684aCsjaQKLTb5iCDoyZk7jy8J7Dir5KA9PExwFiOK8Se8/s600/comparing-means.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="517" data-original-width="600" height="345" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEggZgkLQcagqTWLX6VWcLpE0hP25Q2aNSl1JQ3bOr-1gB7NhQlEf7MZgLomhZPKj_J-eZUCZOOHdXdDQ5T7SZuf7Fvou3rB3684aCsjaQKLTb5iCDoyZk7jy8J7Dir5KA9PExwFiOK8Se8/w400-h345/comparing-means.png" width="400" /></a></div><br /><p><br /></p><h3 style="text-align: left;">Referensi </h3><p></p><ol style="text-align: left;"><li>An Introduction to statistics : Assessing ranks, <a href="http://www.floppybunny.org/robin/web/virtualclassroom/stats/basics/part8.pdf" target="_blank">http://www.floppybunny.org/robin/web/virtualclassroom/stats/basics/part8.pdf</a></li><li>Score Transformations, <a href="http://faculty.cbu.ca/~erudiuk/IntroBook/sbk14m.htm" target="_blank">http://faculty.cbu.ca/~erudiuk/IntroBook/sbk14m.htm</a></li><li>Percentile rank scores are congruous indicators of relative performance, or aren’t they?, <a href="https://arxiv.org/pdf/1108.1860.pdf" target="_blank">https://arxiv.org/pdf/1108.1860.pdf</a></li><li>Comparing Means in R, <a href="http://www.sthda.com/english/wiki/comparing-means-in-r" target="_blank">http://www.sthda.com/english/wiki/comparing-means-in-r</a></li><li>Better Ranking using Bayesian Average, <a href="https://arpitbhayani.me/blogs/bayesian-average" target="_blank">https://arpitbhayani.me/blogs/bayesian-average</a></li><li>Analisis Kruskall-Wallis dengan SPSS, <a href="https://www.semestapsikometrika.com/2018/11/analisis-kruskall-wallis-dengan-spss.html" target="_blank">https://www.semestapsikometrika.com/2018/11/analisis-kruskall-wallis-dengan-spss.html</a></li><li>One-Way Analysis of Variance:Comparing Several Means, <a href="https://www.westga.edu/academics/research/vrc/assets/docs/OneWayANOVA_LectureNotes.pdf" target="_blank">https://www.westga.edu/academics/research/vrc/assets/docs/OneWayANOVA_LectureNotes.pdf</a></li><li>Compare the means of two or more variables or groups in the data, <a href="https://radiant-rstats.github.io/docs/basics/compare_means.html" target="_blank">https://radiant-rstats.github.io/docs/basics/compare_means.html</a> </li><li> Using the Bayesian Average in Custom Ranking, <a href="https://www.algolia.com/doc/guides/managing-results/must-do/custom-ranking/how-to/bayesian-average/" target="_blank">https://www.algolia.com/doc/guides/managing-results/must-do/custom-ranking/how-to/bayesian-average/</a></li><li>Use Bayesian Averages to Improve Rating Sorting in your Elasticsearch Index, <a href="https://jolicode.com/blog/use-bayesian-averages-to-improve-rating-sorting-in-your-elasticsearch-index" target="_blank">https://jolicode.com/blog/use-bayesian-averages-to-improve-rating-sorting-in-your-elasticsearch-index</a></li><li>Bayesian Average Ratings, <a href="https://www.evanmiller.org/bayesian-average-ratings.html" target="_blank">https://www.evanmiller.org/bayesian-average-ratings.html</a></li><li>POSTGRESQL: WEIGHTED AVERAGE INSTEAD OF AVERAGE?-POSTGRESQL, <a href="https://www.appsloveworld.com/postgresql/100/95/postgresql-weighted-average-instead-of-average" target="_blank">https://www.appsloveworld.com/postgresql/100/95/postgresql-weighted-average-instead-of-average</a></li></ol><p></p>Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-28746677284707870222019-10-26T15:07:00.000-07:002019-10-26T15:07:17.700-07:00WrightMap: IRT Item-Person MapSecara default, pelabelan item dalam item person map menggunakan WrightMap adalah berdasarkan urutan. Jika ada soal yang dihapus, maka, akan membingungkan membaca item-person map nya.<br />
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David sudah menambah fitur untuk menggunakan custom label ini, seperti dijelaskan disini [2], tapi sepertinya update ini, baru bisa digunakan jika input WrightMap berupa CQModel<br />
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<h3>
Referensi</h3>
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<ol>
<li>WrightMap: IRT Item-Person Map with 'ConQuest' Integration, <a href="https://cran.r-project.org/web/packages/WrightMap/index.html" target="_blank">https://cran.r-project.org/web/packages/WrightMap/index.html</a></li>
<li>Using item names as item.labels #6, <a href="https://github.com/david-ti/wrightmap/issues/6" target="_blank">https://github.com/david-ti/wrightmap/issues/6</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-52488319993282799242019-10-26T09:16:00.003-07:002019-10-26T09:16:33.498-07:00Arti Nilai Parameter b yang Terlampau Besar pada Teori IRT...<br />
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Referensi</h3>
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<ol>
<li>Meaning of large values of parameter b (|b| >>) 4 in IRT theory, <a href="https://stats.stackexchange.com/questions/169247/meaning-of-large-values-of-parameter-b-b-4-in-irt-theory" target="_blank">https://stats.stackexchange.com/questions/169247/meaning-of-large-values-of-parameter-b-b-4-in-irt-theory</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-78311089078999142702019-10-22T23:31:00.002-07:002019-10-22T23:31:23.473-07:00Parallel Computing in R....<br />
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<h3>
Referensi</h3>
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<ol>
<li>Quick Intro to Parallel Computing in R, <a href="https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html" target="_blank">https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-60213381692577663942019-09-25T07:10:00.003-07:002022-09-01T23:28:31.179-07:00IRT : Item Characteristic Curve (ICC) Item Characteristic Curve (ICC) atau dikenal juga dengan Item Response Function (IRF).<br />
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<h3>
Referensi</h3>
<ol>
<li>Item Characteristic Curve, <a href="https://www.sciencedirect.com/topics/psychology/item-characteristic-curve" target="_blank">https://www.sciencedirect.com/topics/psychology/item-characteristic-curve</a></li><li>Introduction to IRT Using R (2PL), <a href="https://wnarifin.github.io/simpler/irt_2PL.html" target="_blank">https://wnarifin.github.io/simpler/irt_2PL.html</a></li><li>A visual guide to item response theory, <a href="https://www.metheval.uni-jena.de/irt/VisualIRT.pdf" target="_blank">https://www.metheval.uni-jena.de/irt/VisualIRT.pdf</a></li><li>LMS Assessment: using IRT analysis to detect defective multiple-choice test items, <a href="https://www.researchgate.net/publication/273460057_LMS_Assessment_using_IRT_analysis_to_detect_defective_multiple-choice_test_items" target="_blank">https://www.researchgate.net/publication/273460057_LMS_Assessment_using_IRT_analysis_to_detect_defective_multiple-choice_test_items</a></li><li>Hubungan Parameter Item Discrimination dengan Item Fit Menggunakan Model Dua Parameter Logistik, <a href="http://repository.ub.ac.id/id/eprint/163714/1/Danang%20Kamal%20M.pdf" target="_blank">http://repository.ub.ac.id/id/eprint/163714/1/Danang%20Kamal%20M.pdf</a></li>
</ol>
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<br />Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-72402978558143905062019-09-24T18:01:00.000-07:002019-09-27T16:37:51.674-07:00irtoys : A Collection of Functions Related to Item Response Theory (IRT)...<br />
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<h3>
Referensi</h3>
<ol>
<li>irtoys, <a href="https://slideplayer.com/slide/10753417/" target="_blank">https://slideplayer.com/slide/10753417/</a></li>
<li>Round about irtoys, Ivailo Partchev, <a href="https://cran.r-project.org/web/packages/irtoys/vignettes/irtoys-vignette.html" target="_blank">https://cran.r-project.org/web/packages/irtoys/vignettes/irtoys-vignette.html</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-42853014444629691682019-06-08T19:55:00.000-07:002019-06-08T19:55:14.423-07:00Visualisasi Spasial di R...<br />
<br />
<h3>
Referensi</h3>
<br />
<ol>
<li>Indonesia, R package contains things related to my beloved country, Indonesia,<br /><a href="https://github.com/rasyidstat/indonesia" target="_blank">https://github.com/rasyidstat/indonesia</a></li>
<li>Membuat Visualisasi Peta Menggunakan ggplot2 dan sf, <a href="https://datascience.or.id/article/Membuat-Visualisasi-Peta-Menggunakan-ggplot2-+-sf-5a8fa6e6" target="_blank">https://datascience.or.id/article/Membuat-Visualisasi-Peta-Menggunakan-ggplot2-+-sf-5a8fa6e6</a></li>
<li>How to Create Indonesia Map in R, <a href="https://www.linkedin.com/pulse/how-create-indonesia-map-r-bhara-yudhiantara" target="_blank">https://www.linkedin.com/pulse/how-create-indonesia-map-r-bhara-yudhiantara</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-46049314533976883592019-06-08T18:00:00.001-07:002019-06-08T18:00:29.833-07:00Bersahabat dengan Graph Theory...<br />
<br />
<h3>
Referensi</h3>
<br />
<ol>
<li>Graph Theory on the Farm, <a href="http://www.dcs.gla.ac.uk/~kitty/GTotF/index.html" target="_blank">http://www.dcs.gla.ac.uk/~kitty/GTotF/index.html</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com1tag:blogger.com,1999:blog-562200228697594386.post-17080889381586537032019-05-24T08:53:00.001-07:002022-07-12T01:51:07.844-07:00IRT : Equating<b>Status : Draft</b><br />
<b><br /></b>
Referensi<br />
<br />
<ol>
<li>Can AI learn to equate?, <a href="https://www.cambridgeassessment.org.uk/Images/424229-can-ai-learn-to-equate-.pdf" target="_blank">https://www.cambridgeassessment.org.uk/Images/424229-can-ai-learn-to-equate-.pdf</a></li><li>Linking and Equating of Test Scores,<a href="https://slideplayer.com/slide/8986886/" target="_blank"> https://slideplayer.com/slide/8986886/</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com1tag:blogger.com,1999:blog-562200228697594386.post-24894242420667980702019-05-24T08:49:00.000-07:002019-05-24T08:49:01.554-07:00Transformative Assesment: When Assesment is Not Only About Judging The Students But Developing Their PerformanceStatus : Draft<br />
<br />
<h3>
Referensi</h3>
<br />
<ol>
<li>How We Have Used Item Response Theory And Classroom Management To Improve Student Success Rates In Large General Chemistry Classes, <a href="http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422017000400456" target="_blank">http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422017000400456</a></li>
</ol>
<br />
<div>
<br /></div>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com1tag:blogger.com,1999:blog-562200228697594386.post-88876797737020643822019-05-16T07:57:00.003-07:002019-05-16T07:57:53.828-07:00TAM: Test Analysis Modules...<br />
<br />
<h3>
Referensi</h3>
<br />
<ol>
<li>TAM: Test Analysis Modules, <a href="https://cran.r-project.org/web/packages/TAM/index.html" target="_blank">https://cran.r-project.org/web/packages/TAM/index.html</a></li>
<li>TAM Tutorials, <a href="http://www.edmeasurementsurveys.com/TAM/Tutorials/index.htm" target="_blank">http://www.edmeasurementsurveys.com/TAM/Tutorials/index.htm</a></li>
<li>Using WrightMap with the TAM package, <a href="https://wrightmap.org/post/100850738072/using-wrightmap-with-the-tam-package" target="_blank">https://wrightmap.org/post/100850738072/using-wrightmap-with-the-tam-package</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-82299372947614736932019-04-30T11:12:00.002-07:002021-05-18T08:15:15.722-07:00Menjalankan irtoys dengan engine bilog di Mac OS Xgit clone https://github.com/OpenThinkLabs/irtoys.git<br />
cd irtoys<br />
jed R/estimate.R<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiIhzkoEBpSzE4mkIqqgm0l8aVglnRdKspm8kMhyphenhyphenlAJs_eb2u01PLB_LWdEVlSG8SVkfaSK3xX2SOONXfeRXY6wescxZ-fvUHuaZuvF2jVsFTUFXfwjtynXHalq_-Rq34MBfkio3vuuI8Y/s1600/irtoys_bilog_mac.jpeg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="256" data-original-width="1040" height="96" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiIhzkoEBpSzE4mkIqqgm0l8aVglnRdKspm8kMhyphenhyphenlAJs_eb2u01PLB_LWdEVlSG8SVkfaSK3xX2SOONXfeRXY6wescxZ-fvUHuaZuvF2jVsFTUFXfwjtynXHalq_-Rq34MBfkio3vuuI8Y/s400/irtoys_bilog_mac.jpeg" width="400" /></a></div>
R CMD build .<br />
R CMD install irtoys_0.2.1.tar.gz<br />
cp blm1.exe ~/.wine/drive_c/windows/<br />
cp blm2.exe ~/.wine/drive_c/windows/<br />
cp blm3.exe ~/.wine/drive_c/windows/<br />
<div>
<br /></div><div>Bilog versi yg digunakan ini, hanya dapat digunakan di MacOS Mojave ke bawah. </div>
<br />Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-5174118060321596042019-04-18T15:08:00.002-07:002019-04-18T15:08:14.029-07:00Bersahabat dengan JASP...<br />
<br />
Referensi<br />
<br />
<ol>
<li>JASP, A Fresh Way to Do Statistics, <a href="https://jasp-stats.org/" target="_blank">https://jasp-stats.org/</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-36239925691897017782019-03-27T02:12:00.002-07:002019-03-27T02:13:04.745-07:00Outliers...<br />
<br />
<h3>
Referensi</h3>
<ol>
<li>Outlier Treatment, <a href="http://r-statistics.co/Outlier-Treatment-With-R.html" target="_blank">http://r-statistics.co/Outlier-Treatment-With-R.html</a></li>
<li>Data Management in R Identify, describe, plot, and remove the outliers from the dataset, <a href="https://datascienceplus.com/identify-describe-plot-and-removing-the-outliers-from-the-dataset/" target="_blank">https://datascienceplus.com/identify-describe-plot-and-removing-the-outliers-from-the-dataset/</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-60396285950080162892017-05-24T03:02:00.002-07:002022-03-05T17:51:41.364-08:00Menganalisa Hasil Ujian Pilihan Ganda dengan IRT (Item Response Theory)<b>Status : Draft</b><br />
<b><br /></b>
<br />
Output IRT menghasilkan dua cluster informasi, theta siswa dan item parameter.<br />
<b><br /></b>
Θ, theta, adalah tingkat kepandaian siswa.<br />
<br />
IRT modelling dapat menggunakan satu sampai dengan 3 parameter, parameter A, B, dan G.<br />
<br />
<ol>
<li>Parameter B :<i> Item difficulty, difficulty parameter</i>. Parameter ini menunjukkan, seberapa mudah atau sulit sebuah item. Paramater B digunakan dalam <i>one-parameter </i>(1P) IRT model.</li>
<li>Parameter A = <i>Discrimination parameter</i>. Nilai parameter ini, menginformasikan seberapa efektif sebuah item dapat membedakan siswa yang pandai dan kurang pandai. <i>Two-parameter </i>(2P) IRT model menggunakan parameter A dan B.</li>
<li>Parameter C = Dikenal juga dengan nama <i>G parameter</i> atau <i>guessing parameter. </i>Nilai ini memberi informasi kepada kita, seberapa mungkin siswa menjawab suatu item dengan cara menebak. Model yang menggunakan parameter A, B dan G, disebut <i>three-parameter </i>(3P) IRT model. </li>
</ol>
<div>
Untuk menentukan <i>item difficulty </i>dan <i>item discrimination </i>kita bisa menggunakan <i>verbal terms </i>untuk mempermudah membedakannya;<br />
<br />
<i>Item difficulty</i> dapat dibagi menjadi beberapa level : </div>
<div>
<table border="1px" cellpadding="5px" cellspacing="0px" style="width: 80%px;">
<thead>
<tr>
<th>No</th>
<th>Verbal label</th>
<th>Typical value</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td><i>very easy</i></td>
<td>-2.625</td>
</tr>
<tr>
<td>2</td>
<td><i>easy </i></td>
<td>-1.5</td>
</tr>
<tr>
<td>3</td>
<td><i>medium </i></td>
<td>0</td>
</tr>
<tr>
<td>4</td>
<td><i>hard </i></td>
<td>1.5</td>
</tr>
<tr>
<td>5</td>
<td><i>very hard </i></td>
<td>2.625</td>
</tr>
</tbody>
</table>
<div>
<br />
Sedangkan <i>item discrimination </i>dapat dibagi menjadi beberapa level sebagai berikut :</div>
</div>
<div>
<table border="1px" cellpadding="5px" cellspacing="0px" style="width: 80%px;">
<thead>
<tr>
<th>No</th>
<th>Verbal label</th>
<th>Typical value</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td><i>none </i></td>
<td>0</td>
</tr>
<tr>
<td>2</td>
<td><i>low </i></td>
<td>0.4</td>
</tr>
<tr>
<td>3</td>
<td><i>moderate </i></td>
<td>1</td>
</tr>
<tr>
<td>4</td>
<td><i>high </i></td>
<td>2.1</td>
</tr>
<tr>
<td>5</td>
<td><i>perfect </i></td>
<td>999</td>
</tr>
</tbody>
</table>
<div>
<br />
Untuk <i>item discrimination, verbal label-</i>nya, dapat kita bagi sebagai berikut :</div>
</div>
<div>
<table border="1px" cellpadding="5px" cellspacing="0px" style="width: 80%px;">
<thead>
<tr>
<th>No</th>
<th>Verbal label</th>
<th>Range of values</th>
<th>Typical value</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>None</td>
<td>0</td>
<td>0.00</td>
</tr>
<tr>
<td>2</td>
<td>Very Low</td>
<td>0.01 - 0.34</td>
<td>0.18</td>
</tr>
<tr>
<td>3</td>
<td>Low</td>
<td>0.35 - 0.64</td>
<td>0.50</td>
</tr>
<tr>
<td>4</td>
<td>Moderate</td>
<td>0.65 s.d 1.34</td>
<td>1.00</td>
</tr>
<tr>
<td>5</td>
<td>High</td>
<td>1.35 s.d 1.69</td>
<td>1.50</td>
</tr>
<tr>
<td>6</td>
<td>Very High</td>
<td>> 1.70</td>
<td>2.00</td>
</tr>
<tr>
<td>7</td>
<td>Perfect</td>
<td>+∞</td>
<td>+∞</td>
</tr>
</tbody>
</table>
</div>
<div>
<h3>
ICC, Item Charasteristic Curve </h3>
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJoU-v095nmOcOSjYkcXXbug241h7hulWTGovRJHa-9R4sjNJ-O6gTtzN_5pD4cBClmFuA8CrH8i0SXjIMEwGqLiT0mxfNOucz24zT0U69Oy2U3hL0Iyjo6iGVad0KarQw9HzsQHMGwdE/s1600/icc.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="435" data-original-width="691" height="201" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJoU-v095nmOcOSjYkcXXbug241h7hulWTGovRJHa-9R4sjNJ-O6gTtzN_5pD4cBClmFuA8CrH8i0SXjIMEwGqLiT0mxfNOucz24zT0U69Oy2U3hL0Iyjo6iGVad0KarQw9HzsQHMGwdE/s320/icc.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">A typical item characteristic curve, sumber : <a href="http://iqra.openthinklabs.com/2018/12/the-basics-of-item-response-theory-using-R.html" target="_blank">The Basics of Item Response Theory Using R, Hal 13</a></td></tr>
</tbody></table>
<br />
<br /></div>
<div>
IIF, Item Information Function </div>
<div>
TIF, Test Information Function<br />
Odd ratio, </div>
<div>
Logit,<br />
Item-Person Map (IPM)<br />
Misfit, outliers<br />
Misfit indices,<br />
Model fit,<br />
Infit Standardized Residuals,<br />
Outfit Standardized Residuals,<br />
Regression Analysis<br />
Systematic Bias<br />
Chi-square,<br />
Item Fit,<br />
Infit Mean Squared,<br />
Outfit Mean Squared,<br />
Degress of freedom (df),<br />
Parallel coordinate,<br />
Person fit,<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiUPoDvUC_awrMRtn_y7N-Y_m2p5cjYliuxpHCQaB9wLY8-fJuzEp_oWdJ4FfnrqDGSMmxrUoUqY5KG9nLzhJjWV71QE-dG9H4p8TUIiU_kk7q-6Mp9cX8W821Lv3PsLMGgvFwwAUJZaho/s1600/Raw_score_distribution_in_the_classical_sense.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="456" data-original-width="587" height="310" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiUPoDvUC_awrMRtn_y7N-Y_m2p5cjYliuxpHCQaB9wLY8-fJuzEp_oWdJ4FfnrqDGSMmxrUoUqY5KG9nLzhJjWV71QE-dG9H4p8TUIiU_kk7q-6Mp9cX8W821Lv3PsLMGgvFwwAUJZaho/s400/Raw_score_distribution_in_the_classical_sense.png" width="400" /></a></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjmnV3iKJl5zJlMXO90VIvZPODhfGBOpP8ufoAZhocgeDPjmtNoAM1ZIsWBk5wsUF7vjd5zpVBW0fzvtfMSC_mLJRAWhbaICLRKeMqBMetkckXx7StCm6rcH-JHBfZEUXk1KxXu12raqD4/s1600/Theta_distribution_based_on_IRT.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="449" data-original-width="568" height="315" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjmnV3iKJl5zJlMXO90VIvZPODhfGBOpP8ufoAZhocgeDPjmtNoAM1ZIsWBk5wsUF7vjd5zpVBW0fzvtfMSC_mLJRAWhbaICLRKeMqBMetkckXx7StCm6rcH-JHBfZEUXk1KxXu12raqD4/s400/Theta_distribution_based_on_IRT.png" width="400" /></a></div>
<br />
<br />
IRF, Item Response Function,<br />
TRF, Test Response Function,<br />
<br /></div>
<h3>
Referensi</h3>
<ol>
<li>A Simple Guide to the Item Response Theory (IRT) and Rasch Modeling Chong, Ho Yu, Ph.Ds, <a href="https://www.creative-wisdom.com/computer/sas/IRT.pdf" target="_blank">https://www.creative-wisdom.com/computer/sas/IRT.pdf</a></li>
<li>Introduction to Item Response Theory, <a href="https://www.slideshare.net/NathanThompson54/introduction-to-item-response-theory-70872990" target="_blank">https://www.slideshare.net/NathanThompson54/introduction-to-item-response-theory-70872990</a></li>
<li>The Basics of Item Response Theory Using R, by Frank B. Baker, Seock-Ho Kim, <a href="https://amzn.to/2UM849e" target="_blank">https://amzn.to/2UM849e</a></li>
<li>psych: Procedures for Psychological, Psychometric, and Personality Research, <a href="https://cran.r-project.org/web/packages/psych/index.html" target="_blank">https://cran.r-project.org/web/packages/psych/index.html</a></li>
<li>ltm: Latent Trait Models under IRT, <a href="https://cran.r-project.org/web/packages/ltm/index.html" target="_blank">https://cran.r-project.org/web/packages/ltm/index.html</a></li>
<li>IRT workshop Spring 2014, <a href="https://github.com/cddesja/IRTS2014" target="_blank">https://github.com/cddesja/IRTS2014</a></li>
<li>Introduction to IRT Using R (2PL), <a href="https://wnarifin.github.io/simpler/irt_2PL.html" target="_blank">https://wnarifin.github.io/simpler/irt_2PL.html</a></li>
<li>How should we handle missing responses?, <a href="https://www.researchgate.net/post/How_should_we_handle_missing_responses" target="_blank">https://www.researchgate.net/post/How_should_we_handle_missing_responses</a></li>
<li>7 Ways To Handle Missing Data, <a href="https://measuringu.com/handle-missing-data/" target="_blank">https://measuringu.com/handle-missing-data/</a></li>
<li>Evaluating Performance of Missing Data Imputation Methods in IRT Analyses, <a href="http://ijate.net/index.php/ijate/article/view/549" target="_blank">http://ijate.net/index.php/ijate/article/view/549</a></li>
<li>link: IRT Separate Calibration Linking Methods, <a href="https://cran.r-project.org/web/packages/plink/index.html" target="_blank">https://cran.r-project.org/web/packages/plink/index.html</a></li>
<li>A method for designing IRT-based item banks, <a href="https://research.utwente.nl/en/publications/a-method-for-designing-irt-based-item-banks" target="_blank">https://research.utwente.nl/en/publications/a-method-for-designing-irt-based-item-banks</a></li>
<li>WrightMap Tutorial - Part 1, <a href="http://wrightmap.org/post/80523814110/wrightmap-tutorial-part-1" target="_blank">http://wrightmap.org/post/80523814110/wrightmap-tutorial-part-1</a></li>
<li>Data Analysis Using Item Response Theory Methodology: An Introduction to Selected Programs and Applications., <a href="https://digitalcommons.library.umaine.edu/cgi/viewcontent.cgi?article=1019&context=psy_facpub" target="_blank">https://digitalcommons.library.umaine.edu/cgi/viewcontent.cgi?article=1019&context=psy_facpub</a></li>
<li>PSYCHOMETRICS, Item Response Theory (Part 1), <a href="https://rpubs.com/castro/156912" target="_blank">https://rpubs.com/castro/156912</a></li>
<li>PSYCHOMETRICS, Item Response Theory (Part 2), <a href="https://rstudio-pubs-static.s3.amazonaws.com/156125_d88cf281bd2546c294c519afb2577768.html#" target="_blank">https://rstudio-pubs-static.s3.amazonaws.com/156125_d88cf281bd2546c294c519afb2577768.html#</a></li><li>PerFit: Person Fit, <a href="https://rdrr.io/cran/PerFit/" target="_blank">https://rdrr.io/cran/PerFit/</a></li><li>Person fit assessment using the PerFit package in R, <a href="https://www.tqmp.org/RegularArticles/vol12-3/p232/p232.pdf" target="_blank">https://www.tqmp.org/RegularArticles/vol12-3/p232/p232.pdf</a></li><li>HOW DOES DIRECTOR FINANCIAL LITERACY INFLUENCE FINANCIAL MONITORING?, <a href="https://eprints.qut.edu.au/213554/1/Jacqueline_Bettington_Thesis.pdf" target="_blank">https://eprints.qut.edu.au/213554/1/Jacqueline_Bettington_Thesis.pdf</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-14989069883517349802017-01-13T15:21:00.001-08:002017-01-13T15:21:52.270-08:00Analisis Regresi<b>Status : Draft</b><br />
<b><br /></b>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgNCYAmWJwgaKXOKmxmqa0qtAUW71xR4gYHD53Yw6eTWuoYM8L83x10U5DluHdYxjLdM8oEnKpgmI8b6o5LUkNka3fbAsbw-ZWRF5WaXn3SqnX6PsSqPMzFeGw4TAJJ1Nz6AumI4ajT_NI/s1600/MangaRegressionAnalysis2.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="262" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgNCYAmWJwgaKXOKmxmqa0qtAUW71xR4gYHD53Yw6eTWuoYM8L83x10U5DluHdYxjLdM8oEnKpgmI8b6o5LUkNka3fbAsbw-ZWRF5WaXn3SqnX6PsSqPMzFeGw4TAJJ1Nz6AumI4ajT_NI/s400/MangaRegressionAnalysis2.jpg" width="400" /></a></div>
<b><br /></b>
<br />
<h3>
Referensi</h3>
<br />
<ol>
<li>Analisis regresi, <a href="https://id.wikipedia.org/wiki/Analisis_regresi" target="_blank">https://id.wikipedia.org/wiki/Analisis_regresi</a></li>
<li>7 Types of Regression Techniques you should know!, <a href="https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/" target="_blank">https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-66405861062473507862016-09-21T20:01:00.000-07:002016-09-21T20:01:06.986-07:00Berbagai Tipe Data dan Skala Pengukuran : Nominal, Ordinal, Interval dan Ratio<b>Status : Draft</b><br />
<b><br /></b>
<b>Referensi</b><br />
<br />
<ol>
<li>Types of Data & Measurement Scales: Nominal, Ordinal, Interval and Ratio, <a href="http://www.mymarketresearchmethods.com/types-of-data-nominal-ordinal-interval-ratio/" target="_blank">http://www.mymarketresearchmethods.com/types-of-data-nominal-ordinal-interval-ratio/</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-39742346592143108812016-09-03T03:51:00.000-07:002016-09-03T06:38:10.479-07:00Berkenalan dengan Item Response Theory (IRT)<center>
<iframe allowfullscreen="" frameborder="0" height="485" marginheight="0" marginwidth="0" scrolling="no" src="//www.slideshare.net/slideshow/embed_code/key/qHGpMrF6ii1wSH" style="border-width: 1px; border: 1px solid #ccc; margin-bottom: 5px; max-width: 100%;" width="595"> </iframe> <div style="margin-bottom: 5px;">
<strong> <a href="https://www.slideshare.net/openthinklabs/introduction-to-item-response-theory" target="_blank" title="Introduction to Item Response Theory">Introduction to Item Response Theory</a> </strong> from <strong><a href="https://www.slideshare.net/openthinklabs" target="_blank">OpenThink Labs</a></strong> <br />
<br />
<h3 style="text-align: left;">
Referensi</h3>
<div style="text-align: left;">
</div>
<ol>
<li style="text-align: left;">IRT for Dummies, <a href="https://prezi.com/rzwf4bgvhn0q/irt-for-dummies/" target="_blank">https://prezi.com/rzwf4bgvhn0q/irt-for-dummies/</a></li>
<li style="text-align: left;">A Non-Technical Approach for Illustrating Item Response Theory, <a href="http://www.slideshare.net/openthinklabs/a-nontechnical-approach-for-illustrating-item-response-theory" target="_blank">http://www.slideshare.net/openthinklabs/a-nontechnical-approach-for-illustrating-item-response-theory</a></li>
<li style="text-align: left;">Item Response Theory Multimedia Tutorial, <a href="http://www.creative-wisdom.com/multimedia/IRTTHA.htm" target="_blank">http://www.creative-wisdom.com/multimedia/IRTTHA.htm</a></li>
<li style="text-align: left;">A Simple Guide to the Item Response Theory (IRT) and Rasch Modeling, <a href="http://www.slideshare.net/openthinklabs/a-simple-guide-to-the-item-response-theory-irt-and-rasch-modeling" target="_blank">http://www.slideshare.net/openthinklabs/a-simple-guide-to-the-item-response-theory-irt-and-rasch-modeling</a></li>
</ol>
</div>
</center>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-15668626052095407512016-09-03T00:32:00.003-07:002019-10-25T09:41:52.960-07:00Menghitung Item analisis klasikal dengan ITEMAN - Assessment Systems CorporationAlhamdulillah, setelah menghitung analisa item menggunakan ITEMAN - Assessment Systems Corporation hasilnya sama dengan hasil yang menggunakan jMetrik [2] dan R - ITEMAN [3]. Berikut hasilnya :<br />
<br />
<script src="https://gist.github.com/wildanm/9ae00517d2823e0600c1d920ea59d19b.js"></script>
<b><br /></b>
<b>Referensi</b><br />
<br />
<ol>
<li>Sample Data untuk input ITEMAN, <a href="https://gist.github.com/wildanm/44ab9e4ad7ec22607841d03929b5b0dc" target="_blank">https://gist.github.com/wildanm/44ab9e4ad7ec22607841d03929b5b0dc</a></li>
<li>Menghitung Item analisis klasikal dengan jMetrik, <a href="http://statistika.openthinklabs.com/2016/09/menghitung-item-analisis-klasikal-dengan-jmetrik.html" target="_blank">http://statistika.openthinklabs.com/2016/09/menghitung-item-analisis-klasikal-dengan-jmetrik.html</a></li>
<li>Menghitung Analisa Item klasikal dengan Paket ITEMAN di R, <a href="http://statistika.openthinklabs.com/2016/09/menghitung-analisa-item-klasikal-dengan-paket-iteman-di-R.html" target="_blank">http://statistika.openthinklabs.com/2016/09/menghitung-analisa-item-klasikal-dengan-paket-iteman-di-R.html</a></li>
<li>Reliability and Validity of the Test, <a href="https://shodhganga.inflibnet.ac.in/bitstream/10603/40163/14/14_chapter%206.pdf" target="_blank">https://shodhganga.inflibnet.ac.in/bitstream/10603/40163/14/14_chapter%206.pdf</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com1tag:blogger.com,1999:blog-562200228697594386.post-89964016717324030452016-09-02T06:47:00.002-07:002019-04-18T14:59:10.418-07:00Menghitung Item analisis klasikal dengan jMetrik<b>Status : Draft</b><br />
<b><br /></b>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhk-4MOU_p0aeZYVUTDnPe6zSSaKV85HLaLJobSbMJG3i2ANBftIFgrLaK-i_pQIPuybwDfUxWTjLvwVEyj6oPavmtsBhEb2IWiIUuwPXxedkSkYvmIxmALt2_JdH4HmD2_NtI751kYjRs/s1600/jmetrik_item_analisis.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="155" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhk-4MOU_p0aeZYVUTDnPe6zSSaKV85HLaLJobSbMJG3i2ANBftIFgrLaK-i_pQIPuybwDfUxWTjLvwVEyj6oPavmtsBhEb2IWiIUuwPXxedkSkYvmIxmALt2_JdH4HmD2_NtI751kYjRs/s320/jmetrik_item_analisis.png" width="320" /></a></div>
Salah satu hal paling merepotkan adalah ketika melakukan setup Basic Item Scoring, ketika mengisi kunci jawaban, harus manual mengetik satu persatu, tidak ada opsi import. Kalau ada ratusan kode soal bisa sangat merepotkan. Harus dicari tahu cara yang lebih efektif untuk melakukan hal ini. Apakah bisa proses nya dilakukan dengan command line ?<br />
<br />
Dibanding ITEMAN ada kelebihan dan kekurangannya, untuk hal ini masih perlu diexplore.<br />
<br />
Contoh ouput analisa item soal dengan jMetrik :<br />
<br />
<script src="https://gist.github.com/wildanm/204d665eba6e45d4d804e1b879b2449c.js"></script>
<h3>
Referensi</h3>
<br />
<ol>
<li>jMetrik, <a href="http://www.jmetrik.com/index.php" target="_blank">http://www.jmetrik.com/index.php</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-58513815750850308622016-09-02T05:49:00.000-07:002016-09-03T00:24:11.198-07:00Menghitung Analisa Item klasikal dengan Paket ITEMAN di R <b>Status : Draft</b><br />
<br />
Berikut adalah output item analysis jika menggunakan paket ITEMAN di R.<br />
<script src="https://gist.github.com/wildanm/6021bb3655bd2185754d2505142110eb.js"></script>
<br />
Grafik per item-nya :<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjs6OWMqVWtguwnv1bC-eLszlbKKzcCb38-hDPKvcmovvJ1tYmyWnCxvxbYCOsL-mmlUpMkAODz6pHD0POAeFNWpbqkZXS8btSnbqwAK4Me9Xa0Ks9mQkNPqIEvpXPd3zl6UFVyeUAKdGM/s1600/Item_Trace_Line_for_the_first_item.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjs6OWMqVWtguwnv1bC-eLszlbKKzcCb38-hDPKvcmovvJ1tYmyWnCxvxbYCOsL-mmlUpMkAODz6pHD0POAeFNWpbqkZXS8btSnbqwAK4Me9Xa0Ks9mQkNPqIEvpXPd3zl6UFVyeUAKdGM/s320/Item_Trace_Line_for_the_first_item.png" width="309" /></a></div>
<br />
<h3>
Referensi</h3>
<ol>
<li>ITEMAN: Classical Item Analysis, <a href="https://cran.r-project.org/web/packages/ITEMAN/index.html" target="_blank">https://cran.r-project.org/web/packages/ITEMAN/index.html</a></li>
</ol>Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0tag:blogger.com,1999:blog-562200228697594386.post-44148490695689614392016-03-24T21:07:00.003-07:002016-03-24T21:07:42.741-07:00Mengenal Rasch Measurement Analysis<b>Status : Draft</b><br />
<b><br /></b>
<b>Referensi</b><br />
<br />
<ol>
<li>Rasch Measurement Analysis Software Directory, <a href="http://www.rasch.org/software.htm" target="_blank">http://www.rasch.org/software.htm</a></li>
</ol>
Wildan Maulanahttp://www.blogger.com/profile/03271713878662854306noreply@blogger.com0