Category Archives: Tips n Trik Statistik

Distribusi Frekuensi

Distribusi Frekuensi

Oleh : Hendry

Distribusi Frekuensi dalam penelitian sering dilakukan untuk memberikan gambaran mengenai alokasi skor yang diperoleh dari data lapangan. Sebuah distribusi frekuensi umumnya digunakan untuk mengkategorikan informasi sehingga dapat diinterpretasikan dengan cepat dengan cara visual. Supaya tidak terlalu panjang, berikut ini diberikan contoh pembuatan distribusi frekuensi secara manual dengan excel.

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Membuat Tabel Chi-Square dengan SPSS

Tabel Chi-Square sebenarnya dapat diperoleh dari buku-buku statistik, tapi untuk sekedar informasi, berikut ini akan diberikan cara bagaimana membuat tabel Chi-Square dengan bantuan SPSS

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MENGAKTIFKAN DATA ANALYSIS TOOLS PADA EXCEL

 

Analisis statistik sebenarnya dapat dilakukan melalui Excel dengan bantuan tool “data analysis”.

Analysis ToolPak adalah Microsoft Office Excel dilakukan instalasi Microsoft Office or Excel. Untuk menggunakannya perlu dilakukan langkah2 berikut ini :

  1. Click the Microsoft Office Button , lalu click Excel Options.
  2.  Click Add-Ins, lalu pada  Manage box, pilih Excel Add-ins.
  3. Click Go.
  4. di dalam  Add-Ins available box, pilih Analysis ToolPak check box, lalu click OK.
  5. Setelah proses loading selesai maka “Data Analysis command” akan tersedia  Data tab

 

Add-Ins

Data Analysis Tools

Jenis-jenis analisis yang dapat digunakan antara lain :

The CORREL and PEARSON worksheet functions both calculate the correlation coefficient between two measurement variables when measurements on each variable are observed for each of N subjects. (Any missing observation for any subject causes that subject to be ignored in the analysis.) The Correlation analysis tool is particularly useful when there are more than two measurement variables for each of N subjects. It provides an output table, a correlation matrix, that shows the value of CORREL (or PEARSON) applied to each possible pair of measurement variables.

The correlation coefficient, like the covariance, is a measure of the extent to which two measurement variables “vary together.” Unlike the covariance, the correlation coefficient is scaled so that its value is independent of the units in which the two measurement variables are expressed. (For example, if the two measurement variables are weight and height, the value of the correlation coefficient is unchanged if weight is converted from pounds to kilograms.) The value of any correlation coefficient must be between -1 and +1 inclusive.

The Correlation and Covariance tools can both be used in the same setting, when you have N different measurement variables observed on a set of individuals. The Correlation and Covariance tools each give an output table, a matrix, that shows the correlation coefficient or covariance, respectively, between each pair of measurement variables. The difference is that correlation coefficients are scaled to lie between -1 and +1 inclusive. Corresponding covariances are not scaled. Both the correlation coefficient and the covariance are measures of the extent to which two variables “vary together.”

The Covariance tool computes the value of the worksheet function COVAR for each pair of measurement variables. (Direct use of COVAR rather than the Covariance tool is a reasonable alternative when there are only two measurement variables, that is, N=2.) The entry on the diagonal of the Covariance tool’s output table in row i, column i is the covariance of the i-th measurement variable with itself. This is just the population variance for that variable, as calculated by the worksheet function VARP.

The Descriptive Statistics analysis tool generates a report of univariate statistics for data in the input range, providing information about the central tendency and variability of your data.

The Exponential Smoothing analysis tool predicts a value that is based on the forecast for the prior period, adjusted for the error in that prior forecast. The tool uses the smoothing constant a, the magnitude of which determines how strongly the forecasts respond to errors in the prior forecast.

The Fourier Analysis tool solves problems in linear systems and analyzes periodic data by using the Fast Fourier Transform (FFT) method to transform data. This tool also supports inverse transformations, in which the inverse of transformed data returns the original data.or example, in a class of 20 students, you can determine the distribution of scores in letter-grade categories.

dst…silahkan baca sendiri di menu “help Excel”

Cara Menghadapi Respon Kosong pada Kuesioner

Cara Menghadapi Respon Kosong pada Kuesioner

by Hendry

Teori Online Tips

Kadang kala kita menemukan banyak responden yang tidak mau mengisi pertanyaan / pernyataan yang diajukan dalam kuesioner yang kita sebarkan. Bagaimana menghadapi masalah ini ?

Ada beberapa pertimbangan yang dijelaskan Uma Sekaran dalam menghadapi masalah ini,

Jika respon kosong mencapai 25% item kuesioner, maka lebih baik kuesioner itu tidak dimasukkan dalam kumpulan data untuk dianalisis

Contoh :

Kuesioner motivasi kerja yang terdiri dari 10 pertanyaan, dan 3 pertanyaan tidak dijawab oleh responden, maka angket itu sebaiknya dibuang.

Pertama. Respon kosong pada skala interval (misal 7 titik), maka dapat diberikan skor tengah (netral)

Kedua. Membiarkan computer mengabaikan respon kosong saat analisis dilakukan.

Ketiga. memberikan pada item nilai keluar respon dari semua yang merespon item tersebut

Keempat. Memberi item tersebut rata-rata respon dari responden khusus pada semua pertanyaan lain yang mengukur variabel tersebut

Kelima. Memberikan respon kosong sebuah angka acak dalam skala yang digunakan.

Dari lima cara yang dikemukakan di atas, pendekatan umum yang digunakan adalah memberikan angka nilai tengah dalam skala sebagai nilai atau mengabaikan item tersebut dalam proses analisis

Cara terbaik menangani data yang hilang untuk meningkatkan validitas penelitian (khususnya sampel besar) adalah mengabaikan kasus dimana data yang berkaitan dengan analisis tertentu hilang.

 

Referensi :

Uma Sekaran. 2006. Metode Penelitian Bisnis. Jakarta : Salemba empat. pp. 170 – 171

 

The Top Ten Myths and Urban Legends about Likert scales

Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes

 James Carifio and Rocco J. Perla. Journal of Social Sciences 3 (3): 106-116, 2007

Myth 1There is no need to distinguish between a scale and response format; they are basically the same “thing” and what is true about one is true about the other.

Myth 2—Scale items are independent and autonomous with no underlying conceptual, logical or empirical structure that brings them together and synthesizes them.

Myth 3—Likert scales imply Likert response formats and vice versa as they are isomorphic.

Myth 4Likert scales cannot be differentiated into macro and micro conceptual structures.

Myth 5—Likert scale items should be analyzed separately.

Myth 6—Because Likert scales are ordinal-level scales, only non-parametric statistical tests should be used with them.

Myth 7—Likert scales are empirical and mathematical tools with no underlying and deep meaning and structure.

Myth 8—Likert response formats can without impunity be detached from the Liker Scale and its underlying conceptual and logical structure.

Myth 9—The Likert response format is not a system or process for capturing and coding information the stimulus questions elicit about the underlying construct being measured.

Myth 10—Little care, knowledge, insight and understanding is needed to construct or use a Likert scale.

Artikel komplit dapat didonwload disini

 

Membuat Tabel t dengan SPSS

Membuat Tabel t dengan SPSS versi 15.0

Umumnya tabel-tabel statistik seperti t, R, chi-square, F diperoleh melalui buku-buku. Namun sebenarnya pembuatan tabel tersebut dapat dilakukan dengan mudah melalui program SPSS.

Berikut ini tahapan pembuatannya yang saya buat dengan  SPSS versi 15.0

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