Truong Thi Hoa, Ph D 1 1 Preparing data for analysis 2 Descriptive statistics 3 Inferential statistics 4 Testing the measurements 5 Testing research model and hypotheses 2 Inferential statistics take[.]
Truong Thi Hoa, Ph.D 1 Preparing data for analysis Descriptive statistics Inferential statistics Testing the measurements Testing research model and hypotheses Inferential statistics: take data from samples and make generalization about a population Two main areas ØParameter estimation ØHypothesis testing Hypothesis testing § State your research hypothesis alternate hypothesis (Ha or H1) § Set as a null hypothesis (Ho) and the significance level (α) § Perform an appropriate statistical test § Define the critical value (z-statistics or t-statistics) § Draw the conclusion Source: Nguyen Dinh Tho (2012) Hypothesis testing Null hypothesis (H0) -No statistical relationship or significance between variables -Assumed to be true until there is evidence to suggest otherwise Alternative hypothesis (H1) -The initial hypothesis that predicts a relationship between variables (the research analysis) Two-tail test ìH : q = q o ợ H1 : q q One-tail test ìH : q ³ q o í îH : q < q ìH : q £ q o í ỵH : q > q (−𝑍! ) (𝑍! ) (−𝑍! ) " P-value: Significance level P-value 𝛼: Fail to reject H0 (𝑍! ) " Hypothesis testing with SPSS Ordinal and nominal variable Ø testing if there exist associations between variables Testing relationship of nominal-nominal variables and nominal-ordinal variable (Chi-square test) Testing relationship of ordinal-ordinal variable (gamma, Sommers’d, Kendall’s tau-d) Numerical variables ØTesting mean differences -One sample -Two independent groups (two samples) -Paired data ØANOVA test Hypothesis testing with SPSS Ordinal and nominal variable Ø testing if there exist associations between variables Ho: there is no associations between variables H1 (Ha): There is an association between two variables Ø computing Chi-squared ØDefining critical Chi-squared value: c2df,α ØComparing c2 and c2df,α If c2 > c2df,α or p-value (sig) < 𝛼 → Reject H0; the evidence favors H1 Working with SPSS nominal-ordinal variables Analyze → Descriptive Statistics → Crosstabs For example: open file thongke.sav Ha: There is an association between gender and educational levels Working with SPSS (nominal-ordinal variables) P-value > α (0.05) Fail to reject H0 èDo not have the evidence to conclude there is an association between gender and educational level Test the following hypothesis H1: Tồn mối liên hệ trình độ học vấn cách đọc báo -> ? 10 Testing relationship of two ordinal variables P-value < α (0.05) Reject H0 èhave the evidence supports that there is an association between educational level group and age group 13 Numerical variables ØTesting Ø mean differences (one sample) Hypotheses Ho: 𝜇 = 𝜇! H1 (Ha): 𝜇 ≠ 𝜇! Ø one sample t-test ØIf p-value (sig) < 𝛼 → Reject H0; the evidence favors H1 14 Numerical variables Testing mean difference: one sample Analyze -> Compare Means -> One-Sample T Test Example: H0: Tri trung binh cua tuoi=28; H1: Trị trung bình tuoi ≠ 28 15 Numerical variables Testing mean difference: one sample P-value < α (0.05) Reject H0 →Accept Ha 16 Numerical variables Testing mean differences of two independent groups Ø Hypotheses H0: µ1 = µ2 (the means of two groups are equal) H1: µ1 ≠ µ2 (the means of two groups are not equal) Øindependence t-test Levene’s test for equality of variance H0: σ12 - σ22 = 0 (the variances of group and are equal) H1: σ12 - σ22 ≠ 0 (the variances of group and are not equal) ØIf p-value (sig) < 𝛼 → Reject H0; the evidence favors H1 17 Testing mean differences of two independent groups Analyze -> Compare Means -> Independent-Sample T Test 18 Numerical variables Testing mean differences of two independent groups Analyze -> Compare Means -> Independent-Sample T Test 19 Numerical variables Testing mean differences of paired data Data from the same individuals, objects (exp: two time points) Ø Hypotheses H0: µa = µb ("the paired means are equal") H1: µa ≠ µb ("the paired means are not equal") Ø Paired-sample t-test ØIf p-value (sig) < 𝛼 → Reject H0; the evidence favors H1 20 Numerical variables Testing mean difference: Paired data Analyze -> Compare Means -> Paired-sample T Test Using testpaired.sav 21 Numerical variables Testing mean difference: Paired data significantly positive correlation 22 Numerical variables ØTesting Ø mean differences between several groups Hypotheses H0: µ1 = µ2 = µ3 = = µk (The means of all k groups are equal) H1: At least one µi different (At least one of the means is not equal to the others") Ø ANOVA test ØIf p-value (sig) < 𝛼 → Reject H0; the evidence favors H1 ØPost-hoc test 23 ANOVA test Analyze -> Compare Means -> One-way ANOVA Using thongke.sav 24 ANOVA test Fail to reject H0 -> No further tests 25 ANOVA test reject H0 -> Post-hoc test Nhóm có học vấn cấp 1-2 có tự cá nhân cao nhóm có học vấn ĐH; Nhóm có học vấn TH-CĐ có tự cá nhân cao nhóm có học vấn ĐH 26 Hồng Trọng Chu Nguyễn Mộng Ngọc (2008) Phân tích liệu nghiên cứu với SPSS NXB Hồng Đức Nguyễn Đình Thọ (2009) Phương pháp nghiên cứu khoa học kinh doanh—Thiết kế thực NXB Lao động Xã hội 27