Measurement & Types of Data in QNR PDF

SLIDESMANIA.C MEASUREMENT & TYPES OF DATA IN QNR The Levels of Measurement SLIDESMANIA.C Levels of measurement scales of measurement , or rating scales - values that an indicator can take bu...

SLIDESMANIA.C MEASUREMENT & TYPES OF DATA IN QNR The Levels of Measurement SLIDESMANIA.C Levels of measurement scales of measurement , or rating scales - values that an indicator can take but they say nothing about the indicator itself help determine what statistical analysis to be conducted and therefore the type of conclusions that can be drawn from research SLIDESMANIA.C Levels of measurement 1.nominal 2.ordinal 3. interval 4. ratio SLIDESMANIA.C Nominal / Categorical Scale The lowest level of measurement. Data is categorized into distinct categories or labels. Categories have no inherent order or ranking. Examples include gender (male, female), colors (red, blue, green), and types of animals (dog, cat, bird). SLIDESMANIA.C Nominal / Categorical Scale When the measurement of the popularity of a school organization is measured on a nominal scale Which type of school organization are you a member of ? 1. Academic 2. Sports 3. Community service Labeling Academic as ‘1’; Sports as ‘2’ and Community Service as ‘3 ’does not in any way mean any of the attributes are better than the other. They SLIDESMANIA.C Nominal / Categorical Scale Frequency tables: Count the frequency of each category. Mode: Identify the most frequently occurring category. Chi-squared tests: Assess the association or independence between two nominal variables. Bar charts and pie charts: Visual representations of nominal data. SLIDESMANIA.C Ordinal Scale Involves data that can be ranked or ordered, but the intervals between categories are not uniform.  Relative ordering or ranking is meaningful, but the differences between categories are not.  Examples education level (e.g., high school, bachelor's degree, master's degree), survey ratings (e.g., strongly disagree, disagree, neutral, agree, strongly agree), and socioeconomic status (e.g., low-income, middle-income, high-income). SLIDESMANIA.C Ordinal Scale A software company may need to ask its users: How would you rate our app? Excellent Very Good Good Bad Poor The attributes in this example are listed in descending order. SLIDESMANIA.C Ordinal Scale Frequency tables: Count the frequency of each category and order them. Median: Calculate the middle value to describe central tendency. Mann-Whitney U test and Wilcoxon signed-rank test: Non- parametric tests for comparing two or more groups. Ordinal regression: Analyze relationships and trends within ordinal data. SLIDESMANIA.C Interval Scale a scale in which the levels are ordered and each numerically equal distances on the scale have equal interval difference With an interval scale, you not only know that a given attribute A is bigger than another attribute B, but also the extent at which A is larger than B. arithmetic operations can be performed on an interval scale. SLIDESMANIA.C Interval Scale Mean and standard deviation: Calculate measures of central tendency and variability. Histograms and line charts: Visualize the distribution of interval data. T-tests: Compare means between two groups. SLIDESMANIA.C Interval Scale  ANOVA (Analysis of Variance): Compare means among three or more groups.  Correlation coefficients (e.g., Pearson correlation): Examine relationships between two interval variables.  Regression analysis: Model relationships and make predictions using interval data. SLIDESMANIA.C Ratio Scale Has the four characteristics of measurement scale; identity, magnitude, equal interval, and the absolute zero property allows the researcher to compare both the differences and the relative magnitude of numbers examples include length, weight, time compatible with all statistical analysis methods SLIDESMANIA.C Ratio Scale A survey that collects the weights of the respondents. Which of the following category do you fall in? Weigh more than 100 kgs 81 - 100 kgs 61 - 80 kgs 40 - 60 kgs Less than 40 kgs SLIDESMANIA.C SAMPLING & PROBABILITY SAMPLING SLIDESMANIA.C The first step leading to the data collection is to identify the people and places you plan to study. This involves determining which group of people will you study, who, specifically, these people are, and how many of them you will need to involve. SLIDESMANIA.C SAMPLING PROCEDURES & SAMPLE After the research questions and hypotheses have been set and the research design has been identified, then the researcher needs to consider two further aspects of the research process: Sampling: Who will participate in the study? Measurement: How will the key variables be measured? SLIDESMANIA.C SAMPLING PROCEDURES & SAMPLE A population is a group of individuals that have the same characteristic(s), includes all people or items with the characteristic one wishes to understand. Target Population - A group of individuals with some common defining characteristic that the researcher can identify and study SLIDESMANIA.C SAMPLING PROCEDURES & SAMPLE Sampling involves selecting individual units to measure from a larger population. It allows researchers to gather information from a smaller, more manageable subset of the population. That information can be used to represent the greater population. SLIDESMANIA.C SAMPLING PROCEDURES & SAMPLE A sample is a subgroup of the target population that the researcher plans to study for the purpose of making generalizations about the target population. ▫Samples are only estimates. SLIDESMANIA.C DETERMINING THE SAMPLE SIZE For descriptive studies, we think a sample with a minimum number of 100 is essential. For correlational studies, a sample of at least 50 is deemed necessary to establish the existence of a relationship. For experimental studies, we recommend a minimum of 30 individuals per group(Fraenkel & Wallen ,2012) SLIDESMANIA.C Strategies for Determining Sample Size 1. Using a Census for Small Populations all the individuals in the population. 2. Using a Sample Size of a Similar Study 3. Using Published Tables 4. Using Formulas to Calculate a Sample Size SLIDESMANIA.C Strategies for Determining Sample Size 4. Using Formulas to Calculate a Sample Size Taro Yamane's formula: n = N /[1+N(e)^2] where: n = sample size N = population size (the universe) e = sampling error (usually.10,.05 and.01 acceptable error) ^ = raised to the power of SLIDESMANIA.C SAMPLING TECHNIQUES Two main types of sampling: probability and non-probability sampling. The difference between the two is whether or not the sampling selection involves randomization. Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study SLIDESMANIA.C SAMPLING TECHNIQUES Probability sampling is the selection of individuals from the population so that they are representative of the population. It uses randomization and takes steps to ensure all members of a population have a chance of being selected. Simple random sampling Stratified random sampling Systematic sampling Cluster (area) sampling Multistage sampling SLIDESMANIA.C SAMPLING TECHNIQUES 1.Simple Random sampling – every member has an equal chance, Drawing randomly from a list of the population (e.g.: names from a hat, using a matrix of random numbers). 2.Stratified sampling – a process in which certain subgroups, or strata, are selected for the sample in the same proportion as they exist in the population ; population divided into subgroups (strata) and members are randomly selected from each group; random sample is taken from an identifiable strata; (proportional or quota sampling) SLIDESMANIA.C SAMPLING TECHNIQUES 3.Systematic sampling – uses a specific system to select members such as every 10th person on an alphabetized list 4.Cluster sampling – divides the population into clusters, clusters are randomly selected and all members of the cluster selected are sampled; The selection of groups, or clusters, of subjects rather than individuals 5.Multi-stage sampling – a combination of one or more of the above methods SLIDESMANIA.C Nonprobability sampling is the selection of participants because they are available, convenient, or represent some characteristic the investigator wants to study. It does not rely on the use of randomization techniques to select members. SLIDESMANIA.C Nonprobability sampling 1. Purposive sampling – members of a particular group are purposefully sought after 2. Snowball sampling – members are sampled and then asked to help identify other members to sample and this process continues until enough samples are collected 3. Convenience sampling -respondents are selected because they are accessible  Thank you! SLIDESMANIA.C