analysis of data research ppt

• Lecture 1: Course Overview
• Lecture 2: Intro to R
• Lecture 3: Data & Data Handling
• Lecture 4: Data Wrangling
• Lecture 5: Summary Statistics
• Lecture 6: Plotting 1
• Lecture 7: Plotting 2
• Lecture 8: Probability Basics
• Lecture 9: Models
• Lecture 10: Parameter Estimation 1
• Lecture 11: Classical Testing 1
• Lecture 12: Classical Testing 2
• Lecture 13: Classical Testing 3
• Lecture 14: Impact of Statistical Practices (T. Roettger)
• Lecture 15: Model Comparison
• Lecture 16: Simple Regression
• Tutorial 1: Using R, Data Handling / Wrangling
• Tutorial 2: More R, Wrangling, Summary Stats
• Tutorial 3: Summary statistics
• Tutorial 4: Plotting
• Tutorial 5: Probability calculus
• Tutorial 6: Probability calculus, models
• Tutorial 7: Parameter Estimation
• Tutorial 8: Classical Testing
• Tutorial 9: Hypothesis Testing
• Tutorial 10: Bayesian Hypothesis Testing

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Data Analysis PowerPoint Templates & Presentation Slides

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Data Analysis PowerPoint presentation templates are pre-designed slides that can be used for presenting results, insights, and conclusions derived from the analysis of various kinds of data. They often contain a variety of slide layouts, diagrams, charts, and other graphic elements that can effectively communicate complex data in a visually engaging and digestible manner.

Our editable data analysis presentation slides can help to prepare impeccable business reports and data analysis presentations with the help of editable & high-quality data analysis slide templates compatible with PowerPoint & Google Slides presentations.

Possible use cases, applications and presentation ideas for data analysis slide templates:

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• Academic Research: Researchers can use data analysis presentation templates to present their research findings in conferences or seminars. They can showcase data about a variety of subjects, from social sciences to natural sciences.
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These data analysis infographics and charts can help to prepare compelling data analysis presentation designs with charts and visually appealing graphics.

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Data Analysis and Interpretation

This is a slide presentation by Martin Otundo Richard to help learners, researchers and other scholars to understand how data is analyzed and presented.

Related Papers

Shanel Bautista

Fernando Macolor Cruz

Janine Javier

"Data analysis is the process of bringing order, structure and meaning to the mass of collected data. It is a messy, ambiguous, time consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Data analysis is a search for answers about relationships among categories of data."-Marshall and Rossman, 1990:111 Hitchcock and Hughes take this one step further: "…the ways in which the researcher moves from a description of what is the case to an explanation of why what is the case is the case."-Hitchcock and Hughes 1995:295 IV.1 INTRODUCTION In Chapter three, researcher had discussed the research design and methodology, origin of the research, design of the research, variable of the research, population and sample of the research, tools for data collection, development stage of the CAI package, procedure for data collection, statistical analysis done in research work. Data analysis is considered to be important step and heart of the research in research work. In the beginning the data is raw in nature but after it is arranged in a certain format or a meaningful order this raw data takes the form of the information. The most critical and essential supporting pillars of the research are the analysis and the interpretation of the data. With the help of the interpretation step one is able to achieve a conclusion from the set of the gathered data. Interpretation has two major aspects namely establishing continuity in the research through linking the results of a given study with those of another and the establishment of some relationship with the collected data. Interpretation can be defined as the device through which the factors, which seem to explain what has been observed by the researcher in the course of the

Dawit Dibekulu

Research is a scientific field which helps to generate new knowledge and solve the existing problem. So, data analysis is the crucial part of research which makes the result of the study more effective. It is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. In a research it supports the researcher to reach to a conclusion. Therefore, simply stating that data analysis is important for a research will be an understatement rather no research can survive without data analysis. It can be applied in two ways which is qualitatively and quantitative. Both are beneficial because it helps in structuring the findings from different sources of data collection like survey research, again very helpful in breaking a macro problem into micro parts, and acts like a filter when it comes to acquiring meaningful insights out of huge data-set. Furthermore, every researcher has sort out huge pile of data that he/she has collected, before reaching to a conclusion of the research question. Mere data collection is of no use to the researcher. Data analysis proves to be crucial in this process, provides a meaningful base to critical decisions, and helps to create a complete dissertation proposal. So, after analyzing the data the result will provide by qualitative and quantitative method of data results. Quantitative data analysis is mainly use numbers, graphs, charts, equations, statistics (inferential and descriptive). Data that is represented either in a verbal or narrative format is qualitative data which is collected through focus groups, interviews, opened ended questionnaire items, and other less structured situations.

Abubakar Abdulkareem

The aim of this article is to provide and share several tools for analyzing researchable data collected from research participants or research respondents or even secondary data, be it quantitative research methodology or qualitative research methodology. In order to achieve the aim of this article, the researchers carefully itemized and discuss how tools for analyzing quantitative data would be used such tools are:

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hany Elbardan

The analysis and interpretation of data about wearing high heels for female students of Ligao community college.

James Joel Delos Reyes

The analysis and interpretation of data about wearing high heels for female students of Ligao community college. To complete this study properly, it is necessary to analyze the data collected in order to answer the research questions. Data is interpreted in a descriptive form. This chapter comprises the analysis, presentation and interpretation of the findings resulting from this study. The analysis and interpretation of data is carried out in two phases. The first part, which is based on the results of the questionnaire, deals with a qualitative analysis of data. The second, which is based on quantitative analysis. The unit of analysis is the major entity that the researcher going to analyze in the study. It is not 'what' or 'who' that is being studied. Researchers were collecting the data or information from the following female student for the completion of the study 100 questionnaires were distributed, only 80 was retrieve, some students did not completely answer the given data, few of them with a lot of missing data, while the remaining students answer well on the given questionnaire. The researchers use table in order to easily identify the data and interpret it according to the response of the following female students. In order to get the percentage of the following data we use the formula P=the value or the frequency/ by total respondent, (100) multiply (100). Table 1.

This chapter aims to give you access to some of the routines, procedures, phases and tactics that are common in qualitative analytic reasoning and practice. Initially I focus on four routinely cited approaches to qualitative data analysis in order to explore their similarities and differences. I then describe some of the pragmatic issues you might need to consider alongside some of qualities or states of mind you might seek to cultivate.

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection  methods, and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Monograph Matters

Qualitative analysis: process and examples | powerpoint – 85.2.

Authors Laura Wray-Lake and Laura Abrams describe qualitative data analysis, with illustrative examples from their SRCD monograph,  Pathways to Civic Engagement Among Urban Youth of Color . This PowerPoint document includes presenter notes, making it an ideal resource for researchers learning about qualitative analysis and for instructors teaching about it in upper-level undergraduate or graduate courses.

Created by Laura Wray-Lake and Laura S. Abrams. All rights reserved.

Citation: Wray-Lake, L. & Abrams, L. S. (2020) Qualitative Analysis: Process and Examples [PowerPoint]. Retrieved from https://monographmatters.srcd.org/2020/05/12/teachingresources-qualitativeanalysis-powerpoint-85-2

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DATA TYPES AND QUANTITATIVE DATA ANALYSIS

Mar 21, 2019

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DATA TYPES AND QUANTITATIVE DATA ANALYSIS. PRESENTED TO THIRD-TRIMESTER YEAR 1. DATA. Information expressed qualitatively or quantitatively Data are measurements of characteristics Measurements are functions that assign values in quantitative or quantitative form

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• data collection
• sampling procedure
• random sampling procedure
• simple unrestricted random sampling

Presentation Transcript

DATA TYPES AND QUANTITATIVE DATA ANALYSIS PRESENTED TO THIRD-TRIMESTER YEAR 1

DATA • Information expressed qualitatively or quantitatively • Data are measurements of characteristics • Measurements are functions that assign values in quantitative or quantitative form • Characteristics are referred to as variables Eg. Height, weight, sex, tribe, etc

VARIABLES AND DATA TYPES • Variable as characterization of event • Classification of Variables • Qualitative: usually categorical; values/members fall into one of a set of mutually exclusive & collectively exhaustive classes. eg. Sex, crop variety, animal breed, source of water, type of house • Quantitative: numeric values possessing an inherent order. • Discrete: eg. # of children/farmers/animals, etc • Continuous: height, weight, distance, etc • Random and Fixed

Data Types • Scales of measurements • Nominal • Ordinal • Interval • Ratio Levels of measurement distinguished on the basis of the following criteria: • Magnitude or size; Direction • Distance or interval; Origin • Equality of points; Ratios of intervals; Ratio of points

NOMINAL DATA • Example: Sex (Gender) coded M,F or 0,1 • ‘Numbers’ simply identify, classify, categorize or distinguish. • The score has nosize or magnitude • Score has equality because two subjects are similar (equal) if they have same number • Weakest level of measurement; poor • ArithmeticoperationsCANNOT be performed on nominal data types

ORDINAL DATA • Associated with qualitative random variables • Generated from ranked responses (or from a counting process). • Have properties of nominal-data, in addition to DIRECTION • Numeric or non-numeric • Next to nominal in terms of weakness • Arithmeticoperations must be avoided • Egs: knowledge (low, average, high), socio-economic status, attitude, opinion (like, dislike, strongly dislike), etc.

INTERVAL and RATIO INTERVAL • Numeric, have magnitude or size, direction, distance or interval, and origin • Interval scale has no absolute 0 that is NOT independent of system of measurement [0oC not same temperature as 0oF] • Eg. Temperature in degrees Fahrenheit or Celsius RATIO • Weight of cassava in kilogram or pounds weight • Numeric, have magnitude or size, direction, distance or interval, and origin • Absolute origin exists and not system dependent All arithmetic operations can be performed on such data types

DATA COLLECTION PROCESSES • Processes include (not mutually exclusive) • Routine Records; • Survey Data; • Experimental data;

ROUTINE (MONITORING) DATA • Data periodically recorded essentially for administrative use of the establishment and for studying trends or patterns. • Examples – medical records, meteorological data • Some statistical analysis of data possible on description and prescription • Cheap data, and planning could be haphazard

EXPERIMENTAL DATA • Treatments are the investigated factors of variation • Treatments are controlled by the designer • Treatment levels may be fixed, random, qualitative, quantitative • Comparative experimental data require inductive analysis • Emphasis on inference including estimation of effects and test of hypotheses.

SURVEY DATA COLLECTION • Information on characteristics, opinions, attitudes, tendencies, activities or operations of the individual units of the population • Based on a small set of the population • Can be planned; preference for random surveys • Researcher or investigator has no (or must not exercise) control over the respondent or data

Which procedure to use? • Depends on study objectives • All 3 procedures are possible while in the community • Monitoring and Survey procedures will be most used during the first year. • We discuss SURVEY further

SAMPLING (SURVEY) METHODS • Ensure units of population have same chance of being in the sample. Sampling Types • Probability sampling - the selection of sampling units is according to a probability (random & non-random) scheme. • Non-probability sampling - selection of samples not objectively made, but influenced a great deal by the sampler. Example – haphazard and use of volunteers • Preference is for probability sampling, but situation may determine otherwise

SYSTEMATIC SAMPLING Procedure • Sampling units are selected according to a pre-determined pattern. • For instance, given a sampling intensity of 10% from a population of 100 numbered trees or units (strips etc) might require your observing every 1 out of 10 trees (units, strips) in an ordered manner or sequence

Selection in Systematic Procedure • E.g. if by some process, random or non-random, the 3rd tree (unit or strip) is selected first, then the 13th, 23rd, 33rd, 43rd,..., 93rd trees (unit, strips) will accordingly be selected. Strictly, this type of selection as illustrated with the population of 100 trees (units) involves only one sample. • Improve by selecting 1st unit randomly from 1 to 10, or 1 to 100, and by MULTIPLE random starts

Applications of Systematic Sampling _ Population is unknown _ Baseline studies on spatial distribution patterns of population _ Baseline studies on extent/distribution of pests, pathogens, etc. _ Mapping purposes _ Regeneration studies

Advantages of Systematic Sampling _ Easy to set-up _ Relative speed in data collection _ Total coverage of population assured _ Good base for future designs, as position of characters can easily be mapped (with known coordinates) _ Demarcation of units not necessary, as sampling units are defined by first unit.

Disadvantages of Systematic Sampling • With only one random observation, sampling error not valid • Unknown trend(s) in population can influence results adversely [Examples: topography, season of sampling interval]

Avoiding the disadvantages • The first major disadvantage on sampling error can be rectified by introducing several multiple random starts through stratification of the population • The second problem of trend is more difficult but simply relates to the choice of the sampling interval.

Simple/Unrestricted Random Sampling • Unlike the systematic sampling, sampling units need not be equally spaced. • We shall define this as that sampling procedure which ensures equal probability for all samples of the same size (without any restriction imposed on the selection process).

Illustration of SRS • Given a pop. Size of N from which a sample of size n will be drawn, the number of possible ways of obtaining the sample is • Supposing a population is known to have 5 units, and a sample size of 3 is required. • From this population of 5 units, there are 10 possible ways of obtaining a sample of size 3. [The formula is 5C3= 5!/{(5-3)! 3!} = 10]. • Each of these combinations is unique and has the same chance (1/10) of being selected. • Thus SRS is a random sampling procedure where each sample of size n has the same probability of selection.

SRS selection process • (i) Select randomly one 'sample combination' from the number 1 to 10 (as there are 10 possible combinations). • (ii) Use the table of random numbers to select 3 numbers from 1 to 5 or select three numbers from a 'hat' containing all the five numbers. This option seems easier and more practicable than (i).

Summary - SRS • Application: Applied when the population is known to be homogeneous. Procedure is suitable for units defined by plot sizes. • Advantage: Easy to apply, though not as easy as the systematic procedure. • Disadvantage: Requires knowledge of all the units in the population (construction of the frame is necessary)

STRATIFIED RANDOM SAMPLING • Requires dividing the population into non-overlapping homogeneous units, which we are called STRATA. • SRS is then applied to each stratum, hence stratified random sampling (STRS). • Examples of strata types or criteria are ages of plantation, species types, aspect, topography/ altitude, farm types, habitat • Dividing the population into such homogeneous units usually leads to better estimates of the desired population parameters.

Where/when to apply Stratified RS • Very suitable for heterogeneous areas (or units) that can be identified and classified into homogeneous entities. • Supplementary information, e.g. rem sensing aerial photographs, useful for stratification. • Choice of strata should ensure variation between units within strata is less than the variation between strata.

Advantages/Disadvantages of STRS Advantages • Estimates are more precise • Separate estimates and inferences for strata are possible Disadvantages • Sample size depends on type of allocation to be used • Sampling likely to be efficient in some strata than others • Errors in strata classification affect overall estimate • Frame construction for each stratum is required.

Allocation of units (n) to strata • Equal allocation - Equal (same) number of units are collected from each stratum. • Proportional allocation - The number of units per strata is proportional to the size of the strata.

ANALYSING QUALITATIVE DATA • Qualitative data are essentially labels of a categorical variable • Statistical Analyses involve totals, percentages and conversion to pie-charts and bar charts (bar-graphs). • Sophisticated analyses include categorical modelling

You can have multiple bar graphs (i.e, can have more than one variable illustrated on a bar chart. Example is given below:

Contingency Table This involves count summaries for 2 or more categories placed in row-column format: Example of a 2 by 3 contingency table: Assess association between Gender & Group

ANALYSING QUANTITATIVE DATA • Basic analyses involve determining the CENTRE and SPREAD of data. • Inferential, probability and non-probability based

Measuring Centre Statistics include • MODE (most frequently occurring observation) • MEDIAN (observation lying at the centre of an ordered data) – best for INCOME data • MEAN (a sufficient, consistent, unbiased statistic, utilising ALL observations)

EXAMPLE • Consider that we selected RANDOMLY 10 houses out of 50, and observed the number of school-aged children who do not go to school as follows: 1 2 4 4 1 1 6 0 5 2 Find MEDIAN, MODE, MEAN

MODE: 1 as it appeared most often (most households have at least 1 child of school-going age not in school) • MEDIAN: Centremost observation after ordering data lies between the 4th and 5th data, i.e., between 2 and 2 (= 352) 0 1 1 1 2 2 4 4 5 6 Interpretation: 50% of the sampled population have up to 2 children of school-going age not in school) • MEAN: We use the arithmetic mean = sum of data divided by no. of observations, = (0+1+1+1+ 2+2+4+4+5+6)/10=2.6

Measuring Spread Statistics include • MINIMUM, MAXIMUM (ie EXTREME data) • RANGE (a single statistic calculated as MAXIMUM minus MINIMUM value) • MEAN of the sum of the ABSOLUTE DEVIATION • STANDARD DEVIATION (SD, but use the divisor n-1, not n as in most calculators). • STANDARD ERROR

EXAMPLE • Consider that we selected RANDOMLY 10 houses out of 50, and observed the number of school-aged children who do not go to school as follows: 1 2 4 4 1 1 6 0 5 2 Find STANDARD DEVIATION, STANDARD ERROR and CONFIDENCE LIMITS

CALCULATING SPREAD: STANDARD DEVIATION Standard Deviation: = 2.01 Approximate SD = = (6-0)/4 = 1.5 (valid if sample is large and distribution is normal)

Sampling fraction (f) and Finite Population Correction Factor (fpc) • Sampling fraction= f = n/N = 10/50 = 0.20 (represents the proportion of the population that is sampled, i.e. observed) • If f < 0.05, fpc is ignored. In our case, f > 0.5 (indeed equals 0.20), fpc must be calculated and used for the sampling errorcomputation fpc = (N-n)/N = 1– n/N = 1- 0.20 = 0.80

CALCULATING SPREAD: STANDARD ERROR = 0.57

Confidence (Fiducial) Limits • Given a level of significance, 5%, can obtain a 95% confidence limit on the mean number of non-school going children by multiplying SE by 1.96, that is: P(2.6-1.96*0.57 < true number < 2.6+1.96*0.57) =1-0.05= 0.95 P(1.5 < true number per household < 3.7) = 0.95 • Interpretation: 95% certain that true number of children in community who are of school-age but at home is between 1.5 (1) and 3.7 (4). OR can conclude (after multiplying by the total 50 households • 75 to 185 school-aged children in the community are not in school

Combining Spread and Centre BOX PLOT HISTOGRAM

Further Analysis of Quantitative Data • Histograms give idea of the distribution of the data; very useful for quantitative data • An excellent alternative to histogram is the stem-leaf diagram. • Measures of association – correlation analysis, dependence (cause-effect) relations (regression procedures) – 2006/2007

DATA ANALYSIS IS ENDLESS!!! • ENJOY YOUR TIME DURING TTFPP • END • KS Nokoe, PT Birteeb, IK Addai, M Agbolosu, L Kyei,

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Neuroscience

Accessible computing platforms democratize neuroimaging data analysis

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Maternal and Infant Research Electronic Data Analysis (MIREDA): A protocol for creating a common data model for federated analysis of UK birth cohorts and the life course

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Introduction Birth cohorts are valuable resources for studying early life, the determinants of health, disease, and development. They are essential for studying life course. Electronic cohorts are live, dynamic longitudinal cohorts using anonymised, routinely collected data. There is no selection bias through direct recruitment, but they are limited to health and administrative system data and may lack contextual information.

The MIREDA (Maternal and Infant Research Electronic Data Analysis) partnership creates a UK-wide birth cohort by aligning existing electronic birth cohorts to have the same structure, content, and vocabularies, enabling UK-wide federated analyses.

Create a core dynamic, live UK-wide electronic birth cohort with approximately 100,000 new births per year using a common data model (CDM).

Provide data linkage and automation for long-term follow up of births from MuM-PreDiCT and the ‘Born in’ initiatives of Bradford, Wales, Scotland, and South London for comparable analyses.

Methods We will establish core data content and collate linkable data. Use a suite of extraction, transformation, and load (ETL) tools will be used to transform the data for each birth cohort into the CDM. Transformed datasets will remain within each cohort’s trusted research environment (TRE). Metadata will be uploaded for the public to the Health Data Research (HDRUK) Innovation Gateway . We will develop a single online data access request for researchers. A cohort profile will be developed for researchers to reference the resource.

Ethics Each cohort has approval from their TRE through compliance with their project application processes and information governance.

Dissemination We will engage with researchers in the field to promote our resource through partnership networking, publication, research collaborations, conferences, social media, and marketing communications strategies.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by an MRC Partnership Grant [MR/X02055X/1], MatCHNet pump-priming [U20005/302873] and an MRC Programme Grant [MR/X009742/1].

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Access to data is granted according to the information governance requirements of each TRE. The Data Protection Act 2018 is not applicable to anonymised data and the OMOP CDM will be anonymised and provide aggregated data and statistics only. Each TRE has ethical approval for its operation and use, thus no additional ethical approval was required beyond the standard project approval by official channels.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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There was an error in the order of authorship and a missing punctuation mark after the title abstract. Also needed to update the authors order in the file I uploaded as it did not match that of the paper.

Data availability statement

Data will be available upon reasonable request through the Health Data Research (HDRUK) Innovation Gateway .

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The dataset on digitally delivered services trade contains WTO estimates on services traded through computer networks, such as the Internet, apps, emails, voice and video calls, and digital intermediation platforms. The coverage of digitally delivered services reflects the definition of the  IMF-OECD-UNCTAD-WTO Handbook on Measuring Digital Trade .  It covers over 200 economies and regions and eight sub-sectors for the period 2005-23.

The Trade in Services by Mode of Supply (TISMOS) dataset, produced by the WTO, contains estimates of services trade through the four modes of supply identified in the WTO's General Agreement on Trade in Services (GATS), as of April 2024. It covers over 200 economies and regions for the period 2005-22 for 55 sectors. It provides valuable insights into how services trade has modified over the years, including the impact of digitalization and of the COVID-19 pandemic.

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2024 Identity Fraud Study: Resolving the Shattered Identity Crisis

• Date: April 10, 2024
• Suzanne Sando
• Report Details: 48 pages, 27 graphics
• Research Topic(s):
• Fraud & Security
• Fraud Management
• PAID CONTENT

The financial landscape experienced meaningful innovation and expansion over the past few decades. The most notable acceleration in advancement has happened in just the past few years. Digital banking, especially in the form of mobile banking apps, is a must for financial institutions to stay relevant with on-the-go consumers. Brick-and-mortar stores have moved into the e-commerce space (with many businesses moving away from physical stores to online-only offerings). Online loan origination is transforming the auto and mortgage industries. The list is endless. 

Whether it was the growing adoption of cryptocurrency and digital assets, real-time payments innovation, or frugal savers taking advantage of interest rate increases to employ strategic saving in 2023, consumers seemed to be taking advantage of forward economic momentum. 

And criminals surely took advantage of this forward momentum. Fraud-related resolution hours skyrocketed in 2023. The average amount of time consumers spent in 2022 resolving issues stemming from identity fraud clocked in at six hours, but in 2023, fraud resolution hours rose steeply, jumping to a nearly 10-hour average, a major disruption for consumers and financial institutions alike. 

Skyrocketing Resolution Hours Create Headaches for Consumers

Traditional identity fraud losses amounted to nearly $23 billion in 2023, resulting in a 13% increase in overall losses for U.S. adult victims of identity fraud. And since Javelin began tracking financial losses attributable to scams in 2021, there has been a steady yet nearly imperceptible drop in financial loss. Scams orchestrated by criminals resulted in just over $20 billion in fraud losses to victims. 

Javelin makes a distinction between traditional identity fraud and identity fraud scam losses to add perspective to the landscape of identity fraud and provide accurate historical information and relevant recommendations to financial institutions, fintechs, third-party fraud solutions providers, and even consumers. But it’s vital to remember that, to identity fraud victims, it doesn’t matter how the losses are analyzed and categorized. 

What matters is how their fraud and scam encounter is managed by the organizations they trust and with which they choose to do business, how they are treated throughout the resolution process, and how they feel after suffering a financial loss and a breach of trust. This must always be top of mind for organizations as they work to improve their efforts to detect and prevent further damage from identity fraud.

2024 Identity Fraud Study Sponsors

The Javelin Strategy & Research 2024 Identity Fraud Study provides a comprehensive analysis of fraud trends in the context of a changing technological and payments landscape. Its goal is to inform consumers, financial institutions, and businesses about the most effective means of controlling identity fraud. The study began in 2003 and serves as the nation’s longest-running analysis of identity fraud, with more than 105,000 consumers surveyed. This study is independently produced by Javelin and made possible with support from the following sponsors:

Methodology

Survey data collection.

This ID fraud survey was conducted online among 5,000 U.S. adults over the age of 18; this sample is representative of the U.S. census demographics distribution. Data collection took place Oct. 23-Nov. 28, 2023. Data is weighted using 18-plus U.S. population benchmarks on age, gender, race/ethnicity, education, census region, and metropolitan status from the most current CPS targets. Due to rounding errors, the percentages on graphs may add up to 100% plus or minus 1%. To preserve the independence and objectivity of this annual report, the sponsors of this project were not involved in the tabulation, analysis, or reporting of final results.

Comparing Research Findings Across Organizations: Please Anticipate Natural Variances in Key Findings Javelin cautions readers to understand the context behind increases and decreases in key findings as they apply to the annual Identity Fraud Report, especially when comparing your own organization’s experiences or comparing research outcomes from other companies or agencies. It is impossible to compare identity fraud key findings across multiple sources and expect to see universal alignment. Key findings never line up across organizations due to how data is collected. Sample sizes (the number of consumers) also vary a great deal.

Learn More About This Report & Javelin

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Pig butchering is a devastating, multilayered, drawn-out investment scam that leaves victims with nothing. By implementing robust customer education, employing effective fraud dete...

Customer Contact Centers: Heroes in Cybercrime Remediation, Fraud Prevention

Criminals increasingly use cyberattacks and scams to target consumers, and FI call centers are often relied upon for victim assistance. The key will be FI customer-oriented contact...

2024 Authentication and Identity-Proofing Vendor Solutions Scorecard

Identity-proofing, a fundamental need of financial services companies, is a space in considerable flux. Fintech companies that cut their teeth as e-commerce payments platforms are ...

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