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Management Solutions Dr. Sheikh Farhan Ashraf Post Doctorate, School of Management, Jiangsu University, China 

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LEARNING QUANTITATIVE TECHNIQUES THROUGH SPSS & PLS-SEM 

Theory and Practice Outlines: Understanding Sampling Techniques & Questionnaire Design Use of SPSS Using PLS-SEM    

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Recommended Books: Hair, Mary, Money, Samouel, Page (2016). Essentials of Business Research Methods (3rd Ed.). Routledge. Hair, J. F., Black, W. C., Babin, B. J., & Anderson,  R. E. . (2010). Multivariate data analysis (7th ed.). Upper Saddle River: NJ: Prentice Hall. Pallant, Julie. (2011).  Spss survival manaul: A step by step guide to data analysis using  spss for windows 4rth edition. Berkshire, UK: Open University Press:  McGraw-Hill Education. Field, (2013) Discovering Statistics Using  IBM SPSS Statistics (4th Ed.). Sage Tabachnick, B.G., & Fidell, L.S. (2007). Using multivariate statistics (5 ed.). Boston: Pearson Education.  Hair, Hult, Ringle, Sarstedt. (2016) A premier on partial least squares structural equation modeling (PLS-SEM) (2nd Ed.). Sage  

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KEY POINTS IN CHOOSING TECHNIQUE AND  TARGET POPULATION 

Sampling approaches and considerations Methods of collecting primary data Measurement and scaling Questionnaire design 

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Census Researchers should focus  target population efficiently. Sample Sample size is drawn by using  Probability or Non-Probability Sampling Technique. Is a relatively small subset of the population.  The use of sampling design is necessary in selecting  the sample.  

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Probability Sampling is  typically used in Quantitative Research  Selection of representative sample from the target population using  a random sampling technique is  to ensure the objectivity in selecting a sample. The findings from sample data generalize  to the population with a specified degree of accuracy. Non-Probability Sampling is  typically used in Qualitative Research Judgment is used to select  the sample in qualitative research. Findings from the sample can be  used to describe, discover, and develop theory (e.g.,  Grounded Theory). While the findings can be  used to generalize to the population,  it cannot be done with a specified degree of accuracy.  

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Sampling is the part of basic research process and  it involves following key points: Sample size is an important consideration in both  Qualitative and Quantitative Research Mostly large number of sample size is  preferable and required. In contrast, when using  a qualitative approach to develop  theory on organizational behavior a small number of cases may be  sufficient. (Example) Now the Questions Arises!!!! Should a sample or a census be used? If a sample, then which sampling approach is best? And, how and why a large number of sample is  necessary? The main objective is to minimize the sampling error 

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We should focus our target Population very carefully by focusing  which and who is your target population with better knowledge.  We refer/prefer to the group of knowledgeable people as our Target Population or  Universe. A Population is considered  as the total of all elements that share  a common characteristics. E.g. people, supermarkets, hospitals 

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In probability sampling researchers are  able at a minimum to calculate  the error associated with a particular sampling design and  can make decisions with this knowledge in hand. In non-probability sampling researchers are  not able to calculate the error but  have made informed judgments in an effort to obtain  usable sample information. Remember, a sample must be  a representative of the population from which it is  drawn or set for data collection.  Only, the use of an appropriate sampling design can achieve  this objective.  

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Defining the target population  Choosing the sampling frame Selecting the sampling method Implementing the sampling plan 

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Defining the target population  The research objectives and scope of the study are  critical in defining the target population. Target population is complete group of objects or  elements relevant to the research project.  Elements or objects available for selection during the sampling process are  known as sampling unit (people,  households, businesses,  or any logically unit relevant to the study's objectives). Element 		Employees with incentive pay Sampling unit	Customer service representatives and  branch managers Extent 		All branch located in Punjab province Time			June 2020  

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Choosing the Sampling Frame Sampling frame is a comprehensive list of the elements from which the sample is drawn. e.g., a telephone directory,  company's internal database, electronic directories. A sampling frame is an accurate,  complete listing of all the elements in the population targeted. In reality, sampling frame is  often flawed in a number of ways: 1.    It may not be up-to-date  It may include elements that do not belong  to the target population It may not include elements that do belong  to the target population It may have been compiled from multiple lists and  contain duplicate elements as a result of the manner in which the list was  constructed. 

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 Before drawing a sample from the sampling frame list,  the researcher must therefore confirm  the list's accuracy irrespective of its origin. Selecting Sampling Method Depends on number of theoretical and practical issues. Which include: Considering the nature of the study The objectives of the study And the time and budget availability  

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SAMPLING METHODS PROBABILITY			NON-PROBABILITY 

Simple Random			Convenience  Systematic 				Judgment Stratified 				Snowball/Referral Cluster 				Quota Multistage 

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Simple random:  	is a straightforward  Assigns each element of target population an equal probability of being  selected. Drawing names from a hat Easy (drawing names from hat) with a small population Large target population;  other approaches are necessary One example of simple random sampling is  random digital dialing in a telephone survey Issues: nonworking numbers,  refusal to answer a telephone,  lack of time/interest etc  

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Sequentially assign a unique identification number to each element in the sampling frame Use a random number generator to identify  the appropriate elements to be selected in the sample Ensure that no element is selected more than once. 

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2) Systematic sampling  Involves selecting an initial starting point on a list,  and thereafter every nth element in the sampling frame is  selected. Suppose, you have a list of 10,000 students attending  a particular university and  you want a sample of 500 students.  

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Your sampling objective is  a representative cross-section of the student body. To draw the sample you must determine  the sample size and then calculate  the sampling interval.  Sampling interval is the number of population elements between each unit selected  for your sample. 

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In this case, the sampling interval is 20  (10,000 students / sample of 500 = 20) 7, 27,47,67 and so on. 

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Systematic sampling produces  representative data if executed properly. 

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Photo by Matthijs van Heerikhuize on Unsplash

To work properly, the sampling interval must divide  the sampling frame into relatively homogeneous groups,  if there is a cyclic sequence to the sampling frame instead of a random sequence,  systematic sampling will not work. 

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For instance, Alphabetical listings are  considered random not cyclic. In contrast, if we wanted  to do weekly interviews with our customers and  our interval was 7, the sample would produce  biased information, because we would always interview  on the same day of the week. To be truly random, we must conduct  interviews across at least several different days of the week. 

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Lets say, if our list of customers is  1030 arranged according to frequency of dinning,  and the first 100 names on the list eat  at our restaurant at least once a week and  the remaining 930 eat at our restaurant an average of four times a year,  we would have a problem using systematic sampling. If the sampling interval is 10 and our sample size is  103, then our sample may underrepresent  the frequent customers (only 10 frequent customers) and  overrepresent the less frequent customers (93 nonfrequent customers) 

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  Thus, we must know ahead of time if there are underlying  systematic patterns in the data so we can account  for them in our sampling plan. 

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Requires the research to partition  the sampling frame into relatively homogeneous subgroups  that are  distinct and nonoverlapping, called strata.   

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The researcher usually does  the stratification on the basis of some predetermined criteria that may be  the result of his/her past experience,  or stratification could even be specified by the client. In restaurant example, the researcher may wish  to stratify customers on the basis of characteristics such as age,  marital status, family size,  income level, frequency of eating  out, level of satisfaction, who selected the restaurant,  or a combination of these.  

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The researcher determines the total sample size as well as the required  sample sizes for each of individual strata. For example, total sample size might be  400 and the four individual strata might each have  a sample size of 100. 

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The stratified sample is  the composite of the samples taken from the strata. Elements for the stratified sample are  usually selected either by drawing  simple random or systematic samples of the specified  size from the strata of the target population. With stratified sampling, elements must be selected  from all the strata of the total sample. 

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 Practically, stratified sample is  selected in one of two ways;  Proportionately or disproportionately.   

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In Proportionately stratified sampling,  the overall sample size will be  the total of all the elements from each of the strata. The number of elements chosen  from each stratum is proportionate to the size of a particular stratum relative to the overall sample size. 

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So if we have a stratum that is  25 percent of the target population,  then the size of the sample for that stratum will be  25 percent of the total sample. 

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For example, if we use proportionately stratified  sampling to select a sample of males and  females at IBA with 10,000 student,  and 6,000 students are females and 4,000 students are  males, then the overall sample would include  60 percent females and 40 percent males. 

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In disproportionately stratified  sampling the sample elements are  chosen in one of the two ways; One approach involves choosing  the elements from each stratum according  to its relative importance. Relative importance is usually based  on practical considerations such as economic importance of the various strata. 

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For example, if that restaurant is  located in an area dominated  by older individuals who dine  out less frequently, then sampling  a high proportion of younger customers that dine  out more often would be  viewed as more important to him. 

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With disproportionately stratified  sampling based on economic or  other reasons, the sample size from each stratum is  determined independently without considering  the size of the stratum relative to the overall sample size. The more important a particular stratum is  considered, the higher will be  the proportion of the sample elements from the stratum. 

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Another approach to selecting  a disproportionately stratified  sample considers the variability of the data within each stratum. Elements from each stratum are  selected based on the relative variability of the elements. Strata with high relative variability will contribute  a higher proportion of elements to the sample.  Similarly, lower the variability of a stratum the lower will be  its proportional representation in the total sample. 

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For example, assume IBA with 10,000 students has  50 percent male students and  50 percent female students. We know that almost all the male drink beer,  and there is wide variation in beer drinking habits,  with some drinking beer every day and  a very small number not drinking beer at all.  On the other hand, only a small proportion of the female students drink  beer and not very often (they prefer  fresh juices), so there is  not much variation in their beer consumption patterns. In this example, we would sample  a larger number of male students in our survey so we could more accurately represent  male beer consumption patterns,  Since female students do not vary  much in their beer consumption habits,  the smaller sample of females should still accurately represent  their behavior. 

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In cluster sampling, the target population is  viewed as being made up of heterogeneous groups, Examples of cluster are ethnic groups, companies,  households, business units, and geographic areas.  

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the most frequently used type of cluster sampling is  geographic area sampling. For example, assume you want  to interview managers of banks in Sindh province. The research could obtain  a list of zip codes in which banks are  located: each area is then a cluster. The cluster to be sampled would be randomly selected , and then all bank managers or  a random sample of managers of banks would be  interviewed in each of the selected clusters. This process generally works well with and produces  representative data. 

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Define the cluster characteristics in a way that ensures  clusters are unambiguously identified  in the target population.  In this manner, the total number of clusters in the population will be  known ahead of time. Decide on how many clusters to sample. Choose the cluster(s) in a random manner Obtain a sampling frame for the chosen clusters Decide whether to conduct a census on the chosen  cluster(s)  or whether to take  a probability sample from the cluster(s). If a probability sample is  desired, determine the total sample size. If more than one cluster will be  used, then the sample size should be  allocated appropriately. This is generally done on proportionate sampling basis. 

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Involves a sequence of stages. These stages are illustrated by the following example. The problem is to investigate  the views of medical practitioners in the US concerning  the use of medical software to assist  in patient diagnosis. The first stage is to select  a random sample of regions in the US. The regions are clusters. The Second stage is to select  a random sample of hospitals from the selected  regions, and then either  collect information from all medical practitioners from the chosen  hospitals or a random sample from within each of the chosen  hospitals. Even more complex multistage sampling is possible. 

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DETERMINING SAMPLE SIZE  EFFICIENT SAMPLE SIZES CAN BE  DRAWN FROM EITHER LARGE (INFINITE) POPULATIONS OR  SMALL (FINITE) POPULATIONS.  

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Researchers often need to estimate  characteristics of large populations. To achieve this in an efficient manner,  it is necessary to determine  the appropriate sample size prior to data collection. 

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Determining sample size is  complex because of many factors that need  to be taken into account simultaneously.  Challenge is to obtain acceptable balance among several of these factors. These factors include: the variability of elements in the target population,  The type of sample required, Time availability, Budget Required estimation precision, And whether the findings are to be generalized and,  if so, with what degree of confidence. 

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Independent variable: A variable thought to be  the cause of some effect. Used in experimental research to denote  a variable that the experimenter manipulated. Dependent variable: A variable thought to be  affected by changes in the independent variable. This is the outcome variable. Predictor variable: A variable thought to predict  an outcome variable. Basically an independent variable. Outcome variable: A variable thought to change  as a function of change in a predictor variable. 

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IDENTIFY INDEPENDENT & DEPENDENT VARIABLES 

The purpose of this experiment was  to determine the effects of exercise on body fat. The purpose of this experiment was  to determine the effects of power training on vertical jump. The purpose of this experiment was  to determine the relationship between diet and  body composition.  

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From your initial observation you generate  explanations, or theories, of those observations,  from which you can make predictions (hypotheses). Scientific statements are ones that can be verified  (tested) using empirical evidence. 

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Categorical Binary variable: Only 2 categories (male, female) Nominal variable: More than two categories:  young, middle age, elderly. Ordinal variable: The variable is  ordered by some attribute, such as pain. (each interval does not represent and EQUAL distance). Ex: RPE & Pain ratings Continuous  Interval variable: Equal intervals on the variable represent  an equal difference. Temperature is an interval variable. Ratio variable: Equal intervals represent  equal difference. Zero is an absence of the variable,  ratio values are meaningful. 

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Measurement error is the discrepancy between a variables actual value and  its measured value. Some variables more prone to errors than others:  attitude, pain, volume of gas expired , blood pressure, height and weight. Factors that can influence measurement error: Accuracy of instruments Random variation in the variable Adherence to sound measurement principles 

Quantitative Techniques in SPSS

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