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BAB III METODE PENELITIAN 3.1 Pendekatan Penelitian Mengacu pada rumusan masalah yang telah ditetapkan pada bab sebelumnya, maka penelitian ini menggunakan metode kuantitatif untuk menguji dan membuktikan hipotesis yang telah dibuat melalui berbagai tes dan pengolahan data. BAB III METODE PENELITIAN 3.1 Metode Penelitian yang Digunakan Metode penelitian dirancang melalui langkah-langkah penelitian dari mulai operasionalisasi variabel, penentuan jenis dan sumber data, metode. Kuantitatif menggunakan analisis statistik. Sedangkan teknik ukuran yang digunakan yaitu teknik Skala Likert atau skala sikap. Sub-sub untuk penelitian Kuantitatif BAB III. METODOLOGI PENELITIAN 3.1 Objek dan Ruang Lingkup Penelitian 3.2 Waktu dan Tempat Penelitian 3.3 Metode Penelitian 3.4 Variabel Penelitian dan Pengukurannya 3.5 Populasi dan Sampel 3.6 Prosedur Pengumpulan Data 3.7 Metode Analisis Sub-sub untuk penelitian Kualitatif BAB III. METODOLOGI PENELITIAN.



2 BRAINSTORMING Apa yang telah saudara dapatkan dari kuliah ini? Tell to us… Whats ur problems so far? Literature? Lazy? Lecturer ? Knowledge?

3 Berapa tugas yang sudah dan belum saudara kumpulkan ?  Kuisioner  Proposal penelitian (due next week) Sudah siapkah saudara untuk FINAL Exam ?  Berapa kali saudara “bimbingan” ?

4 BAB III METODE PENELITIAN Pendekatan dan design Penelitian  Pendekatan  Jenis,  Strategi dan  Tipe Penelitian Lokasi Penelitian Populasi dan Sampel  Populasi  Teknik Penarikan Sampel  Besaran Sampel Unit Analisis Teknik Pengumpulan Data  Jenis Data  Teknik Pengumpulan Data Primer dan Sekunder Validitas dan Reabilitas Data Analisis Data Pengujian Hipotesis Struktur Penulisan

5 True or False? Analisis yang kompleks membuat orang lain kagum. Saya secara umum mampu untuk menggeneralisir dan menginterpretasi data yang saya dapatkan

6 Analisis data dan interpretasi Pikir tentang analisis LEBIH AWAL Start dengan a plan Code, enter, clean Analisis Interpretasi Refleksi  What did we learn?  What conclusions can we draw?  What are our recommendations?  What are the limitations of our analysis?

7 kenapa saya membutuhkan analysis plan To make sure the questions and your data collection instrument will get the information you want. To align your desired “report” with the results of analysis and interpretation. To improve reliability--consistent measures over time.

8 Key components of a data analysis plan Purpose of the evaluation Questions What you hope to learn from the question Analysis technique How data will be presented

9 Analyzing and Interpreting Quantitative Data Quantitative Data is Presented in a numerical format Collected in a standardized manner e.g. surveys, closed-ended interviews, tests Analyzed using statistical techniques

10 True or False? Quantitative data we gather in Extension are more generalizable than qualitative data. Stating limitations weakens the evaluation

11 Analyzing Survey Data Apa yang akan anda laporkan… how many people answered a, b, c, d? the average number or score? a change in score between two points in time? how people compared? how many people reached a certain level?

12 Common descriptive statistics Count (frequencies) Percentage Mean Mode Median Range Standard deviation Variance Ranking

13 Other Statistics Statistical Significance Factor Analysis Etc. Not often used in Extension program evaluation—generally require randomization, large samples, and/or control groups

14 Getting your data ready Assign a unique identifier Organize and keep all forms (questionnaires, interviews, testimonials) Check for completeness and accuracy Remove those that are incomplete or do not make sense

15 Data entry You can enter your data  By hand  By computer http://learningstore.uwex.edu/Using-Excel-for- Analyzing-Survey-Questionnaires- P1030C0.aspx http://learningstore.uwex.edu/Using-Excel-for- Analyzing-Survey-Questionnaires- P1030C0.aspx

16 Data entry by computer By Computer  Excel (spreadsheet)  Microsoft Access (database mngt)  Quantitative analysis: SPSS (statistical software)

17 Data entry computer screen Survey ID Q1 Do you smoke? Q2 AgeQ3 Support ordinance? 0011242 0021182 0032361 0042481 0051261 Smoking: 1 (YES) 2 (NO)

18 Dig deeper Did different groups show different results? Were there findings that surprised you? Are there things you don’t understand very well – further study needed?

19 Supports restaurant ordinance Opposes restaurant ordinance Undecided/ declined to comment Current smokers (n=55) 8 (15% of smokers) 33 (60% of smokers) 14 (25% of smokers) Non-smokers (n=200) 170 (86% of non- smokers) 16 (8% of non- smokers) 12 (6% of non- smokers) Total (N=255) 178 (70% of all respondents) 49 (19% of all respondents) 26 (11% of all respondents)

20 Discussing limitations Written reports: Be explicit about your limitations Oral reports: Be prepared to discuss limitations Be honest about limitations Know the claims you cannot make  Do not claim causation without a true experimental design  Do not generalize to the population without random sample and quality administration (e.g., <60% response rate on a survey)

21 PENGUJIAN HIPOTESIS Penentuan rumus untuk pengujian hipotesis jenis-jenis pengujian hipotesis.

22 Summarizing Data Data are a bunch of values of one or more variables. A variable is something that has different values.  Values can be numbers or names, depending on the variable: Numeric, e.g. weight Counting, e.g. number of injuries Ordinal, e.g. competitive level (values are numbers/names) Nominal, e.g. sex (values are names  When values are numbers, visualize the distribution of all values in stem and leaf plots or in a frequency histogram. Can also use normal probability plots to visualize how well the values fit a normal distribution.  When values are names, visualize the frequency of each value with a pie chart or a just a list of values and frequencies.

23 A statistic is a number summarizing a bunch of values.  Simple or univariate statistics summarize values of one variable.  Effect or outcome statistics summarize the relationship between values of two or more variables. Simple statistics for numeric variables…  Mean: the average  Standard deviation: the typical variation  Standard error of the mean: the typical variation in the mean with repeated sampling Multiply by  (sample size) to convert to standard deviation.  Use these also for counting and ordinal variables.  Use median (middle value or 50th percentile) and quartiles (25th and 75th percentiles) for grossly non-normally distributed data.  Summarize these and other simple statistics visually with box and whisker plots.

24 Simple statistics for nominal variables  Frequencies, proportions, or odds.  Can also use these for ordinal variables. Effect statistics…  Derived from statistical model (equation) of the form Y (dependent) vs X (predictor or independent).  Depend on type of Y and X. Main ones: YXEffect statisticsModel/Testnumeric slope, intercept, correlationregressionnumericnominal numeric mean difference frequency difference or ratio frequency ratio per… t test, ANOVA chi-square categorical

25 Model: numeric vs numeric e.g. body fat vs sum of skinfolds  Model or test: linear regression  Effect statistics: slope and intercept = parameters correlation coefficient or variance explained (= 100·correlation 2 ) = measures of goodness of fit  Other statistics: typical or standard error of the estimate = residual error = best measure of validity (with criterion variable on the Y axis) sum skinfolds (mm) body fat (%BM)

26 Model: numeric vs nominal e.g. strength vs sex  Model or test: t test (2 groups) 1-way ANOVA (>2 groups)  Effect statistics: difference between means expressed as raw difference, percent difference, or fraction of the root mean square error (Cohen's effect-size statistic) variance explained or better  (variance explained/100) = measures of goodness of fit  Other statistics: root mean square error = average standard deviation of the two groups femalemale strength sex

27 More on expressing the magnitude of the effect  What often matters is the difference between means relative to the standard deviation: strength females males Trivial effect: strength females males Very large effect:

28  Fraction or multiple of a standard deviation is known as the effect-size statistic (or Cohen's 'd').  Cohen suggested thresholds for correlations and effect sizes.  Hopkins agrees with the thresholds for correlations but suggests others for the effect size: trivialsmallmoderatelarge very large !!! Hopkins: CorrelationsCohen: 0.2Hopkins:  Effect Sizes 0.2Cohen:0.50.80  For studies of athletic performance, percent differences or changes in the mean are better than Cohen effect sizes.

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29 Model: numeric vs nominal (repeated measures) e.g. strength vs trial  Model or test: paired t test (2 trials) repeated-measures ANOVA with one within-subject factor (>2 trials)  Effect statistics: change in mean expressed as raw change, percent change, or fraction of the pre standard deviation  Other statistics: within-subject standard deviation (not visible on above plot) = typical error: conveys error of measurement  useful to gauge reliability, individual responses, and magnitude of effects (for measures of athletic performance). prepost strength trial

30 Model: nominal vs nominal e.g. sport vs sex  Model or test: chi-squared test or contingency table  Effect statistics: Relative frequencies, expressed as a difference in frequencies, ratio of frequencies (relative risk), or ratio of odds (odds ratio) Relative risk is appropriate for cross-sectional or prospective designs.  risk of having rugby disease for males relative to females is (75/100)/(30/100) = 2.5 Odds ratio is appropriate for case-control designs.  calculated as (75/25)/(30/70) = 7.0 femalesmales 30% 75% rugby yes rugby no

31 Model: nominal vs numeric e.g. heart disease vs age  Model or test: categorical modeling  Effect statistics: relative risk or odds ratio per unit of the numeric variable (e.g., 2.3 per decade) Model: ordinal or counts vs whatever  Can sometimes be analyzed as numeric variables using regression or t tests  Otherwise logistic regression or generalized linear modeling Complex models  Most reducible to t tests, regression, or relative frequencies.  Example… age (y) heart disease (%) 00 100100 303050507070

32 Model: controlled trial (numeric vs 2 nominals) e.g. strength vs trial vs group  Model or test: unpaired t test of change scores (2 trials, 2 groups) repeated-measures ANOVA with within- and between-subject factors (>2 trials or groups) Note: use line diagram, not bar graph, for repeated measures.  Effect statistics: difference in change in mean expressed as raw difference, percent difference, or fraction of the pre standard deviation  Other statistics: standard deviation representing individual responses (derived from within-subject standard deviations in the two groups) prepost strength trial drug placebo

33 Model: extra predictor variable to 'control for something' e.g. heart disease vs physical activity vs age  Can't reduce to anything simpler.  Model or test: multiple linear regression or analysis of covariance (ANCOVA) Equivalent to the effect of physical activity with everyone at the same age. Reduction in the effect of physical activity on disease when age is included implies age is at least partly the reason or mechanism for the effect. Same analysis gives the effect of age with everyone at same level of physical activity.  Can use special analysis (mixed modeling) to include a mechanism variable in a repeated-measures model. See separate presentation at newstats.org.

34 Problem: some models don't fit uniformly for different subjects  That is, between- or within-subject standard deviations differ between some subjects.  Equivalently, the residuals are non-uniform (have different standard deviations for different subjects).  Determine by examining standard deviations or plots of residuals vs predicteds.  Non-uniformity makes p values and confidence limits wrong.  How to fix… Use unpaired t test for groups with unequal variances, or… Try taking log of dependent variable before analyzing, or… Find some other transformation. As a last resort… Use rank transformation: convert dependent variable to ranks before analyzing (= non-parametric analysis–same as Wilcoxon, Kruskal-Wallis and other tests).

35 Generalizing from a Sample to a Population You study a sample to find out about the population. The value of a statistic for a sample is only an estimate of the true (population) value. Express precision or uncertainty in true value using 95% confidence limits.  Confidence limits represent likely range of the true value.  They do NOT represent a range of values in different subjects.  There's a 5% chance the true value is outside the 95% confidence interval: the Type 0 error rate. Interpret the observed value and the confidence limits as clinically or practically beneficial, trivial, or harmful.  Even better, work out the probability that the effect is clinically or practically beneficial/trivial/harmful. See sportsci.org.

36 Statistical significance is an old-fashioned way of generalizing, based on testing whether the true value could be zero or null.  Assume the null hypothesis : that the true value is zero (null).  If your observed value falls in a region of extreme values that would occur only 5% of the time, you reject the null hypothesis.  That is, you decide that the true value is unlikely to be zero; you can state that the result is statistically significant at the 5% level.  If the observed value does not fall in the 5% unlikely region, most people mistakenly accept the null hypothesis: they conclude that the true value is zero or null!  The p value helps you decide whether your result falls in the unlikely region. If p<0.05, your result is in the unlikely region.

37  One meaning of the p value: the probability of a more extreme observed value (positive or negative) when true value is zero.  Better meaning of the p value: if you observe a positive effect, 1 - p/2 is the chance the true value is positive, and p/2 is the chance the true value is negative. Ditto for a negative effect. Example: you observe a 1.5% enhancement of performance (p=0.08). Therefore there is a 96% chance that the true effect is any 'enhancement' and a 4% chance that the true effect is any 'impairment'. This interpretation does not take into account trivial enhancements and impairments.  Therefore, if you must use p values, show exact values, not p 0.05. Meta-analysts also need the exact p value (or confidence limits).

38  If the true value is zero, there's a 5% chance of getting statistical significance: the Type I error rate, or rate of false positives or false alarms.  There's also a chance that the smallest worthwhile true value will produce an observed value that is not statistically significant: the Type II error rate, or rate of false negatives or failed alarms. In the old-fashioned approach to research design, you are supposed to have enough subjects to make a Type II error rate of 20%: that is, your study is supposed to have a power of 80% to detect the smallest worthwhile effect.  If you look at lots of effects in a study, there's an increased chance being wrong about at least one of them. Old-fashioned statisticians like to control this inflation of the Type I error rate within an ANOVA to make sure the increased chance is kept to 5%. This approach is misguided.

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39 The standard error of the mean (typical variation in the mean from sample to sample) can convey statistical significance.  Non-overlap of the error bars of two groups implies a statistically significant difference, but only for groups of equal size (e.g. males vs females).  In particular, non-overlap does NOT convey statistical significance in experiments: what- ever postpre High reliability p = 0.003 postpre Mean ± SEM in both cases postpre Low reliability p = 0.2

40 In summary  If you must use statistical significance, show exact p values.  Better still, show confidence limits instead.  NEVER show the standard error of the mean!  Show the usual between-subject standard deviation to convey the spread between subjects. In population studies, this standard deviation helps convey magnitude of differences or changes in the mean.  In interventions, show also the within-subject standard deviation (the typical error) to convey precision of measurement. In athlete studies, this standard deviation helps convey magnitude of differences or changes in mean performance.

41 PENENTUAN RUMUS Apa yang harus diperhatikan ketika menentukan rumus yang akan saudara untuk melakukan uji hipotesis ? Apakah semua penelitian HARUS melakukan pengujian hipotesis ? Bagaimana dengan penelitian dengan satu variabel ?

42 YANG HARUS DIPERHATIKAN Jenis hipotesis yang dibangun,  Deskriptif  Asosiatif  Komparatif Jenis skala pengukuran yang dipergunakan,  Nominal, ordinal, interval, rasio. Variasi populasi  homogen atau heterogen.

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43 Macam DataBentuk Hipotesis Deskriptif (satu variabel) Komparatif (dua sampel)Komparatif (lebih dari 2 sampel) Asosiatif (hubungan) RelatedIndependenRelatedIndependen Nominal Binomial  2 One Sample Mc NemarFisher Exact Probability  2 Two Sample  2 for k sample Cochran Q  2 for k sample Contingency Coefficient C Ordinal Run Test Sign test Wilcoxon matched parts Median test Mann-Whitney U test Kolmogorov Simrnov Wald- Woldfowitz Friedman Two Way- Anova Median Extension Kruskal-Wallis One Way Anova Spearman Rank Correlation Kendall Tau Interval Rasio T Test* T-test of* Related T-test of* independent One-Way Anova* Two Way Anova* One-Way Anova* Two Way Anova* Pearson Product Moment * Partial Correlation* Multiple Correlation*

44 (6) Uji statistik (statistical testing). Tahapan ini menguji hipotesis yang dibangun oleh peneliti. Tes signifikansi merupakan nama lain dari uji statistik ini. Tes signifikansi bertujuan untuk mendapatkan bukti empiris dalam melakukan generalisasi terhadap populasi dari mana data diambil.