# Exploring the Recent Trend Shift in Marketing Research: Social Media (Big) Data and its Sampling Property

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7 7 Appendix 1 Figure 1 Big Data from Social Media Landscapes Figure 2 Techniques Convenience Non probabilistic or non random sampling techniques Purposive Quota Techniques Snowball Simple Random Probabilistic or random sampling techniques Stratified Random Systematic Random Cluster Random

8 8 Figure 3 Types of purposive sampling techniques Purposive Maximum Variation Extreme Case Theoretical Total Population Figure 4 Twitter threads ( customer satisfaction is important )

9 9 Figure 5 Maximum Variation Respondents MVS Income Gender Education Geography Lower Income Respondents Male Respondents High School Graduates Urban Respondents Middle Income Respondents Female Respondents UG Degrees City Respondents Higher Income Respondents PG Degrees Village Respondents Higher Degrees Figure 6 in Input-Process-Output (IPO) INPUT Samples PROCESS Qualitative Analysis Samples (Grounded Theory) OUTPUT Research Findings Reports Table 1 Systematic Random (N=500; n=25 and k=25) Respondent No. Respondent No. Respondent No. Respondent No. Respondent No

10 10 Appendix 2 Equation 1 E( x) ( x) where E stands for expected values, x for sample means, µ for population means, for standard deviation of the population, s for standard deviation of the samples, n for number of observations or samples, and superscript bar for average values. Equation 2 E( s ) ( x) n where E stands for expected values, x for sample means, µ for population means, for standard deviation of the population, s for standard deviation of the samples, n for number of observations or samples, and superscript bar for average values. Equation MSE( x) E[ x E( x) ] E[ x X ] Var( x) Bias ( x) Where MSE stands for Mean Square Errors, E for expected values, x for sample values, X for population values and Var for variance. Equation 4 The total and mean and the variance of the observations of the gth stratum can be given by: N g xg X g xgi and xg N 1 g Where G stands for strata of the population, N for the number of strata, the theoretical observation of the strata can be represented by x gi, g = 1, 2, 3..G and i = 1, 2, 3,..N g. Equation 5 S N g 2 1 g ( x X ) gi N g 1 g 2 Where G stands for strata of the population, N for the number of strata, the theoretical observation of the strata can be represented by x gi, g = 1, 2, 3..G and i = 1, 2, 3,..N g. Equation 6 r, r k, r 2 k, r 3 k, r 4 k, r 5 k,..., r ( n 1) k where r stands for the first respondent selected by RNG, N for the population size, n for the number of respondents required, and k for the sampling interval. k must be an integer. If k is not an integer, the solution is to round k to an integer to produce n respondents.

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